Table of contents
  1. Use Cases from NBD(NIST Big Data) Requirements WG V1.0
  2. Blank Template
  3. Government Operation
    1. Big Data Archival: Census 2010 and 2000 – Title 13 Big Data
    2. National Archives and Records Administration Accession NARA Accession, Search, Retrieve, Preservation
    3. Statistical Survey Response Improvement (Adaptive Design)
    4. Non Traditional Data in Statistical Survey Response Improvement (Adaptive Design)
  4. Commercial
    1. Cloud Eco-System, for Financial Industries (Banking, Securities & Investments, Insurance) transacting business within the United States
    2. Mendeley – An International Network of Research
    3. Netflix Movie Service
    4. Web Search
    5. IaaS (Infrastructure as a Service) Big Data Business Continuity & Disaster Recovery (BC/DR) Within A Cloud Eco-System
    6. Cargo Shipping
      1. Figure 0 Cargo Shipping
    7. Materials Data for Manufacturing
    8. Simulation driven Materials Genomic
  5. Defense
    1. Large Scale Geospatial Analysis and Visualization; David Boyd, Data Tactics
    2. Object identification and tracking from Wide Area Large Format Imagery (WALF) Imagery or Full Motion Video (FMV) – Persistent Surveillance
    3. Intelligence Data Processing and Analysis
  6. Healthcare and Life Sciences
    1. Electronic Medical Record (EMR) Data
    2. Pathology Imaging/digital pathology
      1. Figure 1: Examples of 2-D and 3-D pathology images
      2. Figure 2: Architecture of Hadoop-GIS, a spatial data warehousing system over MapReduce to support spatial analytics for analytical pathology imaging
    3. Computational Bioimaging
    4. Genomic Measurements
    5. Comparative analysis for metagenomes and genomes
    6. Individualized Diabetes Management
    7. Statistical Relational Artificial Intelligence for Health Care
    8. World Population Scale Epidemiological Study
    9. Social Contagion Modeling  for Planning, Public Health and Disaster Management
    10. Biodiversity and LifeWatch
  7. Deep Learning and Social Media
    1. Large-scale Deep Learning
    2. Organizing large-scale, unstructured collections of consumer photos
    3. Truthy: Information diffusion research from Twitter Data
    4. Crowd Sourcing in the Humanities as Source for Big and Dynamic Data
    5. CINET: Cyberinfrastructure for Network (Graph) Science and Analytics
    6. NIST Information Access Division analytic technology performance measurement, evaluations, and standards
  8. The Ecosystem for Research
    1. DataNet Federation Consortium DFC
      1. Figure Policy-based Data Management Concept Graph (iRODS)
    2. The ‘Discinnet process’, metadata <-> big data global experiment
    3. Enabling Face-Book like Semantic Graph-search on Scientific Chemical and Text-based Data
    4. Light source beamlines
  9. Astronomy and Physics
    1. Catalina Real-Time Transient Survey (CRTS): a digital, panoramic, synoptic sky survey
      1. Figure Catalina Real-Time Transient Survey (CRTS): a digital, panoramic, synoptic sky survey
    2. DOE Extreme Data from Cosmological Sky Survey and Simulations
    3. Large Survey Data for Cosmology
    4. Particle Physics: Analysis of LHC Large Hadron Collider Data: Discovery of Higgs particle 
      1. Figure 1 The LHC Collider location at CERN
      2. Figure 2 The Multi-tier LHC computing infrastructure
    5. Belle II High Energy Physics Experiment
  10. Earth, Environmental and Polar Science
    1. EISCAT 3D incoherent scatter radar system
      1. Figure EISCAT 3D incoherent scatter radar system
    2. ENVRI, Common Operations of Environmental Research Infrastructure
      1. Figure 1: ENVRI Common Subsystems
      2. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (A) ICOS Architecture
      3. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (B) LifeWatch Architecture
      4. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (C) EMSO Architecture
      5. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (D) Eura-Argo Architecture
      6. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (E) EISCAT 3D Architecture
    3. Radar Data Analysis for CReSIS
      1. Figure 1: Typical Radar Data after analysis
      2. Figure 2: Typical flight paths of data gathering in survey region
      3. Figure 3. Typical echogram with Detected Boundaries.  The upper (green) boundary is between air and ice layer while the lower (red) boundary is between ice and terrain
    4. UAVSAR Data Processing, Data Product Delivery, and Data Services
    5. NASA LARC/GSFC iRODS Federation Testbed
    6. MERRA Analytic Services MERRA/AS
      1. Figure Typical MERRA/AS Output
    7. Atmospheric Turbulence - Event Discovery and Predictive Analytic
      1. Figure Typical NASA image of turbulent waves
    8. Climate Studies Using the Community Earth System Model at DOE’s NERSC Center
    9. DOE-BER Subsurface Biogeochemistry Scientific Focus Are
    10. DOE-BER AmeriFlux and FLUXNET Networks
  11. Energy
    1. Consumption forecasting in Smart Grids

Full Uses Cases

Last modified
Table of contents
  1. Use Cases from NBD(NIST Big Data) Requirements WG V1.0
  2. Blank Template
  3. Government Operation
    1. Big Data Archival: Census 2010 and 2000 – Title 13 Big Data
    2. National Archives and Records Administration Accession NARA Accession, Search, Retrieve, Preservation
    3. Statistical Survey Response Improvement (Adaptive Design)
    4. Non Traditional Data in Statistical Survey Response Improvement (Adaptive Design)
  4. Commercial
    1. Cloud Eco-System, for Financial Industries (Banking, Securities & Investments, Insurance) transacting business within the United States
    2. Mendeley – An International Network of Research
    3. Netflix Movie Service
    4. Web Search
    5. IaaS (Infrastructure as a Service) Big Data Business Continuity & Disaster Recovery (BC/DR) Within A Cloud Eco-System
    6. Cargo Shipping
      1. Figure 0 Cargo Shipping
    7. Materials Data for Manufacturing
    8. Simulation driven Materials Genomic
  5. Defense
    1. Large Scale Geospatial Analysis and Visualization; David Boyd, Data Tactics
    2. Object identification and tracking from Wide Area Large Format Imagery (WALF) Imagery or Full Motion Video (FMV) – Persistent Surveillance
    3. Intelligence Data Processing and Analysis
  6. Healthcare and Life Sciences
    1. Electronic Medical Record (EMR) Data
    2. Pathology Imaging/digital pathology
      1. Figure 1: Examples of 2-D and 3-D pathology images
      2. Figure 2: Architecture of Hadoop-GIS, a spatial data warehousing system over MapReduce to support spatial analytics for analytical pathology imaging
    3. Computational Bioimaging
    4. Genomic Measurements
    5. Comparative analysis for metagenomes and genomes
    6. Individualized Diabetes Management
    7. Statistical Relational Artificial Intelligence for Health Care
    8. World Population Scale Epidemiological Study
    9. Social Contagion Modeling  for Planning, Public Health and Disaster Management
    10. Biodiversity and LifeWatch
  7. Deep Learning and Social Media
    1. Large-scale Deep Learning
    2. Organizing large-scale, unstructured collections of consumer photos
    3. Truthy: Information diffusion research from Twitter Data
    4. Crowd Sourcing in the Humanities as Source for Big and Dynamic Data
    5. CINET: Cyberinfrastructure for Network (Graph) Science and Analytics
    6. NIST Information Access Division analytic technology performance measurement, evaluations, and standards
  8. The Ecosystem for Research
    1. DataNet Federation Consortium DFC
      1. Figure Policy-based Data Management Concept Graph (iRODS)
    2. The ‘Discinnet process’, metadata <-> big data global experiment
    3. Enabling Face-Book like Semantic Graph-search on Scientific Chemical and Text-based Data
    4. Light source beamlines
  9. Astronomy and Physics
    1. Catalina Real-Time Transient Survey (CRTS): a digital, panoramic, synoptic sky survey
      1. Figure Catalina Real-Time Transient Survey (CRTS): a digital, panoramic, synoptic sky survey
    2. DOE Extreme Data from Cosmological Sky Survey and Simulations
    3. Large Survey Data for Cosmology
    4. Particle Physics: Analysis of LHC Large Hadron Collider Data: Discovery of Higgs particle 
      1. Figure 1 The LHC Collider location at CERN
      2. Figure 2 The Multi-tier LHC computing infrastructure
    5. Belle II High Energy Physics Experiment
  10. Earth, Environmental and Polar Science
    1. EISCAT 3D incoherent scatter radar system
      1. Figure EISCAT 3D incoherent scatter radar system
    2. ENVRI, Common Operations of Environmental Research Infrastructure
      1. Figure 1: ENVRI Common Subsystems
      2. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (A) ICOS Architecture
      3. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (B) LifeWatch Architecture
      4. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (C) EMSO Architecture
      5. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (D) Eura-Argo Architecture
      6. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (E) EISCAT 3D Architecture
    3. Radar Data Analysis for CReSIS
      1. Figure 1: Typical Radar Data after analysis
      2. Figure 2: Typical flight paths of data gathering in survey region
      3. Figure 3. Typical echogram with Detected Boundaries.  The upper (green) boundary is between air and ice layer while the lower (red) boundary is between ice and terrain
    4. UAVSAR Data Processing, Data Product Delivery, and Data Services
    5. NASA LARC/GSFC iRODS Federation Testbed
    6. MERRA Analytic Services MERRA/AS
      1. Figure Typical MERRA/AS Output
    7. Atmospheric Turbulence - Event Discovery and Predictive Analytic
      1. Figure Typical NASA image of turbulent waves
    8. Climate Studies Using the Community Earth System Model at DOE’s NERSC Center
    9. DOE-BER Subsurface Biogeochemistry Scientific Focus Are
    10. DOE-BER AmeriFlux and FLUXNET Networks
  11. Energy
    1. Consumption forecasting in Smart Grids

  1. Use Cases from NBD(NIST Big Data) Requirements WG V1.0
  2. Blank Template
  3. Government Operation
    1. Big Data Archival: Census 2010 and 2000 – Title 13 Big Data
    2. National Archives and Records Administration Accession NARA Accession, Search, Retrieve, Preservation
    3. Statistical Survey Response Improvement (Adaptive Design)
    4. Non Traditional Data in Statistical Survey Response Improvement (Adaptive Design)
  4. Commercial
    1. Cloud Eco-System, for Financial Industries (Banking, Securities & Investments, Insurance) transacting business within the United States
    2. Mendeley – An International Network of Research
    3. Netflix Movie Service
    4. Web Search
    5. IaaS (Infrastructure as a Service) Big Data Business Continuity & Disaster Recovery (BC/DR) Within A Cloud Eco-System
    6. Cargo Shipping
      1. Figure 0 Cargo Shipping
    7. Materials Data for Manufacturing
    8. Simulation driven Materials Genomic
  5. Defense
    1. Large Scale Geospatial Analysis and Visualization; David Boyd, Data Tactics
    2. Object identification and tracking from Wide Area Large Format Imagery (WALF) Imagery or Full Motion Video (FMV) – Persistent Surveillance
    3. Intelligence Data Processing and Analysis
  6. Healthcare and Life Sciences
    1. Electronic Medical Record (EMR) Data
    2. Pathology Imaging/digital pathology
      1. Figure 1: Examples of 2-D and 3-D pathology images
      2. Figure 2: Architecture of Hadoop-GIS, a spatial data warehousing system over MapReduce to support spatial analytics for analytical pathology imaging
    3. Computational Bioimaging
    4. Genomic Measurements
    5. Comparative analysis for metagenomes and genomes
    6. Individualized Diabetes Management
    7. Statistical Relational Artificial Intelligence for Health Care
    8. World Population Scale Epidemiological Study
    9. Social Contagion Modeling  for Planning, Public Health and Disaster Management
    10. Biodiversity and LifeWatch
  7. Deep Learning and Social Media
    1. Large-scale Deep Learning
    2. Organizing large-scale, unstructured collections of consumer photos
    3. Truthy: Information diffusion research from Twitter Data
    4. Crowd Sourcing in the Humanities as Source for Big and Dynamic Data
    5. CINET: Cyberinfrastructure for Network (Graph) Science and Analytics
    6. NIST Information Access Division analytic technology performance measurement, evaluations, and standards
  8. The Ecosystem for Research
    1. DataNet Federation Consortium DFC
      1. Figure Policy-based Data Management Concept Graph (iRODS)
    2. The ‘Discinnet process’, metadata <-> big data global experiment
    3. Enabling Face-Book like Semantic Graph-search on Scientific Chemical and Text-based Data
    4. Light source beamlines
  9. Astronomy and Physics
    1. Catalina Real-Time Transient Survey (CRTS): a digital, panoramic, synoptic sky survey
      1. Figure Catalina Real-Time Transient Survey (CRTS): a digital, panoramic, synoptic sky survey
    2. DOE Extreme Data from Cosmological Sky Survey and Simulations
    3. Large Survey Data for Cosmology
    4. Particle Physics: Analysis of LHC Large Hadron Collider Data: Discovery of Higgs particle 
      1. Figure 1 The LHC Collider location at CERN
      2. Figure 2 The Multi-tier LHC computing infrastructure
    5. Belle II High Energy Physics Experiment
  10. Earth, Environmental and Polar Science
    1. EISCAT 3D incoherent scatter radar system
      1. Figure EISCAT 3D incoherent scatter radar system
    2. ENVRI, Common Operations of Environmental Research Infrastructure
      1. Figure 1: ENVRI Common Subsystems
      2. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (A) ICOS Architecture
      3. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (B) LifeWatch Architecture
      4. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (C) EMSO Architecture
      5. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (D) Eura-Argo Architecture
      6. Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (E) EISCAT 3D Architecture
    3. Radar Data Analysis for CReSIS
      1. Figure 1: Typical Radar Data after analysis
      2. Figure 2: Typical flight paths of data gathering in survey region
      3. Figure 3. Typical echogram with Detected Boundaries.  The upper (green) boundary is between air and ice layer while the lower (red) boundary is between ice and terrain
    4. UAVSAR Data Processing, Data Product Delivery, and Data Services
    5. NASA LARC/GSFC iRODS Federation Testbed
    6. MERRA Analytic Services MERRA/AS
      1. Figure Typical MERRA/AS Output
    7. Atmospheric Turbulence - Event Discovery and Predictive Analytic
      1. Figure Typical NASA image of turbulent waves
    8. Climate Studies Using the Community Earth System Model at DOE’s NERSC Center
    9. DOE-BER Subsurface Biogeochemistry Scientific Focus Are
    10. DOE-BER AmeriFlux and FLUXNET Networks
  11. Energy
    1. Consumption forecasting in Smart Grids

Use Cases from NBD(NIST Big Data) Requirements WG V1.0

Source: http://bigdatawg.nist.gov/_uploadfil...305888391.docx (Word)

http://bigdatawg.nist.gov/home.php

Blank Template

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

 

Vertical (area)

 

Author/Company/Email

 

Actors/Stakeholders and their roles and responsibilities

 

Goals

 

 

Use Case Description

 

 

 

Current

Solutions

Compute(System)

 

Storage

 

Networking

 

Software

 

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

 

Volume (size)

 

Velocity

(e.g. real time)

 

Variety

(multiple datasets, mashup)

 

Variability (rate of change)

 

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

 

Visualization

 

Data Quality (syntax)

 

Data Types

 

Data Analytics

 

Big Data Specific Challenges (Gaps)

 

Big Data Specific Challenges in Mobility

 

 

Security & Privacy

Requirements

 

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

 

 

 

More Information (URLs)

 

 

 

Note: <additional comments>

       

Note: No proprietary or confidential information should be included

ADD picture of operation or data architecture of application below table

Government Operation

Big Data Archival: Census 2010 and 2000 – Title 13 Big Data

NBD(NIST Big Data) Requirements WG Use Case Template

Use Case Title

Big Data Archival: Census 2010 and 2000 – Title 13 Big Data

Vertical (area)

Digital Archives

Author/Company/Email

Vivek Navale & Quyen Nguyen (NARA)

Actors/Stakeholders and their roles and responsibilities

NARA’s Archivists

Public users (after 75 years)

Goals

Preserve data for a long term in order to provide access and perform analytics after 75 years. Title 13 of U.S. code authorizes the Census Bureau and guarantees that individual and industry specific data is protected.

Use Case Description

1)      Maintain data “as-is”. No  access and no data analytics for 75 years.

2)      Preserve the data at the bit-level.

3)      Perform curation, which includes format transformation if necessary.

4)      Provide access and analytics after nearly 75 years.

Current

Solutions

Compute(System)

Linux servers

Storage

NetApps, Magnetic tapes.

Networking

 

Software

 

 

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Centralized storage.

 

Volume (size)

380 Terabytes.

 

Velocity

(e.g. real time)

Static.

 

Variety

(multiple datasets, mashup)

Scanned documents

 

Variability (rate of change)

None

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Cannot tolerate data loss.

 

Visualization

TBD

Data Quality

Unknown.

 

Data Types

Scanned documents

 

Data Analytics

Only after 75 years.

 

Big Data Specific Challenges (Gaps)

Preserve data for a long time scale.

Big Data Specific Challenges in Mobility

TBD

 

Security & Privacy

Requirements

Title 13 data.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

.

 

 

More Information (URLs)

 

       

National Archives and Records Administration Accession NARA Accession, Search, Retrieve, Preservation

NBD(NIST Big Data) Requirements WG Use Case Template

Use Case Title

National Archives and Records Administration Accession NARA Accession, Search, Retrieve, Preservation

Vertical (area)

Digital Archives

Author/Company/Email

Quyen Nguyen & Vivek Navale (NARA)

Actors/Stakeholders and their roles and responsibilities

Agencies’ Records Managers

NARA’s Records Accessioners

NARA’s Archivists

Public users

Goals

 

Accession, Search, Retrieval, and Long term Preservation of Big Data.

 

Use Case Description

1)      Get physical and legal custody of the data. In the future, if data reside in the cloud, physical custody should avoid transferring big data from Cloud to Cloud or from Cloud to Data Center.

2)      Pre-process data for virus scan, identifying file format identification, removing empty files

3)      Index

4)      Categorize records (sensitive, unsensitive, privacy data, etc.)

5)      Transform old file formats to modern formats (e.g. WordPerfect to PDF)

6)      E-discovery

7)      Search and retrieve to respond to special request

8)      Search and retrieve of public records by public users

Current

Solutions

Compute(System)

Linux servers

Storage

NetApps, Hitachi, Magnetic tapes.

Networking

 

Software

Custom software, commercial search products, commercial databases.

 

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Distributed data sources from federal agencies.

Current solution requires transfer of those data to a centralized storage.

In the future, those data sources may reside in different Cloud environments.

 

Volume (size)

Hundred of Terabytes, and growing.

 

Velocity

(e.g. real time)

Input rate is relatively low compared to other use cases, but the trend is bursty. That is the data can arrive in batches of size ranging from GB to hundreds of TB.

 

Variety

(multiple datasets, mashup)

Variety data types, unstructured and structured data: textual documents, emails, photos, scanned documents, multimedia, social networks, web sites, databases, etc.

Variety of application domains, since records come from different agencies.

Data come from variety of repositories, some of which can be cloud-based in the future.

 

Variability (rate of change)

Rate can change especially if input sources are variable, some having audio, video more, some more text, and other images, etc.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Search results should have high relevancy and high recall.

Categorization of records should be highly accurate.

 

Visualization

TBD

Data Quality

Unknown.

 

Data Types

Variety data types: textual documents, emails, photos, scanned documents, multimedia, databases, etc.

 

Data Analytics

Crawl/index; search; ranking; predictive search.

Data categorization (sensitive, confidential, etc.)

Personally Identifiable Information (PII) data detection and flagging.

 

Big Data Specific Challenges (Gaps)

Perform pre-processing and manage for long-term of large and varied data.

Search huge amount of data.

Ensure high relevancy and recall.

Data sources may be distributed in different clouds in future.

 

Big Data Specific Challenges in Mobility

Mobile search must have similar interfaces/results

 

Security & Privacy

Requirements

Need to be sensitive to data access restrictions.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

.

 

 

More Information (URLs)

 

Note: <additional comments>

       

Statistical Survey Response Improvement (Adaptive Design)

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Statistical Survey Response Improvement (Adaptive Design)

Vertical (area)

Government Statistical Logistics

Author/Company/Email

Cavan Capps: U.S. Census Bureau cavan.paul.capps@census.gov

Actors/Stakeholders and their roles and responsibilities

U.S. statistical agencies are charged to be the leading authoritative sources about the nation’s people and economy, while honoring privacy and rigorously protecting confidentiality. This is done by working with states, local governments and other government agencies.

 

Goals

To use advanced methods, that are open and scientifically objective, the statistical agencies endeavor to improve the quality, the specificity and the timeliness of statistics provided while reducing operational costs and maintaining the confidentiality of those measured.

 

Use Case Description

Survey costs are increasing as survey response declines. The goal of this work is to use advanced “recommendation system techniques” using data mashed up from several sources and historical survey para-data to drive operational processes in an effort to increase quality and reduce the cost of field surveys.

 

Current

Solutions

Compute(System)

Linux systems

Storage

SAN and Direct Storage

Networking

Fiber, 10 gigabit Ethernet, Infiniband 40 gigabit.

Software

Hadoop, Spark, Hive, R, SAS, Mahout, Allegrograph, MySQL, Oracle, Storm, BigMemory, Cassandra, Pig

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Survey data, other government administrative data, geographical positioning data from various sources.

 

Volume (size)

For this particular class of operational problem approximately one petabyte.

Velocity

(e.g. real time)

Varies, paradata from field data streamed continuously, during the decennial census approximately 150 million records transmitted.

 

Variety

(multiple datasets, mashup)

Data is typically defined strings and numerical fields. Data can be from multiple datasets mashed together for analytical use.

 

Variability (rate of change)

Varies depending on surveys in the field at a given time. High rate of velocity during a decennial census.

 

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Data must have high veracity and systems must be very robust. The semantic integrity of conceptual metadata concerning what exactly is measured and the resulting limits of inference remain a challenge

Visualization

Data visualization is useful for data review, operational activity and general analysis. It continues to evolve.

 

Data Quality (syntax)

Data quality should be high and statistically checked for accuracy and reliability throughout the collection process.

 

Data Types

Pre-defined ASCII strings and numerical data

 

Data Analytics

Analytics are required for recommendation systems, continued monitoring and general survey improvement.

 

Big Data Specific Challenges (Gaps)

Improving recommendation systems that reduce costs and improve quality while providing confidentiality safeguards that are reliable and publically auditable.

Big Data Specific Challenges in Mobility

 Mobile access is important.

Security & Privacy

Requirements

All data must be both confidential and secure. All processes must be auditable for security and confidentiality as required by various legal statutes.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Recommender systems have features in common to e-commerce like Amazon, Netflix, UPS etc.

 

More Information (URLs)

 

Note: <additional comments>

       

Non Traditional Data in Statistical Survey Response Improvement (Adaptive Design)

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Non Traditional Data in Statistical Survey Response Improvement (Adaptive Design)

Vertical (area)

Government Statistical Logistics

Author/Company/Email

Cavan Capps: U.S. Census Bureau cavan.paul.capps@census.gov

Actors/Stakeholders and their roles and responsibilities

U.S. statistical agencies are charged to be the leading authoritative sources about the nation’s people and economy, while honoring privacy and rigorously protecting confidentiality. This is done by working with states, local governments and other government agencies.

 

Goals

To use advanced methods, that are open and scientifically objective, the statistical agencies endeavor to improve the quality, the specificity and the timeliness of statistics provided while reducing operational costs and maintaining the confidentiality of those measured.

 

Use Case Description

Survey costs are increasing as survey response declines. The potential of using non-traditional commercial and public data sources from the web, wireless communication, electronic transactions mashed up analytically with traditional surveys to improve statistics for small area geographies, new measures and to improve the timeliness of released statistics.

 

Current

Solutions

Compute(System)

Linux systems

Storage

SAN and Direct Storage

Networking

Fiber, 10 gigabit Ethernet, Infiniband 40 gigabit.

Software

Hadoop, Spark, Hive, R, SAS, Mahout, Allegrograph, MySQL, Oracle, Storm, BigMemory, Cassandra, Pig

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Survey data, other government administrative data, web scrapped data, wireless data, e-transaction data, potentially social media data and positioning data from various sources.

 

Volume (size)

TBD

Velocity

(e.g. real time)

TBD

Variety

(multiple datasets, mashup)

Textual data as well as the traditionally defined strings and numerical fields. Data can be from multiple datasets mashed together for analytical use.

 

Variability (rate of change)

TBD.

 

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Data must have high veracity and systems must be very robust. The semantic integrity of conceptual metadata concerning what exactly is measured and the resulting limits of inference remain a challenge

Visualization

Data visualization is useful for data review, operational activity and general analysis. It continues to evolve.

 

Data Quality (syntax)

Data quality should be high and statistically checked for accuracy and reliability throughout the collection process.

 

Data Types

Textual data, pre-defined ASCII strings and numerical data

 

Data Analytics

Analytics are required to create reliable estimates using data from traditional survey sources, government administrative data sources and non-traditional sources from the digital economy.

 

Big Data Specific Challenges (Gaps)

Improving analytic and modeling systems that provide reliable and robust statistical estimated using data from multiple sources, that are scientifically transparent and while providing confidentiality safeguards that are reliable and publically auditable.

Big Data Specific Challenges in Mobility

 Mobile access is important.

Security & Privacy

Requirements

All data must be both confidential and secure. All processes must be auditable for security and confidentiality as required by various legal statutes.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Statistical estimation that provide more detail, on a more near real time basis for less cost. The reliability of estimated statistics from such “mashed up” sources still must be evaluated.

More Information (URLs)

 

Note: <additional comments>

       

Commercial

Cloud Eco-System, for Financial Industries (Banking, Securities & Investments, Insurance) transacting business within the United States

Draft, Ver. 0.1_Aug. 24Th, 2013: NBD (NIST Big Data) Finance Industries (FI) Taxonomy/Requirements WG Use Case

Use Case Title

This use case represents one approach to implementing a BD (Big Data) strategy, within a Cloud Eco-System, for FI (Financial Industries) transacting business within the United States.

Vertical (area)

The following lines of business (LOB) include:

Banking, including: Commercial, Retail, Credit Cards, Consumer Finance, Corporate Banking, Transaction Banking, Trade Finance, and Global Payments.

Securities & Investments, such as; Retail Brokerage, Private Banking/Wealth Management, Institutional Brokerages, Investment Banking, Trust Banking, Asset Management, Custody & Clearing Services

Insurance, including; Personal and Group Life, Personal and Group Property/Casualty, Fixed & Variable Annuities, and Other Investments

 

Please Note: Any Public/Private entity, providing financial services within the regulatory and jurisdictional risk and compliance purview of the United States, are required to satisfy a complex multilayer number of regulatory GRC/CIA (Governance, Risk & Compliance/Confidentiality, Integrity & Availability) requirements, as overseen by various jurisdictions and agencies, including; Fed., State, Local and cross-border.

Author/Company/Email

Pw Carey, Compliance Partners, LLC, pwc.pwcarey@email.com

Actors/Stakeholders and their roles and responsibilities

Regulatory and advisory organizations and agencies including the; SEC (Securities & Exchange Commission), FDIC (Federal Deposit Insurance Corporation), CFTC (Commodity Futures Trading Commission), US Treasury, PCAOB (Public Corporation Accounting & Oversight Board), COSO, CobiT, reporting supply chains & stakeholders, investment community, share holders, pension funds, executive management, data custodians, and employees.

 

At each level of a financial services organization, an inter-related and inter-dependent mix of duties, obligations and responsibilities are in-place, which are directly responsible for the performance, preparation and transmittal of financial data, thereby satisfying both the regulatory GRC (Governance, Risk & Compliance) and CIA (Confidentiality, Integrity & Availability) of their organizations financial data. This same information is directly tied to the continuing reputation, trust and survivability of an organization's business.

Goals

The following represents one approach to developing a workable BD/FI strategy within the financial services industry. Prior to initiation and switch-over, an  organization must perform the following baseline methodology for utilizing BD/FI within a Cloud Eco-system for both public and private financial entities offering financial services within the regulatory confines of the United States; Federal, State, Local and/or cross-border such as the UK, EU and China.

 

Each financial services organization must approach the following disciplines supporting their BD/FI initiative, with an understanding and appreciation for the impact each of the following four overlaying and inter-dependent forces will play in a workable implementation.

 

These four areas are:

1.     People (resources),

2.     Processes (time/cost/ROI),

3.     Technology (various operating systems, platforms and footprints) and

4.     Regulatory Governance (subject to various and multiple regulatory agencies).

 

In addition, these four areas must work through the process of being; identified, analyzed, evaluated, addressed, tested, and reviewed in preparation for attending to the following implementation phases:

1.     Project Initiation and Management Buy-in

2.     Risk Evaluations & Controls

3.     Business Impact Analysis

4.     Design, Development & Testing of the Business Continuity Strategies

5.     Emergency Response & Operations (aka; Disaster Recovery)

6.     Developing & Implementing Business Continuity Plans

7.     Awareness & Training Programs

8.     Maintaining & Exercising Business Continuity, (aka: Maintaining Regulatory Currency)

 

Please Note: Whenever appropriate, these eight areas should be tailored and modified to fit the requirements of each organizations unique and specific corporate culture and line of financial services.

Use Case Description

Big Data as developed by Google was intended to serve as an Internet Web site indexing tool to help them sort, shuffle, categorize and label the Internet. At the outset, it was not viewed as a replacement for legacy IT data infrastructures. With the spin-off development within OpenGroup and Hadoop, BigData has evolved into a robust data analysis and storage tool that is still under going development. However, in the end, BigData is still being developed as an adjunct to the current IT client/server/big iron data warehouse architectures which is better at somethings, than these same data warehouse environments, but not others.

 

Currently within FI, BD/Hadoop is used for fraud detection, risk analysis and assessments as well as improving the organizations knowledge and understanding of the customers via a strategy known as....'know your customer', pretty clever, eh?

 

However, this strategy still must following a well thought out taxonomy, that satisfies the entities unique, and individual requirements. One such strategy is the following formal methodology which address two fundamental yet paramount questions; “What are we doing”? and “Why are we doing it”?:

 

1). Policy Statement/Project Charter (Goal of the Plan, Reasons and Resources....define each),

2). Business Impact Analysis (how does effort improve our business services),

3). Identify System-wide Policies, Procedures and Requirements

4). Identify Best Practices for Implementation (including Change Management/Configuration Management) and/or Future Enhancements,

5). Plan B-Recovery Strategies (how and what will need to be recovered, if necessary),

6). Plan Development (Write the Plan and Implement the Plan Elements),

7). Plan buy-in and Testing (important everyone Knows the Plan, and Knows What to Do), and

8). Implement the Plan (then identify and fix gaps during first 3 months, 6 months, and annually after initial implementation)

9). Maintenance (Continuous monitoring and updates to reflect the current enterprise environment)

10). Lastly, System Retirement

Current

Solutions

Compute(System)

Currently, Big Data/Hadoop within a Cloud Eco-system within the FI is operating as part of a hybrid system, with BD being utilized as a useful tool for conducting risk and fraud analysis, in addition to assisting in organizations in the process of ('know your customer'). These are three areas where BD has proven to be good at;

1.     detecting fraud,

2.     associated risks and a

3.     'know your customer' strategy.

 

At the same time, the traditional client/server/data warehouse/RDBM (Relational Database Management ) systems are use for the handling, processing, storage and archival of the entities financial data. Recently the SEC has approved the initiative for requiring the FI to submit financial statements via the XBRL (extensible Business Related Markup Language), as of May 13th, 2013.

Storage

The same Federal, State, Local and cross-border legislative and regulatory requirements can impact any and all geographical locations, including; VMware, NetApps, Oracle, IBM, Brocade, et cetera.

 

Please Note: Based upon legislative and regulatory concerns, these storage solutions for FI data must ensure this same data conforms to US regulatory compliance for GRC/CIA, at this point in time.

 

For confirmation, please visit the following agencies web sites: SEC (Security and Exchange Commission), CFTC (Commodity Futures Trading Commission), FDIC (Federal Deposit Insurance Corporation), DOJ (Dept. of Justice), and my favorite the PCAOB (Public Company Accounting and Oversight Board).

Networking

Please Note: The same Federal, State, Local and cross-border legislative and regulatory requirements can impact any and all geographical locations of HW/SW, including but not limited to; WANs, LANs, MANs WiFi, fiber optics, Internet Access, via Public, Private, Community and Hybrid Cloud environments, with or without VPNs.

Based upon legislative and regulatory concerns, these networking solutions for FI data must ensure this same data conforms to US regulatory compliance for GRC/CIA, such as the US Treasury Dept., at this point in time.

For confirmation, please visit the following agencies web sites: SEC (Security and Exchange Commission), CFTC (Commodity Futures Trading Commission), FDIC (Federal Deposit Insurance Corporation), US Treasury Dept., DOJ (Dept. of Justice), and my favorite the PCAOB (Public Company Accounting and Oversight Board).

Software

Please Note: The same legislative and regulatory obligations impacting the geographical location of HW/SW, also restricts the location for; Hadoop, MapReduce, Open-source, and/or Vendor Proprietary such as AWS (Amazon Web Services), Google Cloud Services, and Microsoft

 

Based upon legislative and regulatory concerns, these software solutions incorporating both SOAP (Simple Object Access Protocol), for Web development and OLAP (Online Analytical Processing) software language for databases, specifically in this case for FI data, both must ensure this same data conforms to US regulatory compliance for GRC/CIA, at this point in time.

 

For confirmation, please visit the following agencies web sites: SEC (Security and Exchange Commission), CFTC (Commodity Futures Trading Commission), US Treasury, FDIC (Federal Deposit Insurance Corporation), DOJ (Dept. of Justice), and my favorite the PCAOB (Public Company Accounting and Oversight Board).

Big Data
Characteristics

 

 

Data Source (distributed/

centralized)

Please Note: The same legislative and regulatory obligations impacting the geographical location of HW/SW, also impacts the location for; both distributed/centralized data sources flowing into HA/DR Environment and HVSs (Hosted Virtual Servers), such as the following constructs: DC1---> VMWare/KVM (Clusters, w/Virtual Firewalls), Data link-Vmware Link-Vmotion Link-Network Link, Multiple PB of NAS (Network as A Service), DC2--->, VMWare/KVM (Clusters w/Virtual Firewalls), DataLink (Vmware Link, Vmotion Link, Network Link), Multiple PB of NAS (Network as A Service), (Requires Fail-Over Virtualization), among other considerations.

 

Based upon legislative and regulatory concerns, these data source solutions, either distributed and/or centralized for FI data, must ensure this same data conforms to US regulatory compliance for GRC/CIA, at this point in time.

 

For confirmation, please visit the following agencies web sites: SEC (Security and Exchange Commission), CFTC (Commodity Futures Trading Commission), US Treasury, FDIC (Federal Deposit Insurance Corporation), DOJ (Dept. of Justice), and my favorite the PCAOB (Public Company Accounting and Oversight Board).

Volume (size)

Tera-bytes up to Peta-bytes.

Please Note: This is a 'Floppy Free Zone'.

Velocity

(e.g. real time)

Velocity is more important for fraud detection, risk assessments and the 'know your customer' initiative within the BD FI.

 

Please Note: However, based upon legislative and regulatory concerns, velocity is not at issue regarding BD solutions for FI data, except for fraud detection, risk analysis and customer analysis.

 

Based upon legislative and regulatory restrictions, velocity is not at issue, rather the primary concern for FI data, is that it must satisfy all US regulatory compliance obligations for GRC/CIA, at this point in time.

 

Variety

(multiple data sets, mash-up)

Multiple virtual environments either operating within a batch processing architecture or a hot-swappable parallel architecture supporting fraud detection, risk assessments and customer service solutions.

 

Please Note: Based upon legislative and regulatory concerns, variety is not at issue regarding BD solutions for FI data within a Cloud Eco-system, except for fraud detection, risk analysis and customer analysis.

 

Based upon legislative and regulatory restrictions, variety is not at issue, rather the primary concern for FI data, is that it must satisfy all US regulatory compliance obligations for GRC/CIA, at this point in time.

 

Variability (rate of change)

Please Note: Based upon legislative and regulatory concerns, variability is not at issue regarding BD solutions for FI data within a Cloud Eco-system, except for fraud detection, risk analysis and customer analysis.

 

Based upon legislative and regulatory restrictions, variability is not at issue, rather the primary concern for FI data, is that it must satisfy all US regulatory compliance obligations for GRC/CIA, at this point in time.

 

Variability with BD FI within a Cloud Eco-System will depending upon the strength and completeness of the SLA agreements, the costs associated with (CapEx), and depending upon the requirements of the business.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Please Note: Based upon legislative and regulatory concerns, veracity is not at issue regarding BD solutions for FI data within a Cloud Eco-system, except for fraud detection, risk analysis and customer analysis.

 

Based upon legislative and regulatory restrictions, veracity is not at issue, rather the primary concern for FI data, is that it must satisfy all US regulatory compliance obligations for GRC/CIA, at this point in time.

 

Within a Big Data Cloud Eco-System, data integrity is important over the entire life-cycle of the organization due to regulatory and compliance issues related to individual data privacy and security, in the areas of CIA (Confidentiality, Integrity & Availability) and GRC (Governance, Risk & Compliance) requirements.

Visualization

Please Note: Based upon legislative and regulatory concerns, visualization is not at issue regarding BD solutions for FI data, except for fraud detection, risk analysis and customer analysis, FI data is handled by traditional client/server/data warehouse big iron servers.

 

Based upon legislative and regulatory restrictions, visualization is not at issue, rather the primary concern for FI data, is that it must satisfy all US regulatory compliance obligations for GRC/CIA, at this point in time.

 

Data integrity within BD is critical and essential over the entire life-cycle of the organization due to regulatory and compliance issues related to CIA (Confidentiality, Integrity & Availability) and GRC (Governance, Risk & Compliance) requirements.

Data Quality

Please Note: Based upon legislative and regulatory concerns, data quality will always be an issue, regardless of the industry or platform.

 

Based upon legislative and regulatory restrictions, data quality is at the core of data integrity, and is the primary concern for FI data, in that it must satisfy all US regulatory compliance obligations for GRC/CIA, at this point in time.

 

For BD/FI data, data integrity is critical and essential over the entire life-cycle of the organization due to regulatory and compliance issues related to CIA (Confidentiality, Integrity & Availability) and GRC (Governance, Risk & Compliance) requirements.

Data Types

Please Note: Based upon legislative and regulatory concerns, data types is important in that it must have a degree of consistency and especially survivability during audits and digital forensic investigations where the data format deterioration can negatively impact both an audit and a forensic investigation when passed through multiple cycles.

 

For BD/FI data, multiple data types and formats, include but is not limited to; flat files, .txt, .pdf, android application files, .wav, .jpg and VOIP (Voice over IP)

Data Analytics

Please Note: Based upon legislative and regulatory concerns, data analytics is an issue regarding BD solutions for FI data, especially in regards to fraud detection, risk analysis and customer analysis.

 

However, data analytics for FI data is currently handled by traditional client/server/data warehouse big iron servers which must ensure they comply with and satisfy all United States GRC/CIA requirements, at this point in time.

 

For BD/FI data analytics must be maintained in a format that is non-destructive during search and analysis processing and procedures.

Big Data Specific Challenges (Gaps)

Currently, the areas of concern associated with BD/FI with a Cloud Eco-system, include the aggregating and storing of data (sensitive, toxic and otherwise) from multiple sources which can and does create administrative and management problems related to the following:

·         Access control

·         Management/Administration

·         Data entitlement and

·         Data ownership

 

However, based upon current analysis, these concerns and issues are widely known and are being addressed at this point in time, via the R&D (Research & Development) SDLC/HDLC (Software Development Life Cycle/Hardware Development Life Cycle) sausage makers of technology. Please stay tuned for future developments in this regard

Big Data Specific Challenges in Mobility

Mobility is a continuously growing layer of technical complexity, however, not all Big Data mobility solutions are technical in nature. There are to interrelated and co-dependent parties who required to work together to find a workable and maintainable solution, the FI business side and IT. When both are in agreement sharing a, common lexicon, taxonomy and appreciation and understand for the requirements each is obligated to satisfy, these technical issues can be addressed.

 

Both sides in this collaborative effort will encounter the following current and on-going FI data considerations:

·         Inconsistent category assignments

·         Changes to classification systems over time

·         Use of multiple overlapping or

·         Different categorization schemes

 

In addition, each of these changing and evolving inconsistencies, are required to satisfy the following data characteristics associated with ACID:

·         Atomic- All of the work in a transaction completes (commit) or none of it completes

·         Consistent- A transmittal transforms the database from one consistent state to another consistent state. Consistency is defined in terms of constraints.

·         Isolated- The results of any changes made during a transaction are not visible until the transaction has committed.

·         Durable- The results of a committed transaction survive failures.

When each of these data categories are satisfied, well, it's a glorious thing. Unfortunately, sometimes glory is not in the room, however, that does not mean we give up the effort to resolve these issues.

Security & Privacy

Requirements

No amount of security and privacy due diligence will make up for the innate deficiencies associated with human nature that creep into any program and/or strategy. Currently, the BD/FI must contend with a growing number of risk buckets, such as:

·         AML-Anti-money Laundering

·         CDD- Client Due Diligence

·         Watch-lists

·         FCPA – Foreign Corrupt Practices Act

 

to name a few.

 

For a reality check, please consider Mr. Harry M. Markopolos's nine year effort to get the SEC among other agencies to do their job and shut down Mr. Bernard Madoff's billion dollar ponzi scheme.

 

However, that aside, identifying and addressing the privacy/security requirements of the FI, providing services within a BD/Cloud Eco-system, via continuous improvements in:

1.     technology,

2.     processes,

3.     procedures,

4.     people and

5.     regulatory jurisdictions

is a far better choice for both the individual and the organization, especially when considering the alternative.

 

Utilizing a layered approach, this strategy can be broken down into the following sub categories:

1.     Maintaining operational resilience

2.     Protecting valuable assets

3.     Controlling system accounts

4.     Managing security services effectively, and

5.     Maintaining operational resilience

 

For additional background security and privacy solutions addressing both security and privacy, we'll refer you to the two following organization's:

·         ISACA (International Society of Auditors & Computer Analysts)

·         isc2 (International Security Computer & Systems Auditors)

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Areas of concern include the aggregating and storing data from multiple sources can create problems related to the following:

·         Access control

·         Management/Administration

·         Data entitlement and

·         Data ownership

 

Each of these areas are being improved upon, yet they still must be considered and addressed , via access control solutions, and SIEM (Security Incident/Event Management) tools.

 

I don't believe we're there yet, based upon current security concerns mentioned whenever Big Data/Hadoop within a Cloud Eco-system is brought up in polite conversation.

 

Current and on-going challenges to implementing BD Finance within a Cloud Eco, as well as traditional client/server data warehouse architectures, include the following areas of Financial Accounting under both US GAAP (Generally Accepted Accounting Practices) or IFRS (…..):

XBRL (extensible Business Related Markup Language)

Consistency (terminology, formatting, technologies, regulatory gaps)

 

SEC mandated use of XBRL (extensible Business Related Markup Language) for regulatory financial reporting.

 

SEC, GAAP/IFRS and the yet to be fully resolved new financial legislation impacting reporting requirements are changing and point to trying to improve the implementation, testing, training, reporting and communication best practices required of an independent auditor, regarding:

Auditing, Auditor's reports, Control self-assessments, Financial audits, GAAS / ISAs, Internal audits, and the Sarbanes–Oxley Act of 2002 (SOX).

More Information (URLs)

  1. Cloud Security Alliance Big Data Working Group, “Top 10 Challenges in Big Data Security and Privacy”, 2012.
  2. The IFRS, Securities and Markets Working Group, www.xbrl-eu.org
  3. IEEE Big Data conference http://www.ischool.drexel.edu/bigdat...013/topics.htm
  4. MapReduce http://www.mapreduce.org.
  5. PCAOB http://www.pcaob.org
  6.  
  7. http://www.ey.com/GL/en/Industries/F...ices/Insurance
  8. http://www.treasury.gov/resource-cen...s/default.aspx
  9. CFTC http://www.cftc.org
  10.  
  11. SEC http://www.sec.gov
  12.  
  13. FDIC http://www.fdic.gov
  14.  
  15. COSO http://www.coso.org
  16.  
  17. isc2 International Information Systems Security Certification Consortium, Inc.: http://www.isc2.org
  18.  
  19. ISACA Information Systems Audit and Control Association: http://www.isca.org
  20.  
  21. IFARS http://www.ifars.org
  22.  
  23. Apache http://www.opengroup.org
  24.  
  25. http://www.computerworld.com/s/artic...ity_issues?tax ...
  26. "No One Would Listen: A True Financial Thriller" (hard-cover book). Hoboken, NJ: John Wiley & Sons. March 2010. Retrieved April 30, 2010. ISBN 978-0-470-55373-2
  27. Assessing the Madoff Ponzi Scheme and Regulatory Failures (Archive of: Subcommittee on Capital Markets, Insurance, and Government Sponsored Enterprises Hearing) (http:/ / financialserv. edgeboss. net/ wmedia/financialserv/ hearing020409. wvx) (Windows Media). U.S. House Financial Services Committee. February 4, 2009. Retrieved June 29, 2009.

19.  COSO, The Committee of Sponsoring Organizations of the Treadway Commission (COSO), Copyright©  2013, www.coso.org.

20.  ITIL Information Technology Infrastructure Library, Copyright© 2007-13 APM Group Ltd. All rights reserved, Registered in England No. 2861902, www.itil-officialsite.com.

21.  CobiT, Ver. 5.0, 2013, ISACA, Information Systems Audit and Control Association, (a framework for IT Governance and Controls), www.isaca.org.

22.  TOGAF, Ver. 9.1, The Open Group Architecture Framework (a framework for IT architecture), www.opengroup.org.

23.  ISO/IEC 27000:2012 Info. Security Mgt., International Organization for Standardization and the International Electrotechnical Commission, www.standards.iso.org/

Note: <additional comments> Please feel free to improve our INITIAL DRAFT, Ver. 0.1, August 25th, 2013....as we do not consider our efforts to be pearls, at this point in time......Respectfully yours, Pw Carey, Compliance Partners, LLC_pwc.pwcarey@gmail.com

       
 

Mendeley – An International Network of Research

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Mendeley – An International Network of Research

Vertical (area)

Commercial Cloud Consumer Services

Author/Company/Email

William Gunn / Mendeley / william.gunn@mendeley.com

Actors/Stakeholders and their roles and responsibilities

Researchers, librarians, publishers, and funding organizations.

Goals

To promote more rapid advancement in scientific research by enabling researchers to efficiently collaborate, librarians to understand researcher needs, publishers to distribute research findings more quickly and broadly, and funding organizations to better understand the impact of the projects they fund.

 

Use Case Description

 

Mendeley has built a database of research documents and facilitates the creation of shared bibliographies. Mendeley uses the information collected about research reading patterns and other activities conducted via the software to build more efficient literature discovery and analysis tools. Text mining and classification systems enables automatic recommendation of relevant research, improving the cost and performance of research teams, particularly those engaged in curation of literature on a particular subject, such as the Mouse Genome Informatics group at Jackson Labs, which has a large team of manual curators who scan the literature. Other use cases include enabling publishers to more rapidly disseminate publications, facilitating research institutions and librarians with data management plan compliance, and enabling funders to better understand the impact of the work they fund via real-time data on the access and use of funded research.

 

Current

Solutions

Compute(System)

Amazon EC2

Storage

HDFS Amazon S3

Networking

Client-server connections between Mendeley and end user machines, connections between Mendeley offices and Amazon services.

Software

Hadoop, Scribe, Hive, Mahout, Python

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Distributed and centralized

Volume (size)

    15TB presently, growing about 1 TB/month

Velocity

(e.g. real time)

Currently Hadoop batch jobs are scheduled daily, but work has begun on real-time recommendation

Variety

(multiple datasets, mashup)

PDF documents and log files of social network and client activities

Variability (rate of change)

Currently a high rate of growth as more researchers sign up for the service, highly fluctuating activity over the course of the year

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Metadata extraction from PDFs is variable, it’s challenging to identify duplicates, there’s no universal identifier system for documents or authors (though ORCID proposes to be this)

Visualization

Network visualization via Gephi, scatterplots of readership vs. citation rate, etc

Data Quality

90% correct metadata extraction according to comparison with Crossref, Pubmed, and Arxiv

Data Types

Mostly PDFs, some image, spreadsheet, and presentation files

Data Analytics

Standard libraries for machine learning and analytics, LDA, custom built reporting tools for aggregating readership and social activities per document

Big Data Specific Challenges (Gaps)

The database contains ~400M documents, roughly 80M unique documents, and receives 5-700k new uploads on a weekday. Thus a major challenge is clustering matching documents together in a computationally efficient way (scalable and parallelized) when they’re uploaded from different sources and have been slightly modified via third-part annotation tools or publisher watermarks and cover pages

Big Data Specific Challenges in Mobility

Delivering content and services to various computing platforms from Windows desktops to Android and iOS mobile devices

 

Security & Privacy

Requirements

Researchers often want to keep what they’re reading private, especially industry researchers, so the data about who’s reading what has access controls.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

This use case could be generalized to providing content-based recommendations to various scenarios of information consumption

 

 

More Information (URLs)

 

 

http://mendeley.com http://dev.mendeley.com

 

Note: <additional comments>

       

Netflix Movie Service

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Netflix Movie Service

Vertical (area)

Commercial Cloud Consumer Services

Author/Company/Email

Geoffrey Fox, Indiana University gcf@indiana.edu

Actors/Stakeholders and their roles and responsibilities

Netflix Company (Grow sustainable Business), Cloud Provider (Support streaming and data analysis), Client user (Identify and watch good movies on demand)

Goals

Allow streaming of user selected movies to satisfy multiple objectives (for different stakeholders) -- especially retaining subscribers. Find best possible ordering of a set of videos for a user (household) within a given context in real-time; maximize movie consumption.

Use Case Description

Digital movies stored in cloud with metadata; user profiles and rankings for small fraction of movies for each user. Use multiple criteria – content based recommender system; user-based recommender system; diversity. Refine algorithms continuously with A/B testing.

Current

Solutions

Compute(System)

Amazon Web Services AWS

Storage

Uses Cassandra NoSQL technology with Hive, Teradata

Networking

Need Content Delivery System to support effective streaming video

Software

Hadoop and Pig; Cassandra; Teradata

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Add movies institutionally. Collect user rankings and profiles in a distributed fashion

Volume (size)

Summer 2012. 25 million subscribers; 4 million ratings per day; 3 million searches per day; 1 billion hours streamed in June 2012. Cloud storage 2 petabytes (June 2013)

Velocity

(e.g. real time)

Media (video and properties) and Rankings continually updated

Variety

(multiple datasets, mashup)

Data varies from digital media to user rankings, user profiles and media properties for content-based recommendations

Variability (rate of change)

Very competitive business. Need to aware of other companies and trends in both content (which Movies are hot) and technology. Need to investigate new business initiatives such as Netflix sponsored content

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Success of business requires excellent quality of service

Visualization

Streaming media and quality user-experience to allow choice of content

Data Quality

Rankings are intrinsically “rough” data and need robust learning algorithms

Data Types

Media content, user profiles, “bag” of user rankings

Data Analytics

Recommender systems and streaming video delivery. Recommender systems are always personalized and use logistic/linear regression, elastic nets, matrix factorization, clustering, latent Dirichlet allocation, association rules, gradient boosted decision trees and others. Winner of Netflix competition (to improve ratings by 10%) combined over 100 different algorithms.

Big Data Specific Challenges (Gaps)

Analytics needs continued monitoring and improvement.

Big Data Specific Challenges in Mobility

Mobile access important

 

Security & Privacy

Requirements

Need to preserve privacy for users and digital rights for media.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Recommender systems have features in common to e-commerce like Amazon. Streaming video has features in common with other content providing services like iTunes, Google Play, Pandora and Last.fm

 

More Information (URLs)

http://www.slideshare.net/xamat/building-largescale-realworld-recommender-systems-recsys2012-tutorial by Xavier Amatriain

http://techblog.netflix.com/

Note: <additional comments>

       

Web Search

NBD(NIST Big Data) Requirements WG Use Case Template

Use Case Title

Web Search (Bing, Google, Yahoo..)

Vertical (area)

Commercial Cloud Consumer Services

Author/Company/Email

Geoffrey Fox, Indiana University gcf@indiana.edu

Actors/Stakeholders and their roles and responsibilities

Owners of web information being searched; search engine companies; advertisers; users

Goals

Return in ~0.1 seconds, the results of a search based on average of 3 words; important to maximize “precision@10”; number of great responses in top 10 ranked results

Use Case Description

.1) Crawl the web;  2) Pre-process data to get searchable things (words, positions); 3) Form Inverted Index mapping words to documents; 4) Rank relevance of documents: PageRank; 5) Lots of technology for advertising, “reverse engineering ranking” “preventing reverse engineering”; 6) Clustering of documents into topics (as in Google News) 7) Update results efficiently

Current

Solutions

Compute(System)

Large Clouds

Storage

Inverted Index not huge; crawled documents are petabytes of text – rich media much more

Networking

Need excellent external network links; most operations pleasingly parallel and I/O sensitive. High performance internal network not needed

Software

MapReduce + Bigtable; Dryad + Cosmos. PageRank. Final step essentially a recommender engine

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Distributed web sites

Volume (size)

45B web pages total, 500M photos uploaded each day,  100 hours of video uploaded to YouTube each minute

Velocity

(e.g. real time)

Data continually updated

Variety

(multiple datasets, mashup)

Rich set of functions. After processing, data similar for each page (except for media types)

Variability (rate of change)

Average page has life of a few months

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Exact results not essential but important to get main hubs and authorities for search query

Visualization

Not important although page layout critical

Data Quality

A lot of duplication and spam

Data Types

Mainly text but more interest in rapidly growing image and video

Data Analytics

Crawling; searching including topic based search; ranking; recommending

Big Data Specific Challenges (Gaps)

Search of “deep web” (information behind query front ends)

Ranking of responses sensitive to intrinsic value (as in Pagerank) as well as advertising value

Link to user profiles and social network data

Big Data Specific Challenges in Mobility

Mobile search must have similar interfaces/results

 

Security & Privacy

Requirements

Need to be sensitive to crawling restrictions. Avoid Spam results

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Relation to Information retrieval such as search of scholarly works.

 

 

More Information (URLs)

http://www.slideshare.net/kleinerper...et-trends-2013

http://webcourse.cs.technion.ac.il/2..._Lectures.html

http://www.ifis.cs.tu-bs.de/teaching/ss-11/irws

http://www.slideshare.net/beechung/r...rialpart1intro

http://www.worldwidewebsize.com/

Note: <additional comments>

       

IaaS (Infrastructure as a Service) Big Data Business Continuity & Disaster Recovery (BC/DR) Within A Cloud Eco-System

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

IaaS (Infrastructure as a Service) Big Data Business Continuity & Disaster Recovery (BC/DR) Within A Cloud Eco-System provided by Cloud Service Providers (CSPs) and Cloud Brokerage Service Providers (CBSPs)  

Vertical (area)

Large Scale Reliable Data Storage

Author/Company/Email

Pw Carey, Compliance Partners, LLC, pwc.pwcarey@email.com

Actors/Stakeholders and their roles and responsibilities

Executive Management, Data Custodians, and Employees responsible for the integrity, protection, privacy, confidentiality, availability, safety, security and survivability of a business by ensuring the 3-As of data accessibility to an organizations services are satisfied; anytime, anyplace and on any device.

Goals

The following represents one approach to developing a workable BC/DR strategy. Prior to outsourcing an organizations BC/DR onto the backs/shoulders of a CSP or CBSP, the organization must perform the following Use Case, which will provide each organization with a baseline methodology for business continuity and disaster recovery (BC/DR) best practices, within a Cloud Eco-system for both Public and Private organizations.

 

Each organization must approach the ten disciplines supporting BC/DR (Business Continuity/Disaster Recovery), with an understanding and appreciation for the impact each of the following four overlaying and inter-dependent forces will play in ensuring a workable solution to an entity's business continuity plan and requisite disaster recovery strategy. The four areas are; people (resources), processes (time/cost/ROI), technology (various operating systems, platforms and footprints) and governance (subject to various and multiple regulatory agencies).

These four concerns must be; identified, analyzed, evaluated, addressed, tested, reviewed, addressed during the following ten phases:

6.     Project Initiation and Management Buy-in

7.     Risk Evaluations & Controls

8.     Business Impact Analysis

9.     Design, Development & Testing of the Business Continuity Strategies

10.  Emergency Response & Operations (aka; Disaster Recovery

11.  Developing & Implementing Business Continuity Plans

12.  Awareness & Training Programs

13.  Maintaining & Exercising Business Continuity Plans, (aka: Maintaining Currency)

14.  Public Relations (PR) & Crises Management Plans

15.  Coordination with Public Agencies

Please Note: When appropriate, these ten areas can be tailored to fit the requirements of the organization.

Use Case Description

Big Data as developed by Google was intended to serve as an Internet Web site indexing tool to help them sort, shuffle, categorize and label the Internet. At the outset, it was not viewed as a replacement for legacy IT data infrastructures. With the spin-off development within OpenGroup and Hadoop, BigData has evolved into a robust data analysis and storage tool that is still under going development. However, in the end, BigData is still being developed as an adjunct to the current IT client/server/big iron data warehouse architectures which is better at somethings, than these same data warehouse environments, but not others.

As a result, it is necessary, within this business continuity/disaster recovery use case, we ask good questions, such as; why are we doing this and what are we trying to accomplish? What are our dependencies upon manual practices and when can we leverage them? What systems have been and remain outsourced to other organizations, such as our Telephony and what are their DR/BC business functions, if any? Lastly, we must recognize the functions that can be simplified and what are the preventative steps we can take that do not have a high cost associated with them such as simplifying business practices.

We must identify what are the critical business functions that need to be recovered, 1st, 2nd, 3rd in priority, or at a later time/date, and what is the Model of A Disaster we're trying to resolve, what are the types of disasters more likely to occur realizing that we don't need to resolve all types of disasters. When backing up data within a Cloud Eco-system is a good solution, this will shorten the fail-over time and satisfy the requirements of RTO/RPO (Response Time Objectives and Recovery Point Objectives. In addition there must be 'Buy-in', as this is not just an IT problem, it is a business services problem as well, requiring the testing of the Disaster Plan via formal walk-throughs,.et cetera. There should be a formal methodology for developing a BC/DR Plan, including: 1). Policy Statement (Goal of the Plan, Reasons and Resources....define each), 2). Business Impact Analysis (how does a shutdown impact the business financially and otherwise), 3). Identify Preventive Steps (can a disaster be avoided by taking prudent steps), 4). Recovery Strategies (how and what you will need to recover), 5). Plan Development (Write the Plan and Implement the Plan Elements), 6). Plan buy-in and Testing (very important so that everyone knows the Plan and knows what to do during its execution), and 7). Maintenance (Continuous changes to reflect the current enterprise environment)

Current

Solutions

Compute(System)

Cloud Eco-systems, incorporating IaaS (Infrastructure as a Service), supported by Tier 3 Data Centers....Secure Fault Tolerant (Power).... for Security, Power, Air Conditioning et cetera...geographically off-site data recovery centers...providing data replication services, Note: Replication is different from Backup. Replication only moves the changes since the last time a replication, including block level changes. The replication can be done quickly, with a five second window, while the data is replicated every four hours. This data snap shot is retained for seven business days, or longer if necessary. Replicated data can be moved to a Fail-over Center to satisfy the  organizations RPO (Recovery Point Objectives) and RTO (Recovery Time Objectives)

Storage

VMware, NetApps, Oracle, IBM, Brocade,

Networking

WANs, LANs, WiFi, Internet Access, via Public, Private, Community and Hybrid Cloud environments, with or without VPNs.

Software

Hadoop, MapReduce, Open-source, and/or Vendor Proprietary such as AWS (Amazon Web Services), Google Cloud Services, and Microsoft

Big Data
Characteristics

 

 

Data Source (distributed

/centralized)

Both distributed/centralized data sources flowing into HA/DR Environment and HVSs (Hosted Virtual Servers), such as the following: DC1---> VMWare/KVM (Clusters, w/Virtual Firewalls), Data link-Vmware Link-Vmotion Link-Network Link, Multiple PB of NAS (Network as A Service), DC2--->, VMWare/KVM (Clusters w/Virtual Firewalls), DataLink (Vmware Link, Vmotion Link, Network Link), Multiple PB of NAS (Network as A Service), (Requires Fail-Over Virtualization)

Volume (size)

Terabytes up to Petabytes

Velocity

(e.g. real time)

Tier 3 Data Centers with Secure Fault Tolerant (Power) for Security, Power, Air Conditioning. IaaS (Infrastructure as a Service) in this example, based upon NetApps. Replication is different from Backup, replication requires only moving the CHANGES since the last time a REPLICATION was performed, including the block level changes. The Replication can be done quickly as the data is Replicated every four hours. This replications can be performed within a 5 second window, and this Snap Shot will be kept for 7 business days, or longer if necessary to a Fail-Over Center.....at the RPO and RTO....

Variety

(multiple data sets, mash-up)

Multiple virtual environments either operating within a batch processing architecture or a hot-swappable parallel architecture.

Variability (rate of change)

Depending upon the SLA agreement, the costs (CapEx) increases, depending upon the RTO/RPO and the requirements of the business.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Data integrity is critical and essential over the entire life-cycle of the organization due to regulatory and compliance issues related to data CIA (Confidentiality, Integrity & Availability) and GRC (Governance, Risk & Compliance) data requirements.

Visualization

Data integrity is critical and essential over the entire life-cycle of the organization due to regulatory and compliance issues related to data CIA (Confidentiality, Integrity & Availability) and GRC (Governance, Risk & Compliance) data requirements.

Data Quality

Data integrity is critical and essential over the entire life-cycle of the organization due to regulatory and compliance issues related to data CIA (Confidentiality, Integrity & Availability) and GRC (Governance, Risk & Compliance) data requirements.

Data Types

Multiple data types and formats, including but not limited to; flat files, .txt, .pdf, android application files, .wav, .jpg and VOIP (Voice over IP)

Data Analytics

Must be maintained in a format that is non-destructive during search and analysis processing and procedures.

Big Data Specific Challenges (Gaps)

The complexities associated with migrating from a Primary Site to either a Replication Site or a Backup Site is not fully automated at this point in time. The goal is to enable the user to automatically initiate the Fail Over Sequence, moving Data Hosted within Cloud requires a well defined and continuously monitored server configuration management. In addition, both organizations must know which servers have to be restored and what are the dependencies and inter-dependencies between the Primary Site servers and Replication and/or Backup Site servers. This requires a continuous monitoring of both, since there are two solutions involved with this process, either dealing with servers housing stored images or servers running hot all the time, as in running parallel systems with hot-swappable functionality, all of which requires accurate and up-to-date information from the client.

Big Data Specific Challenges in Mobility

Mobility is a continuously growing layer of technical complexity, however, not all DR/BC solutions are technical in nature, as there are two sides required to work together to find a solution, the business side and the IT side. When they are in agreement, these technical issues must be addressed by the BC/DR strategy implemented and maintained by the entire organization. One area, which is not limited to mobility challenges, concerns a fundamental issue impacting most BC/DR solutions. If your Primary Servers (A,B,C) understand X,Y,Z....but your Secondary Virtual Replication/Backup Servers (a,b, c) over the passage of time, are not properly maintained (configuration management) and become out of sync with your Primary Servers, and only understand X, and Y, when called upon to perform a Replication or Back-up, well "Houston, we have a problem...."

Please Note: Over time all systems can and will suffer from sync-creep, some more than others, when relying upon manual processes to ensure system stability.

Security & Privacy

Requirements

Dependent upon the nature and requirements of the organization's industry verticals, such as; Finance, Insurance, and Life Sciences including both public and/or private entities, and the restrictions placed upon them by;regulatory, compliance and legal jurisdictions.

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Challenges to Implement BC/DR, include the following:

1) Recognition, a). Management Vision, b). Assuming the issue is an IT issue, when it is not just an IT issue, 2). People: a). Staffing levels - Many SMBs are understaffed in IT for their current workload, b). Vision - (Driven from the Top Down) Can the business and IT resources see the whole problem and craft a strategy such a 'Call List' in case of a Disaster, c). Skills - Are there resources who can architect, implement and test a BC/DR Solution, d). Time - Do Resources have the time and does the business have the Windows of Time for constructing and testing a DR/BC Solution as DR/BC is an additional Add-On Project the organization needs the time & resources. 3). Money - This can be turned in to an OpEx Solution rather than a CapEx Solution which and can be controlled by varying RPO/RTO, a). Capital is always a constrained resource, b). BC Solutions need to start with "what is the Risk" and "how does cost constrain the solution"?, 4). Disruption - Build BC/DR into the standard "Cloud" infrastructure (IaaS) of the SMB, a). Planning for BC/DR is disruptive to business resources, b). Testing BC is also disruptive.....

More Information (URLs)

9.     www.disasterrecovery.org/, (March, 2013).

10.  BC_DR From the Cloud, Avoid IT Disasters EN POINTE Technologies and dinCloud, Webinar Presenter Barry Weber, www.dincloud.com.

11.  COSO, The Committee of Sponsoring Organizations of the Treadway Commission (COSO), Copyright©  2013, www.coso.org.

12.  ITIL Information Technology Infrastructure Library, Copyright© 2007-13 APM Group Ltd. All rights reserved, Registered in England No. 2861902, www.itil-officialsite.com.

13.  CobiT, Ver. 5.0, 2013, ISACA, Information Systems Audit and Control Association, (a framework for IT Governance and Controls), www.isaca.org.

14.  TOGAF, Ver. 9.1, The Open Group Architecture Framework (a framework for IT architecture), www.opengroup.org.

15.  ISO/IEC 27000:2012 Info. Security Mgt., International Organization for Standardization and the International Electrotechnical Commission, www.standards.iso.org/.

16.  PCAOB, Public Company Accounting and Oversight Board, www.pcaobus.org.

Note: Please feel free to improve our INITIAL DRAFT, Ver. 0.1, August 10th, 2013....as we do not consider our efforts to be pearls, at this point in time......Respectfully yours, Pw Carey, Compliance Partners, LLC_pwc.pwcarey@gmail.com

       

Cargo Shipping

NBD (NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Cargo Shipping

Vertical (area)

Industry

Author/Company/Email

William Miller/MaCT USA mact-usa@att.net

Actors/Stakeholders and their roles and responsibilities

End-users (Sender/Recipients)

Transport Handlers (Truck/Ship/Plane)

Telecom Providers (Cellular/SATCOM)

Shippers (Shipping and Receiving)

Goals

Retention and analysis of items (Things) in transport

Use Case Description

 

The following use case defines the overview of a Big Data application related to the shipping industry (i.e. FedEx, UPS, DHL, etc.).  The shipping industry represents possible the largest potential use case of Big Data that is in common use today.  It relates to the identification, transport, and handling of item (Things) in the supply chain.  The identification of an item begins with the sender to the recipients and for all those in between with a need to know the location and time of arrive of the items while in transport.  A new aspect will be status condition of the items which will include sensor information, GPS coordinates, and a unique identification schema based upon a new ISO 29161 standards under development within ISO JTC1 SC31 WG2.  The data is in near real-time being updated when a truck arrives at a depot or upon delivery of the item to the recipient.  Intermediate conditions are not currently known, the location is not updated in real-time, items lost in a warehouse or while in shipment represent a problem potentially for homeland security.  The records are retained in an archive and can be accessed for xx days.

 

Current

Solutions

Compute(System)

Unknown

Storage

Unknown

Networking

LAN/T1/Internet Web Pages

Software

Unknown

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Centralized today

Volume (size)

 Large

Velocity

(e.g. real time)

The system is not currently real-time.

Variety

(multiple datasets, mashup)

Updated when the driver arrives at the depot and download the time and date the items were picked up.  This is currently not real-time.

Variability (rate of change)

Today the information is updated only when the items that were checked with a bar code scanner are sent to the central server.  The location is not currently displayed in real-time.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

 

Visualization

NONE

Data Quality

YES

Data Types

Not Available

Data Analytics

YES

Big Data Specific Challenges (Gaps)

Provide more rapid assessment of the identity, location, and conditions of the shipments, provide detailed analytics and location of problems in the system in real-time.

Big Data Specific Challenges in Mobility

 

Currently conditions are not monitored on-board trucks, ships, and aircraft

Security & Privacy

Requirements

 

Security need to be more robust

Highlight issues for generalizing this use case (e.g. for ref. architecture)

 

This use case includes local data bases as well as the requirement to synchronize with the central server.  This operation would eventually extend to mobile device and on-board systems which can track the location of the items and provide real-time update of the information including the status of the conditions, logging, and alerts to individuals who have a need to know.

 

More Information (URLs)

 

 

 

Note: <additional comments>

       

Figure 0 Cargo Shipping

Figure 0.png

Materials Data for Manufacturing

NBD(NIST Big Data) Requirements WG Use Case Template Aug 22 2013

Use Case Title

Materials Data

Vertical (area)

Manufacturing, Materials Research

Author/Company/Email

John Rumble, R&R Data Services; jumbleusa@earthlink.net

Actors/Stakeholders and their roles and responsibilities

Product Designers (Inputters of materials data in CAE)

Materials Researchers (Generators of materials data; users in some cases)

Materials Testers (Generators of materials data; standards developers)

Data distributors ( Providers of access to materials, often for profit)

Goals

Broaden accessibility, quality, and usability; Overcome proprietary barriers to sharing materials data; Create sufficiently large repositories of materials data to support discovery

Use Case Description

Every physical product is made from a material that has been selected for its properties, cost, and availability. This translates into hundreds of billion dollars of material decisions made every year.

 

In addition, as the Materials Genome Initiative has so effectively pointed out, the adoption of new materials normally takes decades (two to three) rather than a small number of years, in part because data on new materials is not easily available.

 

All actors within the materials life cycle today have access to very limited quantities of materials data, thereby resulting in materials-related decision that are non-optimal, inefficient, and costly.  While the Materials Genome Initiative is addressing one major and important aspect of the issue, namely the fundamental materials data necessary to design and test materials computationally, the issues related to physical measurements on physical materials ( from basic structural and thermal properties to complex performance properties to properties of novel (nanoscale materials) are not being addressed systematically, broadly (cross-discipline and internationally), or effectively (virtually no materials data meetings, standards groups, or dedicated funded programs).

 

One of the greatest challenges that Big Data approaches can address is predicting the performance of real materials (gram to ton quantities) starting at the atomistic, nanometer, and/or micrometer level of description.

 

As a result of the above considerations, decisions about materials usage are unnecessarily conservative, often based on older rather than newer materials R&D data, and not taking advantage of advances in modeling and simulations. Materials informatics is an area in which the new tools of data science can have major impact.

 

Current

Solutions

Compute(System)

None

Storage

Widely dispersed with many barriers to access

Networking

Virtually none

Software

Narrow approaches based on national programs (Japan, Korea, and China), applications (EU Nuclear program), proprietary solutions (Granta, etc.)

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Extremely distributed with data repositories existing only for a very few fundamental properties

Volume (size)

It is has been estimated (in the 1980s) that there were over 500,000 commercial materials made in the last fifty years. The last three decades has seen large growth in that number.

Velocity

(e.g. real time)

Computer-designed and theoretically design materials (e.g., nanomaterials) are growing over time

Variety

(multiple datasets, mashup)

Many data sets and virtually no standards for mashups

Variability (rate of change)

Materials are changing all the time, and new materials data are constantly being generated to describe the new materials

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

More complex material properties can require many (100s?) of independent variables to describe accurately. Virtually no activity no exists that is trying to identify and systematize the collection of these variables to create robust data sets.

Visualization

Important for materials discovery.  Potentially important to understand the dependency of properties on the many independent variables. Virtually unaddressed.

Data Quality

Except for fundamental data on the structural and thermal properties, data quality is poor or unknown. See Munro’s NIST Standard Practice Guide.

Data Types

Numbers, graphical, images

Data Analytics

Empirical and narrow in scope

Big Data Specific Challenges (Gaps)

1.     Establishing materials data repositories beyond the existing ones that focus on fundamental data

2.     Developing internationally-accepted data recording standards that can be used by a very diverse materials community, including developers materials test standards (such as ASTM and ISO), testing companies, materials producers, and R&D labs

3.     Tools and procedures to help organizations wishing to deposit proprietary materials in data repositories to mask proprietary information, yet to maintain the usability of data

4.     Multi-variable materials data visualization tools, in which the number of variables can be quite high

Big Data Specific Challenges in Mobility

Not important at this time

 

Security & Privacy

Requirements

Proprietary nature of many data very sensitive.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Development of standards; development of large scale repositories; involving industrial users; integration with CAE (don’t underestimate the difficulty of this – materials people are generally not as computer savvy as chemists, bioinformatics people, and engineers)

 

 

More Information (URLs)

 

 

 

Note: <additional comments>

       

Simulation driven Materials Genomic

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Simulation driven Materials Genomics

Vertical (area)

Scientific Research: Materials Science

Author/Company/Email

David Skinner/LBNL deskinner@lbl.gov

Actors/Stakeholders and their roles and responsibilities

Capability providers: National labs and energy hubs provide advanced materials genomics capabilities using computing and data as instruments of discovery.

User Community: DOE, industry and academic researchers as a user community seeking capabilities for rapid innovation in materials.    

Goals

Speed the discovery of advanced materials through informatically driven simulation surveys.

Use Case Description

Innovation of battery technologies through massive simulations spanning wide spaces of possible design. Systematic computational studies of innovation possibilities in photovoltaics. Rational design of materials based on search and simulation.

 

Current

Solutions

Compute(System)

Hopper.nersc.gov (150K cores) , omics-like data analytics hardware resources.

Storage

GPFS, MongoDB

Networking

10Gb

Software

PyMatGen, FireWorks, VASP, ABINIT, NWChem, BerkeleyGW, varied community codes

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Gateway-like. Data streams from simulation surveys driven on centralized peta/exascale systems. Widely distributed web of dataflows from central gateway to users.

Volume (size)

100TB (current), 500TB within 5 years. Scalable key-value and object store databases needed.

Velocity

(e.g. real time)

High-throughput computing (HTC), fine-grained tasking and queuing. Rapid start/stop for ensembles of tasks. Real-time data analysis for web-like responsiveness.

Variety

(multiple datasets, mashup)

Mashup of simulation outputs across codes and levels of theory. Formatting, registration and integration of datasets. Mashups of data across simulation scales. 

Variability (rate of change)

The targets for materials design will become more search and crowd-driven. The computational backend must flexibly adapt to new targets.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Validation and UQ of simulation with experimental data of varied quality. Error checking and bounds estimation from simulation inter-comparison.

Visualization

Materials browsers as data from search grows. Visual design of materials.

Data Quality (syntax)

UQ in results based on multiple datasets.

Propagation of error in knowledge systems.

Data Types

Key value pairs, JSON, materials fileformats

Data Analytics

MapReduce and search that join simulation and experimental data.

Big Data Specific Challenges (Gaps)

HTC at scale for simulation science. Flexible data methods at scale for messy data. Machine learning and knowledge systems that integrate data from publications, experiments, and simulations to advance goal-driven thinking in materials design.

Big Data Specific Challenges in Mobility

Potential exists for widespread delivery of actionable knowledge in materials science. Many materials genomics “apps” are amenable to a mobile platform.

Security & Privacy

Requirements

Ability to “sandbox” or create independent working areas between data stakeholders. Policy-driven federation of datasets. 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

 

An OSTP blueprint toward broader materials genomics goals was made available in May 2013.

 

More Information (URLs)

 

http://www.materialsproject.org

 

Note: <additional comments>

       

Defense

Large Scale Geospatial Analysis and Visualization; David Boyd, Data Tactics

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Large Scale Geospatial Analysis and Visualization

Vertical (area)

Defense – but applicable to many others

Author/Company/Email

David Boyd/Data Tactics/ dboyd@data-tactics.com

Actors/Stakeholders and their roles and responsibilities

Geospatial Analysts

Decision Makers

Policy Makers

Goals

Support large scale geospatial data analysis and visualization.

 

Use Case Description

As the number of geospatially aware sensors increase and the number of geospatially tagged data sources increases the volume geospatial data requiring complex analysis and visualization is growing exponentially.  Traditional GIS systems are generally capable of analyzing a millions of objects and easily visualizing thousands.   Today’s intelligence systems often contain trillions of geospatial objects and need to be able to visualize and interact with millions of objects.

 

 

Current

Solutions

Compute(System)

Compute and Storage systems -  Laptops to Large servers (see notes about clusters)

Visualization systems - handhelds to laptops

Storage

Compute and Storage - local disk or SAN

Visualization - local disk, flash ram

Networking

Compute and Storage - Gigabit or better LAN connection

Visualization - Gigabit wired connections, Wireless including WiFi (802.11), Cellular (3g/4g), or Radio Relay

Software

Compute and Storage – generally Linux or Win Server with Geospatially enabled RDBMS, Geospatial server/analysis software – ESRI ArcServer, Geoserver

Visualization -  Windows, Android, IOS – browser based visualization.  Some laptops may have local ArcMap.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Very distributed.

Volume (size)

Imagery – 100s of Terabytes

Vector Data – 10s of Gigabytes but billions of points

Velocity

(e.g. real time)

Some sensors delivery vector data in NRT.  Visualization of changes should be NRT.

Variety

(multiple datasets, mashup)

Imagery (various formats NITF, GeoTiff, CADRG)

Vector (various formats shape files, kml, text streams: Object types include points, lines, areas, polylines, circles, ellipses.

Variability (rate of change)

Moderate to high

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Data accuracy is critical and is controlled generally by three factors:

1.     Sensor accuracy is a big issue. 

2.     datum/spheroid.

3.     Image registration accuracy

Visualization

Displaying in a meaningful way large data sets (millions of points) on small devices (handhelds) at the end of low bandwidth networks.

Data Quality

The typical problem is visualization implying quality/accuracy not available in the original data.  All data should include metadata for accuracy or circular error probability.

Data Types

Imagery (various formats NITF, GeoTiff, CADRG)

Vector (various formats shape files, kml, text streams: Object types include points, lines, areas, polylines, circles, ellipses.

Data Analytics

Closest point of approach, deviation from route, point density over time, PCA and ICA

Big Data Specific Challenges (Gaps)

Indexing, retrieval and distributed analysis

Visualization generation and transmission

Big Data Specific Challenges in Mobility

Visualization of data at the end of low bandwidth wireless connections.

 

Security & Privacy

Requirements

Data is sensitive and must be completely secure in transit and at rest (particularly on handhelds)

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Geospatial data requires unique approaches to indexing and distributed analysis.

 

 

More Information (URLs)

Applicable Standards:  http://www.opengeospatial.org/standards

http://geojson.org/

http://earth-info.nga.mil/publications/specs/printed/CADRG/cadrg.html

 

Geospatial Indexing:  Quad Trees,  Space Filling Curves (Hilbert Curves) – You can google these for lots of references.

 

 

Note: The has been some work with in DoD related to this problem set.  Specifically, the DCGS-A standard cloud (DSC) stores, indexes, and analyzes some big data sources.   However, many issues still remain with visualization.

       

Object identification and tracking from Wide Area Large Format Imagery (WALF) Imagery or Full Motion Video (FMV) – Persistent Surveillance

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Object identification and tracking from Wide Area Large Format Imagery (WALF) Imagery or Full Motion Video (FMV) – Persistent Surveillance

Vertical (area)

Defense (Intelligence)

Author/Company/Email

David Boyd/Data Tactics/ dboyd@data-tactics.com

Actors/Stakeholders and their roles and responsibilities

1.     Civilian Military decision makers

2.     Intelligence Analysts

3.     Warfighters

Goals

To be able to process and extract/track entities (vehicles, people, packages) over time from the raw image data.  Specifically, the idea is to reduce the petabytes of data generated by persistent surveillance down to a manageable size (e.g. vector tracks)

 

Use Case Description

 

Persistent surveillance sensors can easily collect petabytes of imagery data in the space of a few hours.  It is unfeasible for this data to be processed by humans for either alerting or tracking purposes.  The data needs to be processed close to the sensor which is likely forward deployed since it is too large to be easily transmitted.  The data should be reduced to a set of geospatial object (points, tracks, etc.) which can easily be integrated with other data to form a common operational picture.

 

Current

Solutions

Compute(System)

Various – they range from simple storage capabilities mounted on the sensor, to simple display and storage, to limited object extraction.  Typical object extraction systems are currently small (1-20 node) GPU enhanced clusters.

Storage

Currently flat files persisted on disk in most cases.  Sometimes RDBMS indexes pointing to files or portions of files based on metadata/telemetry data.

Networking

Sensor comms tend to be Line of Sight or Satellite based.

Software

A wide range custom software and tools including traditional RDBM’s and display tools.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Sensors include airframe mounted and fixed position optical, IR, and SAR images.  

Volume (size)

FMV – 30-60 frames per/sec at full color 1080P resolution.

WALF – 1-10 frames per/sec at 10Kx10K full color resolution.

Velocity

(e.g. real time)

Real Time

Variety

(multiple datasets, mashup)

Data Typically exists in one or more standard imagery or video formats.

Variability (rate of change)

Little

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

The veracity of extracted objects is critical.  If the system fails or generates false positives people are put at risk.

Visualization

Visualization of extracted outputs will typically be as overlays on a geospatial display.  Overlay objects should be links back to the originating image/video segment.

Data Quality

Data quality is generally driven by a combination of sensor characteristics and weather (both obscuring factors - dust/moisture and stability factors – wind).

Data Types

Standard imagery and video formats are input.  Output should be in the form of OGC compliant web features or standard geospatial files (shape files, KML).

Data Analytics

1.     Object identification (type, size, color) and tracking.

2.     Pattern analysis of object (did the truck observed every weds afternoon take a different route today or is there a standard route this person takes every day).

3.     Crowd behavior/dynamics (is there a small group attempting to incite a riot.  Is this person out of place in the crowd or behaving differently.

4.     Economic activity

a.     is the line at the bread store, the butcher, or the ice cream store,

b.    are more trucks traveling north with goods than trucks going south

c.     Has activity at or the size of stores in this market place increased or decreased over the past year.

5.     Fusion of data with other data to improve quality and confidence.

 

Big Data Specific Challenges (Gaps)

Processing the volume of data in NRT to support alerting and situational awareness.

Big Data Specific Challenges in Mobility

Getting data from mobile sensor to processing

 

Security & Privacy

Requirements

Significant – sources and methods cannot be compromised the enemy should not be able to know what we see.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Typically this type of processing fits well into massively parallel computing such as provided by GPUs.  Typical problem is integration of this processing into a larger cluster capable of processing data from several sensors in parallel and in NRT.

 

Transmission of data from sensor to system is also a large challenge.

 

More Information (URLs)

Motion Imagery Standards - http://www.gwg.nga.mil/misb/

Some of many papers on object ident/tracking: http://www.dabi.temple.edu/~hbling/publication/SPIE12_Dismount_Formatted_v2_BW.pdf

http://csce.uark.edu/~jgauch/library/Tracking/Orten.2005.pdf

http://www.sciencedirect.com/science/article/pii/S0031320305004863

 

General Articles on the need: http://www.militaryaerospace.com/topics/m/video/79088650/persistent-surveillance-relies-on-extracting-relevant-data-points-and-connecting-the-dots.htm

 

http://www.defencetalk.com/wide-area-persistent-surveillance-revolutionizes-tactical-isr-45745/

 

http://www.defencetalk.com/wide-area-persistent-surveillance-revolutionizes-tactical-isr-45745/

 

 

 

 

 

 

 

 

Note: <additional comments>

       

Intelligence Data Processing and Analysis

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Intelligence Data Processing and Analysis

Vertical (area)

Defense (Intelligence)

Author/Company/Email

David Boyd/Data Tactics/ dboyd@data-tactics.com

Actors/Stakeholders and their roles and responsibilities

Senior Civilian/Military Leadership

Field Commanders

Intelligence Analysts

Warfighters

Goals

1.     Provide automated alerts to Analysts, Warfighters, Commanders, and Leadership based on incoming intelligence data.

2.     Allow Intelligence Analysts to identify in Intelligence data

a.     Relationships between entities (people, organizations, places, equipment)

b.    Trends in sentiment or intent for either general population or leadership group (state, non-state actors).

c.     Location of and possibly timing of hostile actions (including implantation of IEDs).

d.    Track the location and actions of (potentially) hostile actors

3.     Ability to reason against and derive knowledge from diverse, disconnected, and frequently unstructured (e.g. text) data sources.

4.     Ability to process data close to the point of collection and allow data to be shared easily to/from individual soldiers, forward deployed units, and senior leadership in garrison.

 

Use Case Description

1.     Ingest/accept data from a wide range of sensors and sources across intelligence disciplines (IMINT, MASINT, GEOINT, HUMINT, SIGINT, OSINT, etc.)

2.     Process, transform, or align date from disparate sources in disparate formats into a unified data space to permit:

a.     Search

b.    Reasoning

c.     Comparison

3.     Provide alerts to users of significant changes in the state of monitored entities or significant activity within an area.

4.     Provide connectivity to the edge for the Warfighter (in this case the edge would go as far as a single soldier on dismounted patrol)

 

 

Current

Solutions

Compute(System)

Fixed and deployed computing clusters ranging from 1000s of nodes to 10s of nodes.

Storage

10s of Terabytes to 100s of Petabytes for edge and fixed site clusters.  Dismounted soldiers would have at most 1-100s of Gigabytes (mostly single digit handheld data storage sizes).

Networking

Networking with-in and between in garrison fixed sites is robust.  Connectivity to forward edge is limited and often characterized by high latency and packet loss.  Remote comms might be Satellite based (high latency) or even limited to RF Line of sight radio.

Software

Currently baseline leverages:

1.     Hadoop

2.     Accumulo (Big Table)

3.     Solr

4.     NLP (several variants)

5.     Puppet (for deployment and security)

6.     Storm

7.     Custom applications and visualization tools

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Very distributed

Volume (size)

Some IMINT sensors can produce over a petabyte of data in the space of hours.    Other data is as small as infrequent sensor activations or text messages.

Velocity

(e.g. real time)

Much sensor data is real time (Full motion video, SIGINT) other is less real time.   The critical aspect is to be able ingest, process, and disseminate alerts in NRT.

Variety

(multiple datasets, mashup)

Everything from text files, raw media, imagery, video, audio, electronic data, human generated data.

Variability (rate of change)

While sensor interface formats tend to be stable, most other data is uncontrolled and may be in any format.  Much of the data is unstructured.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Data provenance (e.g. tracking of all transfers and transformations) must be tracked over the life of the data. 

Determining the veracity of “soft” data sources (generally human generated) is a critical requirement.

Visualization

Primary visualizations will be Geospatial overlays and network diagrams. Volume amounts might be millions of points on the map and thousands of nodes in the network diagram. 

Data Quality (syntax)

Data Quality for sensor generated data is generally known (image quality, sig/noise) and good. 

Unstructured or “captured” data quality varies significantly and frequently cannot be controlled.

 

Data Types

Imagery, Video, Text, Digital documents of all types, Audio, Digital signal data.

Data Analytics

1.     NRT Alerts based on patterns and baseline changes.

2.     Link Analysis

3.     Geospatial Analysis

4.     Text Analytics (sentiment, entity extraction, etc.)

Big Data Specific Challenges (Gaps)

1.     Big (or even moderate size data) over tactical networks

2.     Data currently exists in disparate silos which must be accessible through a semantically integrated data space.

3.     Most critical data is either unstructured or imagery/video which requires significant processing to extract entities and information.

Big Data Specific Challenges in Mobility

The  outputs of this analysis and information must be transmitted to or accessed by the dismounted forward soldier.

 

Security & Privacy

Requirements

 

Foremost.  Data must be protected against:

1.     Unauthorized access or disclosure

2.     Tampering

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Wide variety of data types, sources, structures, and quality which will span domains and requires integrated search and reasoning.

 

 

More Information (URLs)

http://www.afcea-aberdeen.org/files/presentations/AFCEAAberdeen_DCGSA_COLWells_PS.pdf

 

http://stids.c4i.gmu.edu/papers/STID...ghterIntel.pdf

 

http://stids.c4i.gmu.edu/STIDS2011/papers/STIDS2011_CR_T1_SalmenEtAl.pdf

 

http://www.youtube.com/watch?v=l4Qii7T8zeg

 

http://dcgsa.apg.army.mil/

Note: <additional comments>

       

Healthcare and Life Sciences

Electronic Medical Record (EMR) Data

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Electronic Medical Record (EMR) Data

Vertical (area)

Healthcare

Author/Company/Email

Shaun Grannis/Indiana University/ sgrannis@regenstrief.org

Actors/Stakeholders and their roles and responsibilities

Biomedical informatics research scientists (implement and evaluate enhanced methods for seamlessly integrating, standardizing, analyzing, and operationalizing highly heterogeneous, high-volume clinical data streams); Health services researchers (leverage integrated and standardized EMR data to derive knowledge that supports implementation and evaluation of translational, comparative effectiveness, patient-centered outcomes research); Healthcare providers – physicians, nurses, public health officials (leverage information and knowledge derived from integrated and standardized EMR data to support direct patient care and population health)

Goals

Use advanced methods for normalizing patient, provider, facility and clinical concept identification within and among separate health care organizations to enhance models for defining and extracting clinical phenotypes from non-standard discrete and free-text clinical data using feature selection, information retrieval and machine learning decision-models. Leverage clinical phenotype data to support cohort selection, clinical outcomes research, and clinical decision support.

Use Case Description

As health care systems increasingly gather and consume electronic medical record data, large national initiatives aiming to leverage such data are emerging, and include developing a digital learning health care system to support increasingly evidence-based clinical decisions with timely accurate and up-to-date patient-centered clinical information; using electronic observational clinical data to efficiently and rapidly translate scientific discoveries into effective clinical treatments; and electronically sharing integrated health data to improve healthcare process efficiency and outcomes. These key initiatives all rely on high-quality, large-scale, standardized and aggregate health data.  Despite the promise that increasingly prevalent and ubiquitous electronic medical record data hold, enhanced methods for integrating and rationalizing these data are needed for a variety of reasons. Data from clinical systems evolve over time. This is because the concept space in healthcare is constantly evolving: new scientific discoveries lead to new disease entities, new diagnostic modalities, and new disease management approaches. These in turn lead to new clinical concepts, which drives the evolution of health concept ontologies. Using heterogeneous data from the Indiana Network for Patient Care (INPC), the nation's largest and longest-running health information exchange, which includes more than 4 billion discrete coded clinical observations from more than 100 hospitals for more than 12 million patients, we will use information retrieval techniques to identify highly relevant clinical features from electronic observational data. We will deploy information retrieval and natural language processing techniques to extract clinical features. Validated features will be used to parameterize clinical phenotype decision models based on maximum likelihood estimators and Bayesian networks. Using these decision models we will identify a variety of clinical phenotypes such as diabetes, congestive heart failure, and pancreatic cancer.

 

Current

Solutions

Compute(System)

Big Red II, a new Cray supercomputer at I.U.

Storage

Teradata, PostgreSQL, MongoDB

Networking

Various. Significant I/O intensive processing needed.

Software

Hadoop, Hive, R. Unix-based.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Clinical data from more than 1,100 discrete logical, operational healthcare sources in the Indiana Network for Patient Care (INPC) the nation's largest and longest-running health information exchange.

Volume (size)

More than 12 million patients, more than 4 billion discrete clinical observations. > 20 TB raw data.

Velocity

(e.g. real time)

Between 500,000 and 1.5 million new real-time clinical transactions added per day.

Variety

(multiple datasets, mashup)

We integrate a broad variety of clinical datasets from multiple sources: free text provider notes; inpatient, outpatient, laboratory, and emergency department encounters; chromosome and molecular pathology; chemistry studies; cardiology studies; hematology studies; microbiology studies; neurology studies; provider notes; referral labs; serology studies; surgical pathology and cytology, blood bank, and toxicology studies.

Variability (rate of change)

Data from clinical systems evolve over time because the clinical and biological concept space is constantly evolving: new scientific discoveries lead to new disease entities, new diagnostic modalities, and new disease management approaches. These in turn lead to new clinical concepts, which drive the evolution of health concept ontologies, encoded in highly variable fashion.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Data from each clinical source are commonly gathered using different methods and representations, yielding substantial heterogeneity. This leads to systematic errors and bias requiring robust methods for creating semantic interoperability.

Visualization

Inbound data volume, accuracy, and completeness must be monitored on a routine basis using focus visualization methods. Intrinsic informational characteristics of data sources must be visualized to identify unexpected trends.

Data Quality (syntax)

A central barrier to leveraging electronic medical record data is the highly variable and unique local names and codes for the same clinical test or measurement performed at different institutions. When integrating many data sources, mapping local terms to a common standardized concept using a combination of probabilistic and heuristic classification methods is necessary.

Data Types

Wide variety of clinical data types including numeric, structured numeric, free-text, structured text, discrete nominal, discrete ordinal, discrete structured, binary large blobs (images and video).

Data Analytics

Information retrieval methods to identify relevant clinical features (tf-idf, latent semantic analysis, mutual information). Natural Language Processing techniques to extract relevant clinical features. Validated features will be used to parameterize clinical phenotype decision models based on maximum likelihood estimators and Bayesian networks. Decision models will be used to identify a variety of clinical phenotypes such as diabetes, congestive heart failure, and pancreatic cancer.

Big Data Specific Challenges (Gaps)

Overcoming the systematic errors and bias in large-scale, heterogeneous clinical data to support decision-making in research, patient care, and administrative use-cases requires complex multistage processing and analytics that demands substantial computing power. Further, the optimal techniques for accurately and effectively deriving knowledge from observational clinical data are nascent.

Big Data Specific Challenges in Mobility

 Biological and clinical data are needed in a variety of contexts throughout the healthcare ecosystem.  Effectively delivering clinical data and knowledge across the healthcare ecosystem will be facilitated by mobile platform such as mHealth.

Security & Privacy

Requirements

Privacy and confidentiality of individuals must be preserved in compliance with federal and state requirements including HIPAA. Developing analytic models using comprehensive, integrated clinical data requires aggregation and subsequent de-identification prior to applying complex analytics.

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Patients increasingly receive health care in a variety of clinical settings. The subsequent EMR data is fragmented and heterogeneous. In order to realize the promise of a Learning Health Care system as advocated by the National Academy of Science and the Institute of Medicine, EMR data must be rationalized and integrated. The methods we propose in this use-case support integrating and rationalizing clinical data to support decision-making at multiple levels.

More Information (URLs)

Regenstrief Institute (http://www.regenstrief.org); Logical observation identifiers names and codes (http://www.loinc.org); Indiana Health Information Exchange (http://www.ihie.org); Institute of Medicine Learning Healthcare System (http://www.iom.edu/Activities/Qualit...ealthcare.aspx)

 

Note: <additional comments>

       

Pathology Imaging/digital pathology

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Pathology Imaging/digital pathology

Vertical (area)

Healthcare

Author/Company/Email

Fusheng Wang/Emory University/ fusheng.wang@emory.edu

Actors/Stakeholders and their roles and responsibilities

Biomedical researchers on translational research; hospital clinicians on imaging guided diagnosis

Goals

Develop high performance image analysis algorithms to extract spatial information from images; provide efficient spatial queries and analytics, and feature clustering and classification

Use Case Description

Digital pathology imaging is an emerging field where examination of high resolution images of tissue specimens enables novel and more effective ways for disease diagnosis. Pathology image analysis segments massive (millions per image) spatial objects such as nuclei and blood vessels, represented with their boundaries, along with many extracted image features from these objects. The derived information is used for many complex queries and analytics to support biomedical research and clinical diagnosis. Recently, 3D pathology imaging is made possible through 3D laser technologies or serially sectioning hundreds of tissue sections onto slides and scanning them into digital images. Segmenting 3D microanatomic objects from registered serial images could produce tens of millions of 3D objects from a single image. This provides a deep “map” of human tissues for next generation diagnosis.

Current

Solutions

Compute(System)

Supercomputers; Cloud

Storage

SAN or HDFS

Networking

Need excellent external network link

Software

MPI for image analysis; MapReduce + Hive with spatial extension

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Digitized pathology images from human tissues

Volume (size)

1GB raw image data + 1.5GB analytical results per 2D image; 1TB raw image data + 1TB analytical results per 3D image. 1PB data per moderated hospital per year

Velocity

(e.g. real time)

Once generated, data will not be changed

Variety

(multiple datasets, mashup)

Image characteristics and analytics depend on disease types

Variability (rate of change)

No change

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

High quality results validated with human annotations are essential

Visualization

Needed for validation and training

Data Quality

Depend on pre-processing of tissue slides such as chemical staining and quality of image analysis algorithms

Data Types

Raw images are whole slide images (mostly based on BIGTIFF), and analytical results are structured data (spatial boundaries and features)

Data Analytics

Image analysis, spatial queries and analytics, feature clustering and classification

Big Data Specific Challenges (Gaps)

Extreme large size; multi-dimensional; disease specific analytics; correlation with other data types (clinical data, -omic data)

Big Data Specific Challenges in Mobility

3D visualization of 3D pathology images is not likely in mobile platforms

 

Security & Privacy

Requirements

Protected health information has to be protected; public data have to be de-identified

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Imaging data; multi-dimensional spatial data analytics

 

 

More Information (URLs)

https://web.cci.emory.edu/confluence/display/PAIS

https://web.cci.emory.edu/confluence/display/HadoopGIS

Note: <additional comments>

       


Figure 1: Examples of 2-D and 3-D pathology images

Figure 1 Examples of 2-D and 3-D pathology images.png

 

Figure 2: Architecture of Hadoop-GIS, a spatial data warehousing system over MapReduce to support spatial analytics for analytical pathology imaging

Figure 2 Architecture of Hadoop-GIS, a spatial data warehousing system over MapReduce to support spatial analytics for analytical pathology imaging.png

Computational Bioimaging

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Computational Bioimaging

Vertical (area)

Scientific Research: Biological Science

Author/Company/Email

David Skinner1, deskinner@lbl.gov

Joaquin Correa1, JoaquinCorrea@lbl.gov

Daniela Ushizima2, dushizima@lbl.gov

Joerg Meyer2, joergmeyer@lbl.gov

 

1National Energy Scientific Computing Center (NERSC), Lawrence Berkeley National Laboratory, USA

2Computational Research Division, Lawrence Berkeley National Laboratory, USA

Actors/Stakeholders and their roles and responsibilities

Capability providers: Bioimaging instrument operators, microscope developers, imaging facilities, applied mathematicians, and data stewards.

User Community: DOE, industry and academic researchers seeking to collaboratively build models from imaging data.

Goals

Data delivered from bioimaging is increasingly automated, higher resolution, and multi-modal. This has created a data analysis bottleneck that, if resolved, can advance the biosciences discovery through Big Data techniques. Our goal is to solve that bottleneck with extreme scale computing.

 

Meeting that goal will require more than computing. It will require building communities around data resources and providing advanced algorithms for massive image analysis. High-performance computational solutions can be harnessed by community-focused science gateways to guide the application of massive data analysis toward massive imaging data sets. Workflow components include data acquisition, storage, enhancement, minimizing noise, segmentation of regions of interest, crowd-based selection and extraction of features, and object classification, and organization, and search.

Use Case Description

Web-based one-stop-shop for high performance, high throughput image processing for producers and consumers of models built on bio-imaging data.

Current

Solutions

Compute(System)

Hopper.nersc.gov (150K cores)

Storage

Database and image collections

Networking

10Gb, could use 100Gb and advanced networking (SDN)

Software

ImageJ, OMERO, VolRover, advanced segmentation and feature detection methods from applied math researchers

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Distributed experimental sources of bioimages (instruments). Scheduled high volume flows from automated high-resolution optical and electron microscopes.

Volume (size)

Growing very fast. Scalable key-value and object store databases needed. In-database processing and analytics. 50TB here now, but currently over a petabyte overall. A single scan on emerging machines is 32TB

Velocity

(e.g. real time)

High-throughput computing (HTC), responsive analysis

Variety

(multiple datasets, mashup)

Multi-modal imaging essentially must mash-up disparate channels of data with attention to registration and dataset formats. 

Variability (rate of change)

Biological samples are highly variable and their analysis workflows must cope with wide variation.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Data is messy overall as is training classifiers.  

Visualization

Heavy use of 3D structural models. 

Data Quality (syntax)

 

Data Types

Imaging file formats

Data Analytics

Machine learning (SVM and RF) for classification and recommendation services.

Big Data Specific Challenges (Gaps)

HTC at scale for simulation science. Flexible data methods at scale for messy data. Machine learning and knowledge systems that drive pixel based data toward biological objects and models. 

Big Data Specific Challenges in Mobility

 

Security & Privacy

Requirements

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

 

There is potential in generalizing concepts of search in the context of bioimaging.

 

More Information (URLs)

 

 

Note: <additional comments>

       

Genomic Measurements

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Genomic Measurements

Vertical (area)

Healthcare

Author/Company/Email

Justin Zook/NIST/ jzook@nist.gov

Actors/Stakeholders and their roles and responsibilities

NIST/Genome in a Bottle Consortium – public/private/academic partnership

Goals

Develop well-characterized Reference Materials, Reference Data, and Reference Methods needed to assess performance of genome sequencing

 

Use Case Description

Integrate data from multiple sequencing technologies and methods to develop highly confident characterization of whole human genomes as Reference Materials, and develop methods to use these Reference Materials to assess performance of any genome sequencing run

 

 

Current

Solutions

Compute(System)

72-core cluster for our NIST group, collaboration with >1000 core clusters at FDA, some groups are using cloud

Storage

~40TB NFS at NIST, PBs of genomics data at NIH/NCBI

Networking

Varies. Significant I/O intensive processing needed

Software

Open-source sequencing bioinformatics software from academic groups (UNIX-based)

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Sequencers are distributed across many laboratories, though some core facilities exist.

Volume (size)

40TB NFS is full, will need >100TB in 1-2 years at NIST; Healthcare community will need many PBs of storage

Velocity

(e.g. real time)

DNA sequencers can generate ~300GB compressed data/day.  Velocity has increased much faster than Moore’s Law

Variety

(multiple datasets, mashup)

File formats not well-standardized, though some standards exist. Generally structured data.

Variability (rate of change)

Sequencing technologies have evolved very rapidly, and new technologies are on the horizon.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

All sequencing technologies have significant systematic errors and biases, which require complex analysis methods and combining multiple technologies to understand, often with machine learning

Visualization

“Genome browsers” have been developed to visualize processed data

Data Quality

Sequencing technologies and bioinformatics methods have significant systematic errors and biases

Data Types

Mainly structured text

Data Analytics

Processing of raw data to produce variant calls. Also, clinical interpretation of variants, which is now very challenging.

Big Data Specific Challenges (Gaps)

Processing data requires significant computing power, which poses challenges especially to clinical laboratories as they are starting to perform large-scale sequencing.  Long-term storage of clinical sequencing data could be expensive. Analysis methods are quickly evolving.  Many parts of the genome are challenging to analyze, and systematic errors are difficult to characterize.

Big Data Specific Challenges in Mobility

Physicians may need access to genomic data on mobile platforms

Security & Privacy

Requirements

Sequencing data in health records or clinical research databases must be kept secure/private, though our Consortium data is public.

Highlight issues for generalizing this use case (e.g. for ref. architecture)

I have some generalizations to medical genome sequencing above, but focus on NIST/Genome in a Bottle Consortium work.  Currently, labs doing sequencing range from small to very large.  Future data could include other ‘omics’ measurements, which could be even larger than DNA sequencing

More Information (URLs)

Genome in a Bottle Consortium: www.genomeinabottle.org

 

Note: <additional comments>

       

Comparative analysis for metagenomes and genomes

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Comparative analysis for metagenomes and genomes

Vertical (area)

Scientific Research: Genomics

Author/Company/Email

Ernest Szeto / LBNL / eszeto@lbl.gov

Actors/Stakeholders and their roles and responsibilities

Joint Genome Institute (JGI) Integrated Microbial Genomes (IMG) project. Heads: Victor M. Markowitz, and Nikos C. Kyrpides. User community: JGI, bioinformaticians and biologists worldwide.

Goals

Provide an integrated comparative analysis system for metagenomes and genomes.  This includes interactive Web UI with core data, backend precomputations, batch job computation submission from the UI.

 

Use Case Description

Given a metagenomic sample, (1) determine the community composition in terms of other reference isolate genomes, (2) characterize the function of its genes, (3) begin to infer possible functional pathways, (4) characterize similarity or dissimilarity with other metagenomic samples, (5) begin to characterize changes in community composition and function due to changes in environmental pressures, (6) isolate sub-sections of data based on quality measures and community composition.

Current

Solutions

Compute(System)

Linux cluster, Oracle RDBMS server, large memory machines, standard Linux interactive hosts

Storage

Oracle RDBMS, SQLite files, flat text files, Lucy (a version of Lucene) for keyword searches, BLAST databases, USEARCH databases

Networking

Provided by NERSC

Software

Standard bioinformatics tools (BLAST, HMMER, multiple alignment and phylogenetic tools, gene callers, sequence feature predictors…), Perl/Python wrapper scripts, Linux Cluster scheduling

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Centralized.

Volume (size)

 50tb

Velocity

(e.g. real time)

Front end web UI must be real time interactive. Back end data loading processing must keep up with exponential growth of sequence data due to the rapid drop in cost of sequencing technology.

Variety

(multiple datasets, mashup)

Biological data is inherently heterogeneous, complex, structural, and hierarchical. One begins with sequences, followed by features on sequences, such as genes, motifs, regulatory regions, followed by organization of genes in neighborhoods (operons), to proteins and their structural features, to coordination and expression of genes in pathways.  Besides core genomic data, new types of “Omics” data such as transcriptomics, methylomics, and proteomics describing gene expression under a variety of conditions must be incorporated into the comparative analysis system.

Variability (rate of change)

The sizes of metagenomic samples can vary by several orders of magnitude, such as several hundred thousand genes to a billion genes (e.g., latter in a complex soil sample).

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Metagenomic sampling science is currently preliminary and exploratory.  Procedures for evaluating assembly of highly fragmented data in raw reads is better defined, but still an open research area.

Visualization

Interactive speed of web UI  on very large data sets is an ongoing challenge.  Web UI’s still seem to be the preferred interface for most biologists.  It is use for basic querying and browsing of data.  More specialized tools may be launched from them, e.g. for viewing multiple alignments.  Ability to download large amounts of data for offline analysis is another requirement of the system.

Data Quality

Improving quality of metagenomic assembly is still a fundamental challenge.  Improving the quality of reference isolate genomes, both in terms of the coverage in the phylogenetic tree, improved gene calling and functional annotation is a more mature process, but an ongoing project.

Data Types

Cf. above on “Variety”

Data Analytics

Descriptive statistics, statistical significance in hypothesis testing, discovering new relationships, data clustering and classification is a standard part of the analytics.  The less quantitative part includes the ability to visualize structural details at different levels of resolution.  Data reduction, removing redundancies through clustering, more abstract representations such as representing a group of highly similar genomes in a pangenome are all strategies for both data management as well as analytics.

Big Data Specific Challenges (Gaps)

The biggest friend for dealing with  the heterogeneity of biological data is still the relational database management system (RDBMS).  Unfortunately, it does not scale for the current volume of data.   NoSQL solutions aim at providing an alternative.  Unfortunately, NoSQL solutions do not always lend themselves to real time interactive use, rapid and parallel bulk loading, and sometimes have issues regarding robustness.  Our current approach is currently ad hoc, custom, relying mainly on the Linux cluster and the file system to supplement the Oracle RDBMS.  The custom solution oftentimes rely in knowledge of the peculiarities of the data allowing us to devise horizontal partitioning schemes as well as inversion of data organization when applicable.

Big Data Specific Challenges in Mobility

No special challenges.  Just world wide web access.

 

Security & Privacy

Requirements

No special challenges.  Data is either public or requires standard login with password.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

A replacement for the RDBMS in big data would be of benefit to everyone. Many NoSQL solutions attempt to fill this role, but have their limitations.

 

 

More Information (URLs)

http://img.jgi.doe.gov

 

 

 

Note: <additional comments>

       

Individualized Diabetes Management

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Individualized Diabetes Management

Vertical (area)

Healthcare

Author/Company/Email

Peter Li, Ying Ding, Philip Yu, Geoffrey Fox, David Wild at Mayo Clinic, Indiana University, UIC; dingying@indiana.edu

Actors/Stakeholders and their roles and responsibilities

Mayo Clinic + IU/semantic integration of EHR data

UIC/semantic graph mining of EHR data

IU cloud and parallel computing

Goals

Develop advanced graph-based data mining techniques applied to EHR to search for these cohorts and extract their EHR data for outcome evaluation. These methods will push the boundaries of scalability and data mining technologies and advance knowledge and practice in these areas as well as clinical management of complex diseases.

Use Case Description

Diabetes is a growing illness in world population, affecting both developing and developed countries. Current management strategies do not adequately take into account of individual patient profiles, such as co-morbidities and medications, which are common in patients with chronic illnesses. We propose to approach this shortcoming by identifying similar patients from a large Electronic Health Record (EHR) database, i.e. an individualized cohort, and evaluate their respective management outcomes to formulate one best solution suited for a given patient with diabetes.

Project under development as below

 

Stage 1: Use the Semantic Linking for Property Values method to convert an existing data warehouse at Mayo Clinic, called the Enterprise Data Trust (EDT), into RDF triples that enables us to find similar patients much more efficiently through linking of both vocabulary-based and continuous values,

Stage 2: Needs efficient parallel retrieval algorithms, suitable for cloud or HPC, using open source Hbase with both indexed and custom search to identify patients of possible interest.

Stage 3: The EHR, as an RDF graph, provides a very rich environment for graph pattern mining. Needs new distributed graph mining algorithms to perform pattern analysis and graph indexing technique for pattern searching on RDF triple graphs.

Stage 4: Given the size and complexity of graphs, mining subgraph patterns could generate numerous false positives and miss numerous false negatives. Needs robust statistical analysis tools to manage false discovery rate and determine true subgraph significance and validate these through several clinical use cases.

Current

Solutions

Compute(System)

supercomputers; cloud

Storage

HDFS

Networking

Varies. Significant I/O intensive processing needed

Software

Mayo internal data warehouse called Enterprise Data Trust (EDT)

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

distributed EHR data

Volume (size)

The Mayo Clinic EHR dataset is a very large dataset containing over 5 million patients with thousands of properties each and many more that are derived from primary values.

Velocity

(e.g. real time)

not real-time but updated periodically

Variety

(multiple datasets, mashup)

Structured data, a patient has controlled vocabulary (CV) property values (demographics, diagnostic codes, medications, procedures, etc.) and continuous property values (lab tests, medication amounts, vitals, etc.). The number of property values could range from less than 100 (new patient) to more than 100,000 (long term patient) with typical patients composed of 100 CV values and 1000 continuous values. Most values are time based, i.e. a timestamp is recorded with the value at the time of observation.

Variability (rate of change)

Data will be updated or added during each patient visit.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Data are annotated based on domain ontologies or taxonomies. Semantics of data can vary from labs to labs.

Visualization

no visualization

Data Quality

Provenance is important to trace the origins of the data and data quality

Data Types

text, and Continuous Numerical values

Data Analytics

Integrating data into semantic graph, using graph traverse to replace SQL join. Developing semantic graph mining algorithms to identify graph patterns, index graph, and search graph. Indexed Hbase. Custom code to develop new patient properties from stored data.

Big Data Specific Challenges (Gaps)

For individualized cohort, we will effectively be building a datamart for each patient since the critical properties and indices will be specific to each patient. Due to the number of patients, this becomes an impractical approach. Fundamentally, the paradigm changes from relational row-column lookup to semantic graph traversal.

Big Data Specific Challenges in Mobility

Physicians and patient may need access to this data on mobile platforms

Security & Privacy

Requirements

Health records or clinical research databases must be kept secure/private.

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Data integration: continuous values, ontological annotation, taxonomy

Graph Search: indexing and searching graph

Validation: Statistical validation

More Information (URLs)

 

 

Note: <additional comments>

       

Statistical Relational Artificial Intelligence for Health Care

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Statistical Relational AI for Health Care

Vertical (area)

Healthcare

Author/Company/Email

Sriraam Natarajan / Indiana University / natarasr@indiana.edu

Actors/Stakeholders and their roles and responsibilities

Researchers in Informatics, medicine and practitioners in medicine.

Goals

The goal of the project is to analyze large, multi-modal, longitudinal data. Analyzing different data types such as imaging, EHR, genetic and natural language data requires a rich representation. This approach employs the relational probabilistic models that have the capability of handling rich relational data and modeling uncertainty using probability theory. The software learns models from multiple data types and can possibly integrate the information and reason about complex queries.

 

Use Case Description

Users can provide a set of descriptions – say for instance, MRI images and demographic data about a particular subject. They can then query for the onset of a particular disease (say Alzheimer’s) and the system will then provide a probability distribution over the possible occurrence of this disease.

 

Current

Solutions

Compute(System)

A high performance computer (48 GB RAM) is needed to run the code for a few hundred patients. Clusters for large datasets

Storage

A 200 GB – 1 TB hard drive typically stores the test data. The relevant data is retrieved to main memory to run the algorithms. Backend data in database or NoSQL stores

Networking

Intranet.

Software

Mainly Java based, in house tools are used to process the data.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

All the data about the users reside in a single disk file. Sometimes, resources such as published text need to be pulled from internet. 

Volume (size)

Variable due to the different amount of data collected. Typically can be in 100s of GBs for a single cohort of a few hundred people. When dealing with millions of patients, this can be in the order of 1 petabyte.

Velocity

(e.g. real time)

Varied. In some cases, EHRs are constantly being updated. In other controlled studies, the data often comes in batches in regular intervals.

Variety

(multiple datasets, mashup)

This is the key property in medical data sets. That data is typically in multiple tables and need to be merged in order to perform the analysis.

Variability (rate of change)

The arrival of data is unpredictable in many cases as they arrive in real-time.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Challenging due to different modalities of the data, human errors in data collection and validation.

Visualization

The visualization of the entire input data is nearly impossible. But typically, partially visualizable. The models built can be visualized under some reasonable assumptions.

Data Quality (syntax)

 

Data Types

EHRs, imaging, genetic data that are stored in multiple databases.

Data Analytics

 

Big Data Specific Challenges (Gaps)

Data is in abundance in many cases of medicine. The key issue is that there can possibly be too much data (as images, genetic sequences etc) that can make the analysis complicated. The real challenge lies in aligning the data and merging from multiple sources in a form that can be made useful for a combined analysis. The other issue is that sometimes, large amount of data is available about a single subject but the number of subjects themselves is not very high (i.e., data imbalance). This can result in learning algorithms picking up random correlations between the multiple data types as important features in analysis. Hence, robust learning methods that can faithfully model the data are of paramount importance. Another aspect of data imbalance is the occurrence of positive examples (i.e., cases). The incidence of certain diseases may be rare making the ratio of cases to controls extremely skewed making it possible for the learning algorithms to model noise instead of examples.

Big Data Specific Challenges in Mobility

 

 

Security & Privacy

Requirements

Secure handling and processing of data is of crucial importance in medical domains.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Models learned from one set of populations cannot be easily generalized across other populations with diverse characteristics. This requires that the learned models can be generalized and refined according to the change in the population characteristics.

 

 

More Information (URLs)

 

 

 

Note: <additional comments>

       

World Population Scale Epidemiological Study

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

World Population Scale Epidemiological Study

Vertical (area)

Epidemiology, Simulation Social Science, Computational Social Science

Author/Company/Email

Madhav Marathe Stephen Eubank or Chris Barrett/ Virginia Bioinformatics Institute, Virginia Tech, mmarathe@vbi.vt.edu, seubank@vbi.vt.edu or cbarrett@vbi.vt.edu

Actors/Stakeholders and their roles and responsibilities

Government and non-profit institutions involved in health, public policy, and disaster mitigation.  Social Scientist who wants to study the interplay between behavior and contagion.

Goals

(a) Build a synthetic global population. (b) Run simulations over the global population to    reason about outbreaks and various intervention strategies.

 

Use Case Description

Prediction and control of pandemic similar to the 2009 H1N1 influenza.

 

 

Current

Solutions

Compute(System)

Distributed (MPI) based simulation system written in Charm++. Parallelism is achieved by exploiting the disease residence time period. 

Storage

Network file system. Exploring database driven techniques.

Networking

Infiniband. High bandwidth 3D Torus.

Software

Charm++, MPI

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Generated from synthetic population generator. Currently centralized. However, could be made distributed as part of post-processing. 

Volume (size)

100TB

Velocity

(e.g. real time)

Interactions with experts and visualization routines generate large amount of real time data. Data feeding into the simulation is small but data generated by simulation is massive.

Variety

(multiple datasets, mashup)

Variety depends upon the complexity of the model over which the simulation is being performed.  Can be very complex if other aspects of the world population such as type of activity, geographical, socio-economic, cultural variations are taken into account.

Variability (rate of change)

Depends upon the evolution of the model and corresponding changes in the code. This is complex and time intensive. Hence low rate of change.

 

 

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Robustness of the simulation is dependent upon the quality of the model. However, robustness of the computation itself, although non-trivial, is tractable.

Visualization

Would require very large amount of movement of data to enable visualization.

Data Quality (syntax)

Consistent due to generation from a model

Data Types

Primarily network data.

Data Analytics

Summary of various runs and replicates of a simulation

Big Data Specific Challenges (Gaps)

Computation of the simulation is both compute intensive and data intensive.  Moreover, due to unstructured and irregular nature of graph processing the problem is not easily decomposable. Therefore it is also bandwidth intensive. Hence, a supercomputer is applicable than cloud type clusters.

Big Data Specific Challenges in Mobility

None

 

Security & Privacy

Requirements

Several issues at the synthetic population-modeling phase (see social contagion model).

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

In general contagion diffusion of various kinds: information, diseases, social unrest can be modeled and computed. All of them are agent-based model that utilize the underlying interaction network to study the evolution of the desired phenomena.

 

More Information (URLs)

 

 

 

Note: <additional comments>

       

Social Contagion Modeling  for Planning, Public Health and Disaster Management

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Social Contagion Modeling

Vertical (area)

Social behavior (including national security, public health, viral marketing, city planning, disaster preparedness)

Author/Company/Email

Madhav Marathe or Chris Kuhlman /Virginia Bioinformatics Institute, Virginia Tech  mmarathe@vbi.vt.edu  or ckuhlman@vbi.vt.edu

/Actors/Stakeholders and their roles and responsibilities

 

Goals

Provide a computing infrastructure that models social contagion processes.

The infrastructure enables different types of human-to-human interactions (e.g., face-to-face versus online media; mother-daughter relationships versus mother-coworker relationships) to be simulated.  It takes not only human-to-human interactions into account, but also interactions among people, services (e.g., transportation), and infrastructure (e.g., internet, electric power).

Use Case Description

Social unrest.  People take to the streets to voice unhappiness with government leadership.  There are citizens that both support and oppose government. Quantify the degrees to which normal business and activities are disrupted owing to fear and anger.  Quantify the possibility of peaceful demonstrations, violent protests.  Quantify the potential for government responses ranging from appeasement, to allowing protests, to issuing threats against protestors, to actions to thwart protests.  To address these issues, must have fine-resolution models and datasets.

Current

Solutions

Compute(System)

Distributed processing software running on commodity clusters and newer architectures and systems (e.g., clouds).

Storage

File servers (including archives), databases.

Networking

Ethernet, Infiniband, and similar.

Software

Specialized simulators, open source software, and proprietary modeling environments. Databases.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Many data sources:  populations, work locations, travel patterns, utilities (e.g., power grid) and other man-made infrastructures, online (social) media. 

Volume (size)

Easily 10s of TB per year of new data.

Velocity

(e.g. real time)

During social unrest events, human interactions and mobility key to understanding system dynamics.  Rapid changes in data; e.g., who follows whom in Twitter.

Variety

(multiple datasets, mashup)

Variety of data seen in wide range of data sources.  Temporal data.  Data fusion.

 

 

Data fusion a big issue.  How to combine data from different sources and how to deal with missing or incomplete data?  Multiple simultaneous contagion processes.

Variability (rate of change)

Because of stochastic nature of events, multiple instances of models and inputs must be run to ranges in outcomes.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Failover of soft realtime analyses.

Visualization

Large datasets; time evolution; multiple contagion processes over multiple network representations.  Levels of detail (e.g., individual, neighborhood, city, state, country-level).

Data Quality (syntax)

Checks for ensuring data consistency, corruption.  Preprocessing of raw data for use in models.

Data Types

Wide-ranging data, from human characteristics to utilities and transportation systems, and interactions among them.

Data Analytics

Models of behavior of humans and hard infrastructures, and their interactions.  Visualization of results.

Big Data Specific Challenges (Gaps)

How to take into account heterogeneous features of 100s of millions or billions of individuals, models of cultural variations across countries that are assigned to individual agents? How to validate these large models?  Different types of models (e.g., multiple contagions):  disease, emotions, behaviors.  Modeling of different urban infrastructure systems in which humans act.  With multiple replicates required to assess stochasticity, large amounts of output data are produced; storage requirements.

Big Data Specific Challenges in Mobility

How and where to perform these computations?  Combinations of cloud computing and clusters.  How to realize most efficient computations; move data to compute resources? 

Security & Privacy

Requirements

Two dimensions.  First, privacy and anonymity issues for individuals used in modeling (e.g., Twitter and Facebook users).  Second, securing data and computing platforms for computation.

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Fusion of different data types. Different datasets must be combined depending on the particular problem.  How to quickly develop, verify, and validate new models for new applications.  What is appropriate level of granularity to capture phenomena of interest while generating results sufficiently quickly; i.e., how to achieve a scalable solution.  Data visualization and extraction at different levels of granularity.

More Information (URLs)

 

 

 

Note: <additional comments>

       

Biodiversity and LifeWatch

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

                  Use Case Title

LifeWatch – E-Science European Infrastructure for Biodiversity and Ecosystem Research

Vertical (area)

Scientific Research: Life Science

Author/Company/Email

Wouter Los, Yuri Demchenko (y.demchenko@uva.nl), University of Amsterdam

Actors/Stakeholders and their roles and responsibilities

End-users (biologists, ecologists, field researchers)

Data analysts, data archive managers, e-Science Infrastructure managers, EU states national representatives

Goals

Research and monitor different ecosystems, biological species, their dynamics and migration.

 

Use Case Description

LifeWatch project and initiative intends to provide integrated access to a variety of data, analytical and modeling tools as served by a variety of collaborating initiatives. Another service is offered with data and tools in selected workflows for specific scientific communities. In addition, LifeWatch will provide opportunities to construct personalized ‘virtual labs', also allowing to enter new data and analytical tools.

New data will be shared with the data facilities cooperating with LifeWatch.

Particular case studies: Monitoring alien species, monitoring migrating birds, wetlands

LifeWatch operates Global Biodiversity Information facility and Biodiversity Catalogue that is Biodiversity Science Web Services Catalogue

Current

Solutions

Compute(System)

Field facilities TBD

Datacenter: General Grid and cloud based resources provided by national e-Science centers

Storage

Distributed, historical and trends data archiving

Networking

May require special dedicated or overlay sensor network.

Software

Web Services based, Grid based services, relational databases

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Ecological information from numerous observation and monitoring facilities and sensor network, satellite images/information, climate and weather, all recorded information.

Information from field researchers

Volume (size)

Involves many existing data sets/sources

Collected amount of data TBD

Velocity

(e.g. real time)

Data analysed incrementally, processes dynamics corresponds to dynamics of biological and ecological processes.

However may require real time processing and analysis in case of the natural or industrial disaster.

May require data streaming processing.

Variety

(multiple datasets, mashup)

Variety and number of involved databases and observation data is currently limited by available tools; in principle, unlimited with the growing ability to process data for identifying ecological changes, factors/reasons, species evolution and trends.

See below in additional information.

Variability (rate of change)

Structure of the datasets and models may change depending on the data processing stage and tasks

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

In normal monitoring mode are data are statistically processed to achieve robustness.

Some biodiversity research are critical to data veracity (reliability/trustworthiness).

In case of natural and technogenic disasters data veracity is critical.

Visualization

Requires advanced and rich visualization, high definition visualisation facilities, visualisation data

·  4D visualization

·  Visualizing effects of parameter change in (computational) models

·  Comparing model outcomes with actual observations (multi dimensional)

 

Data Quality

Depends on and ensued by initial observation data.

Quality of analytical data depends on used mode and algorithms that are constantly improved.

Repeating data analytics should be possible to re-evaluate initial observation data.

Actionable data are human aided.

Data Types

Multi-type.

Relational data, key-value, complex semantically rich data

Data Analytics

Parallel data streams and streaming analytics

Big Data Specific Challenges (Gaps)

Variety, multi-type data: SQL and no-SQL, distributed multi-source data.

Visualisation, distributed sensor networks.

Data storage and archiving, data exchange and integration; data linkage: from the initial observation data to processed data and reported/visualised data.

·  Historical unique data

·  Curated (authorized) reference data (i.e. species names lists), algorithms, software code, workflows

·  Processed (secondary) data serving as input for other researchers

·  Provenance (and persistent identification (PID)) control of data, algorithms, and workflows

 

Big Data Specific Challenges in Mobility

Require supporting mobile sensors (e.g. birds migration) and mobile researchers (both for information feed and catalogue search)

·  Instrumented field vehicles, Ships, Planes, Submarines, floating buoys, sensor tagging on organisms

·  Photos, video, sound recording

 

Security & Privacy

Requirements

Data integrity, referral integrity of the datasets.

Federated identity management for mobile researchers and mobile sensors

Confidentiality, access control and accounting for information on protected species, ecological information, space images, climate information.

Highlight issues for generalizing this use case (e.g. for ref. architecture)

·         Support of distributed sensor network

·         Multi-type data combination and linkage; potentially unlimited data variety

·         Data lifecycle management: data provenance, referral integrity and identification

·         Access and integration of multiple distributed databases

 

More Information (URLs)

http://www.lifewatch.eu/web/guest/home

https://www.biodiversitycatalogue.org/

 

 

Note: <additional comments>

Variety of data used in Biodiversity research

Genetic (genomic) diversity

-          DNA sequences & barcodes

-          Metabolomics functions

Species information

-          -species names

-          occurrence data (in time and place)

-          species traits and life history data

-          host-parasite relations

-          collection specimen data

Ecological information

-          biomass, trunk/root diameter and other physical characteristics

-          population density etc.

-          habitat structures

-          C/N/P etc molecular cycles

Ecosystem data

-          species composition and community dynamics

-          remote and earth observation data

-          CO2 fluxes

-          Soil characteristics

-          Algal blooming

-          Marine temperature, salinity, pH, currents, etc.

Ecosystem services

-          productivity (i.e biomass production/time)

-          fresh water dynamics

-          erosion

-          climate buffering

-          genetic pools

Data concepts

-          conceptual framework of each data

-          ontologies

-          provenance data

Algorithms and workflows

-          software code & provenance

-          tested workflows

 

Multiple sources of data and information

·         Specimen collection data

·         Observations (human interpretations)

·         Sensors and sensor networks (terrestrial, marine, soil organisms), bird etc tagging

·         Aerial & satellite observation spectra

·         Field * Laboratory experimentation

·         Radar & LiDAR

·         Fisheries & agricultural data

·         Deceases and epidemics

 

       

Deep Learning and Social Media

Large-scale Deep Learning

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Large-scale Deep Learning

Vertical (area)

Machine Learning/AI

Author/Company/Email

Adam Coates / Stanford University / acoates@cs.stanford.edu

Actors/Stakeholders and their roles and responsibilities

Machine learning researchers and practitioners faced with large quantities of data and complex prediction tasks.  Supports state-of-the-art development in computer vision as in automatic car driving, speech recognition, and natural language processing in both academic and industry systems.

Goals

Increase the size of datasets and models that can be tackled with deep learning algorithms.  Large models (e.g., neural networks with more neurons and connections) combined with large datasets are increasingly the top performers in benchmark tasks for vision, speech, and NLP.

Use Case Description

A research scientist or machine learning practitioner wants to train a deep neural network from a large (>>1TB) corpus of data (typically imagery, video, audio, or text).  Such training procedures often require customization of the neural network architecture, learning criteria, and dataset pre-processing.  In addition to the computational expense demanded by the learning algorithms, the need for rapid prototyping and ease of development is extremely high.

Current

Solutions

Compute(System)

GPU cluster with high-speed interconnects (e.g., Infiniband, 40gE)

Storage

100TB Lustre filesystem

Networking

Infiniband within HPC cluster;  1G ethernet to outside infrastructure (e.g., Web, Lustre).

Software

In-house GPU kernels and MPI-based communication developed by Stanford CS.  C++/Python source.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Centralized filesystem with a single large training dataset.  Dataset may be updated with new training examples as they become available.

Volume (size)

Current datasets typically 1 to 10 TB.  With increases in computation that enable much larger models, datasets of 100TB or more may be necessary in order to exploit the representational power of the larger models. Training a self-driving car could take 100 million images.

Velocity

(e.g. real time)

Much faster than real-time processing is required.  Current computer vision applications involve processing hundreds of image frames per second in order to ensure reasonable training times.  For demanding applications (e.g., autonomous driving) we envision the need to process many thousand high-resolution (6 megapixels or more) images per second.

Variety

(multiple datasets, mashup)

Individual applications may involve a wide variety of data.  Current research involves neural networks that actively learn from heterogeneous tasks (e.g., learning to perform tagging, chunking and parsing for text, or learning to read lips from combinations of video and audio).

Variability (rate of change)

Low variability.  Most data is streamed in at a consistent pace from a shared source.  Due to high computational requirements, server loads can introduce burstiness into data transfers.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Datasets for ML applications are often hand-labeled and verified.  Extremely large datasets involve crowd-sourced labeling and invite ambiguous situations where a label is not clear.  Automated labeling systems still require human sanity-checks.  Clever techniques for large dataset construction is an active area of research.

Visualization

Visualization of learned networks is an open area of research, though partly as a debugging technique.   Some visual applications involve visualization predictions on test imagery.

Data Quality (syntax)

Some collected data (e.g., compressed video or audio) may involve unknown formats, codecs, or may be corrupted.  Automatic filtering of original source data removes these.

Data Types

Images, video, audio, text.  (In practice: almost anything.)

Data Analytics

Small degree of batch statistical pre-processing;  all other data analysis is performed by the learning algorithm itself.

Big Data Specific Challenges (Gaps)

Processing requirements for even modest quantities of data are extreme.  Though the trained representations can make use of many terabytes of data, the primary challenge is in processing all of the data during training.  Current state-of-the-art deep learning systems are capable of using neural networks with more than 10 billion free parameters (akin to synapses in the brain), and necessitate trillions of floating point operations per training example.  Distributing these computations over high-performance infrastructure is a major challenge for which we currently use a largely custom software system.

Big Data Specific Challenges in Mobility

After training of large neural networks is completed, the learned network may be copied to other devices with dramatically lower computational capabilities for use in making predictions in real time.  (E.g., in autonomous driving, the training procedure is performed using a HPC cluster with 64 GPUs.  The result of training, however, is a neural network that encodes the necessary knowledge for making decisions about steering and obstacle avoidance.  This network can be copied to embedded hardware in vehicles or sensors.)

Security & Privacy

Requirements

None.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Deep Learning shares many characteristics with the broader field of machine learning.  The paramount requirements are high computational throughput for mostly dense linear algebra operations, and extremely high productivity.  Most deep learning systems require a substantial degree of tuning on the target application for best performance and thus necessitate a large number of experiments with designer intervention in between.  As a result, minimizing the turn-around time of experiments and accelerating development is crucial.

 

These two requirements (high throughput and high productivity) are dramatically in contention.  HPC systems are available to accelerate experiments, but current HPC software infrastructure is difficult to use which lengthens development and debugging time and, in many cases, makes otherwise computationally tractable applications infeasible.

 

The major components needed for these applications (which are currently in-house custom software) involve dense linear algebra on distributed-memory HPC systems.  While libraries for single-machine or single-GPU computation are available (e.g., BLAS, CuBLAS, MAGMA, etc.), distributed computation of dense BLAS-like or LAPACK-like operations on GPUs remains poorly developed.  Existing solutions (e.g., ScaLapack for CPUs) are not well-integrated with higher level languages and require low-level programming which lengthens experiment and development time.

 

 

More Information (URLs)

Recent popular press coverage of deep learning technology:

http://www.nytimes.com/2012/11/24/science/scientists-see-advances-in-deep-learning-a-part-of-artificial-intelligence.html

 

http://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html

 

http://www.wired.com/wiredenterprise/2013/06/andrew_ng/

 

A recent research paper on HPC for Deep Learning: http://www.stanford.edu/~acoates/papers/CoatesHuvalWangWuNgCatanzaro_icml2013.pdf

 

Widely-used tutorials and references for Deep Learning:

http://ufldl.stanford.edu/wiki/index.php/Main_Page

http://deeplearning.net/

 

Note: <additional comments>

       

Organizing large-scale, unstructured collections of consumer photos

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Organizing large-scale, unstructured collections of consumer photos

Vertical (area)

(Scientific Research: Artificial Intelligence)

Author/Company/Email

David Crandall, Indiana University, djcran@indiana.edu

Actors/Stakeholders and their roles and responsibilities

Computer vision researchers (to push forward state of art), media and social network companies (to help organize large-scale photo collections), consumers (browsing both personal and public photo collections), researchers and others interested in producing cheap 3d models (archaeologists, architects, urban planners, interior designers…)

Goals

Produce 3d reconstructions of scenes using collections of millions to billions of consumer images, where neither the scene structure nor the camera positions are known a priori. Use resulting 3d models to allow efficient and effective browsing of large-scale photo collections by geographic position. Geolocate new images by matching to 3d models. Perform object recognition on each image.

Use Case Description

3d reconstruction is typically posed as a robust non-linear least squares optimization problem in which observed (noisy) correspondences between images are constraints and unknowns are 6-d camera pose of each image and 3-d position of each point in the scene. Sparsity and large degree of noise in constraints typically makes naïve techniques fall into local minima that are not close to actual scene structure. Typical specific steps are: (1) extracting features from images, (2) matching images to find pairs with common scene structures, (3) estimating an initial solution that is close to scene structure and/or camera parameters, (4) optimizing non-linear objective function directly. Of these, (1) is embarrassingly parallel. (2) is an all-pairs matching problem, usually with heuristics to reject unlikely matches early on. We solve (3) using discrete optimization using probabilistic inference on a graph (Markov Random Field) followed by robust Levenberg-Marquardt in continuous space. Others solve (3) by solving (4) for a small number of images and then incrementally adding new images, using output of last round as initialization for next round. (4) is typically solved with Bundle Adjustment, which is a non-linear least squares solver that is optimized for the particular constraint structure that occurs in 3d reconstruction problems. Image recognition problems are typically embarrassingly parallel, although learning object models involves learning a classifier (e.g. a Support Vector Machine), a process that is often hard to parallelize.

Current

Solutions

Compute(System)

Hadoop cluster (about 60 nodes, 480 core)

Storage

Hadoop DFS and flat files

Networking

Simple Unix

Software

Hadoop Map-reduce, simple hand-written multithreaded tools (ssh and sockets for communication)

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Publicly-available photo collections, e.g. on Flickr, Panoramio, etc.

Volume (size)

500+ billion photos on Facebook, 5+ billion photos on Flickr.

Velocity

(e.g. real time)

100+ million new photos added to Facebook per day.

Variety

(multiple datasets, mashup)

Images and metadata including EXIF tags (focal distance, camera type, etc),

Variability (rate of change)

Rate of photos varies significantly, e.g. roughly 10x photos to Facebook on New Years versus other days. Geographic distribution of photos follows long-tailed distribution, with 1000 landmarks (totaling only about 100 square km) accounting for over 20% of photos on Flickr.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Important to make as accurate as possible, subject to limitations of computer vision technology.

Visualization

Visualize large-scale 3-d reconstructions, and navigate large-scale collections of images that have been aligned to maps.

Data Quality

Features observed in images are quite noisy due both to imperfect feature extraction and to non-ideal properties of specific images (lens distortions, sensor noise, image effects added by user, etc.)

Data Types

Images, metadata

Data Analytics

 

Big Data Specific Challenges (Gaps)

Analytics needs continued monitoring and improvement.

Big Data Specific Challenges in Mobility

Many/most images are captured by mobile devices; eventual goal is to push reconstruction and organization to phone to allow real-time interaction with the user.

Security & Privacy

Requirements

Need to preserve privacy for users and digital rights for media.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Components of this use case including feature extraction, feature matching, and large-scale probabilistic inference appear in many or most computer vision and image processing problems, including recognition, stereo resolution, image denoising, etc.

 

More Information (URLs)

http://vision.soic.indiana.edu/disco

Note: <additional comments>

       

Truthy: Information diffusion research from Twitter Data

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Truthy: Information diffusion research from Twitter Data

Vertical (area)

Scientific Research: Complex Networks and Systems research

Author/Company/Email

Filippo Menczer, Indiana University, fil@indiana.edu;

Alessandro Flammini, Indiana University, aflammin@indiana.edu;

Emilio Ferrara, Indiana University, ferrarae@indiana.edu;

Actors/Stakeholders and their roles and responsibilities

Research funded by NFS, DARPA, and McDonnel Foundation.

Goals

Understanding how communication spreads on socio-technical networks. Detecting potentially harmful information spread at the early stage (e.g., deceiving messages, orchestrated campaigns, untrustworthy information, etc.)

Use Case Description

(1) Acquisition and storage of a large volume of continuous streaming data from Twitter (~100 million messages per day, ~500GB data/day increasing over time); (2) near real-time analysis of such data, for anomaly detection, stream clustering, signal classification and online-learning; (3) data retrieval, big data visualization, data-interactive Web interfaces, public API for data querying.

Current

Solutions

Compute(System)

Current: in-house cluster hosted by Indiana University. Critical requirement: large cluster for data storage, manipulation, querying and analysis.

Storage

Current: Raw data stored in large compressed flat files, since August 2010. Need to move towards Hadoop/IndexedHBase & HDFS distributed storage. Redis as a in-memory database as a buffer for real-time analysis.

Networking

10GB/Infiniband required.

Software

Hadoop, Hive, Redis for data management.

Python/SciPy/NumPy/MPI for data analysis.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Distributed – with replication/redundancy

Volume (size)

~30TB/year compressed data

Velocity (e.g. real time)

Near real-time data storage, querying & analysis

Variety (multiple datasets, mashup)

Data schema provided by social media data source. Currently using Twitter only. We plan to expand incorporating Google+, Facebook

Variability (rate of change)

Continuous real-time data-stream incoming from each source.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

99.99% uptime required for real-time data acquisition. Service outages might corrupt data integrity and significance.

Visualization

Information diffusion, clustering, and dynamic network visualization capabilities already exist.

Data Quality (syntax)

Data structured in standardized formats, the overall quality is extremely high. We generate aggregated statistics; expand the features set, etc., generating high-quality derived data.

Data Types

Fully-structured data (JSON format) enriched with users meta-data, geo-locations, etc.

Data Analytics

Stream clustering: data are aggregated according to topics, meta-data and additional features, using ad hoc online clustering algorithms. Classification: using multi-dimensional time series to generate, network features, users, geographical, content features, etc., we classify information produced on the platform. Anomaly detection: real-time identification of anomalous events (e.g., induced by exogenous factors). Online learning: applying machine learning/deep learning methods to real-time information diffusion patterns analysis, users profiling, etc.

Big Data Specific Challenges (Gaps)

Dealing with real-time analysis of large volume of data. Providing a scalable infrastructure to allocate resources, storage space, etc. on-demand if required by increasing data volume over time. 

Big Data Specific Challenges in Mobility

Implementing low-level data storage infrastructure features to guarantee efficient, mobile access to data.

Security & Privacy

Requirements

Twitter publicly releases data collected by our platform. Although, data-sources incorporate user meta-data (in general, not sufficient to uniquely identify individuals) therefore some policy for data storage security and privacy protection must be implemented.

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Definition of high-level data schema to incorporate multiple data-sources providing similarly structured data.

More Information (URLs)

http://truthy.indiana.edu/

http://cnets.indiana.edu/groups/nan/truthy

http://cnets.indiana.edu/groups/nan/despic

Note: <additional comments>

       

Crowd Sourcing in the Humanities as Source for Big and Dynamic Data

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Crowd Sourcing in the Humanities as Source for Big and Dynamic Data

Vertical (area)

Humanities, Social Sciences

Author/Company/Email

Sebastian Drude <Sebastian.Drude@mpi.nl>, Max Planck Institute for Psycholinguistics (MPI)

Actors/Stakeholders and their roles and responsibilities

Scientists (Sociologists, Psychologists, Linguists, Politic Scientists, Historians, etc.), data managers and analysts, data archives

The general public as data providers and participants

Goals

Capture information (manually entered, recorded multimedia, reaction times, pictures, sensor information) from many individuals and their devices.

Thus capture wide ranging individual, social, cultural and linguistic variation among several dimensions (space, social space, time).

 

Use Case Description

Many different possible use cases: get recordings of language usage (words, sentences, meaning descriptions, etc.), answers to surveys, info on cultural facts, transcriptions of pictures and texts -- correlate these with other phenomena, detect new cultural practices, behavior, values and believes, discover individual variation

 

Current

Solutions

Compute(System)

Individual systems for manual data collection (mostly Websites)

Storage

Traditional servers

Networking

barely used other than for data entry via web

Software

XML technology, traditional relational databases for storing  pictures, not much multi-media yet.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Distributed, individual contributors via webpages and mobile devices

Volume (size)

Depends dramatically, from hundreds to millions of data records.

Depending on data-type: from gigabytes (text, surveys, experiment values) to hundreds of terabytes (multimedia)

Velocity

(e.g. real time)

Depends very much on project: dozens to thousands of new data records per day

Data has to be analyzed incrementally.

Variety

(multiple datasets, mashup)

so far mostly homogeneous small data sets; expected large distributed heterogeneous datasets which have to be archived as primary data

Variability (rate of change)

Data structure and content of collections are changing during data lifecycle.

There is no critical variation of data producing speed, or runtime characteristics variations.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Noisy data is possible, unreliable metadata, identification and pre-selection of appropriate data

Visualization

important for interpretation, no special visualization techniques

Data Quality

validation is necessary; quality of recordings, quality of content, spam

Data Types

individual data records (survey answers, reaction times);

text (e.g., comments, transcriptions,…);

multi-media (pictures, audio, video)

Data Analytics

pattern recognition of all kind (e.g., speech recognition, automatic A&V analysis, cultural patterns), identification of structures (lexical units, linguistic rules, etc)

Big Data Specific Challenges (Gaps)

Data management (metadata, provenance info, data identification with PIDs)

Data curation

Digitising existing audio-video, photo and documents archives

Big Data Specific Challenges in Mobility

Include data from sensors of mobile devices (position, etc.);

Data collection from expeditions and field research.

Security & Privacy

Requirements

Privacy issues may be involved (A/V from individuals), anonymization may be necessary but not always possible (A/V analysis, small speech communities)

Archive and metadata integrity, long term preservation

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Many individual data entries from many individuals, constant flux of data entry, metadata assignment, etc.

Offline vs. online use, to be synchronized later with central database.

Giving significant feedback to contributors.

More Information (URLs)

---

 

 

Note: Crowd sourcing has been barely started to be used on a larger scale.
With the availability of mobile devices, now there is a huge potential for collecting much data from many individuals, also making use of sensors in mobile devices.  This has not been explored on a large scale so far; existing projects of crowd sourcing are usually of a limited scale and web-based.

       

CINET: Cyberinfrastructure for Network (Graph) Science and Analytics

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

CINET: Cyberinfrastructure for Network (Graph) Science and Analytics

Vertical (area)

Network Science

Author/Company/Email

Team lead by Virginia Tech and comprising of researchers from Indiana University, University at Albany, North Carolina AT, Jackson State University, University at Houston Downtown, Argonne National Laboratory

Point of Contact: Madhav Marathe or Keith Bisset, Network Dynamics and Simulation Science Laboratory, Virginia Bio-informatics Institute Virginia Tech, mmarathe@vbi.vt.edu  / kbisset@vbi.vt.edu

Actors/Stakeholders and their roles and responsibilities

Researchers, practitioners, educators and students interested in the study of networks.

Goals

CINET cyberinfrastructure middleware to support network science. This middleware will give researchers, practitioners, teachers and students access to a computational and analytic environment for research, education and training. The user interface provides lists of available networks and network analysis modules (implemented algorithms for network analysis). A user, who can be a researcher in network science area, can select one or more networks and analysis them with the available network analysis tools and modules. A user can also generate random networks following various random graph models. Teachers and students can use CINET for classroom use to demonstrate various graph theoretic properties and behaviors of various algorithms. A user is also able to add a network or network analysis module to the system. This feature of CINET allows it to grow easily and remain up-to-date with the latest algorithms.

 

The goal is to provide a common web-based platform for accessing various (i) network and graph analysis tools such as SNAP, NetworkX, Galib, etc. (ii) real-world and synthetic networks, (iii)  computing resources and (iv) data management systems to the end-user in a seamless manner.

Use Case Description

Users can run one or more structural or dynamic analysis on a set of selected networks. The domain specific language allows users to develop flexible high level workflows to define more complex network analysis.

Current

Solutions

Compute(System)

A high performance computing cluster (DELL C6100), named Shadowfax, of 60 compute nodes and 12 processors (Intel Xeon X5670 2.93GHz) per compute node with a total of 720 processors and 4GB main memory per processor.

 

Shared memory systems ; EC2 based clouds are also used

 

Some of the codes and networks can utilize single node systems and thus are being currently mapped to Open Science Grid

 

Storage

628 TB GPFS

Networking

Internet, infiniband.  A loose collection of supercomputing resources.

Software

Graph libraries: Galib, NetworkX.

Distributed Workflow Management: Simfrastructure, databases, semantic web tools

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

A single network remains in a single disk file accessible by multiple processors. However, during the execution of a parallel algorithm, the network can be partitioned and the partitions are loaded in the main memory of multiple processors.

Volume (size)

Can be hundreds of GB for a single network.

Velocity

(e.g. real time)

Two types of changes: (i) the networks are very dynamic and (ii)  as the repository grows, we expect at least a rapid growth to lead to over 1000-5000 networks and methods in about a year

Variety

(multiple datasets, mashup)

Data sets are varied: (i) directed as well as undirected networks, (ii) static and dynamic networks, (iii) labeled, (iv) can have dynamics over these networks,

Variability (rate of change)

The rate of graph-based data is growing at increasing rate.  Moreover, increasingly other life sciences domains are using graph-based techniques to address problems. Hence, we expect the data and the computation to grow at a significant pace.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Challenging due to asynchronous distributed computation. Current systems are designed for real time synchronous response.

Visualization

As the input graph size grows the visualization system on client side is stressed heavily both in terms of data and compute.

Data Quality (syntax)

 

Data Types

 

Data Analytics

 

Big Data Specific Challenges (Gaps)

Parallel algorithms are necessary to analyze massive networks. Unlike many structured data, network data is difficult to partition. The main difficulty in partitioning a network is that different algorithms require different partitioning schemes for efficient operation. Moreover, most of the network measures are global in nature and require either i) huge duplicate data in the partitions or ii) very large communication overhead resulted from the required movement of data. These issues become significant challenges for big networks.

 

Computing dynamics over networks is harder since the network structure often interacts with the dynamical process being studied.

 

CINET enables large class of operations across wide variety, both in terms of structure and size, of graphs. Unlike other compute + data intensive systems, such as parallel databases or CFD, performance on graph computation is sensitive to underlying architecture.   Hence, a unique challenge in CINET is manage the mapping between workload (graph type + operation) to a machine whose architecture and runtime is conducive to the system.

 

Data manipulation and bookkeeping of the derived for users is another big challenge since unlike enterprise data there is no well defined and effective models and tools for management of various graph data in a unified fashion.

 

Big Data Specific Challenges in Mobility

 

 

Security & Privacy

Requirements

 

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

HPC as a service. As data volume grows increasingly large number of applications such as biological sciences need to use HPC systems. CINET can be used to deliver the compute resource necessary for such domains.

 

 

More Information (URLs)

http://cinet.vbi.vt.edu/cinet_new/

Note: <additional comments>

       

NIST Information Access Division analytic technology performance measurement, evaluations, and standards

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

NIST Information Access Division analytic technology performance measurement, evaluations, and standards

Vertical (area)

Analytic technology performance measurement and standards for government, industry, and academic stakeholders

Author/Company/Email

John Garofolo (john.garofolo@nist.gov)

Actors/Stakeholders and their roles and responsibilities

NIST developers of measurement methods, data contributors, analytic algorithm developers, users of analytic technologies for unstructured, semi-structured data, and heterogeneous data across all sectors.

Goals

Accelerate the development of advanced analytic technologies for unstructured, semi-structured, and heterogeneous data through performance measurement and standards. Focus communities of interest on analytic technology challenges of importance, create consensus-driven measurement metrics and methods for performance evaluation, evaluate the performance of the performance metrics and methods via community-wide evaluations which foster knowledge exchange and accelerate progress, and build consensus towards widely-accepted standards for performance measurement.

 

Use Case Description

Develop performance metrics, measurement methods, and community evaluations to ground and accelerate the development of advanced analytic technologies in the areas of speech and language processing, video and multimedia processing, biometric image processing, and heterogeneous data processing as well as the interaction of analytics with users. Typically employ one of two processing models: 1) Push test data out to test participants and analyze the output of participant systems, 2) Push algorithm test harness interfaces out to participants and bring in their algorithms and test them on internal computing clusters.  Developing approaches to support scalable Cloud-based developmental testing.  Also perform usability and utility testing on systems with users in the loop.

 

Current

Solutions

Compute(System)

Linux and OS-10 clusters; distributed computing with stakeholder collaborations; specialized image processing architectures.

Storage

RAID arrays, and distribute data on 1-2TB drives, and occasionally FTP. Distributed data distribution with stakeholder collaborations.

Networking

Fiber channel disk storage, Gigabit Ethernet for system-system communication, general intra- and Internet resources within NIST and shared networking resources with its stakeholders.

Software

PERL, Python, C/C++, Matlab, R development tools. Create ground-up test and measurement applications.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Large annotated corpora of unstructured/semi-structured text, audio, video, images, multimedia, and heterogeneous collections of the above including ground truth annotations for training, developmental testing, and summative evaluations.

Volume (size)

The test corpora exceed 900M Web pages occupying 30 TB of storage, 100M tweets, 100M ground-truthed biometric images, several hundred thousand partially ground-truthed video clips, and terabytes of smaller fully ground-truthed test collections.  Even larger data collections are being planned for future evaluations of analytics involving multiple data streams and very heterogeneous data.

Velocity

(e.g. real time)

Most legacy evaluations are focused on retrospective analytics.  Newer evaluations are focusing on simulations of real-time analytic challenges from multiple data streams.

Variety

(multiple datasets, mashup)

The test collections span a wide variety of analytic application types including textual search/extraction, machine translation, speech recognition, image and voice biometrics, object and person recognition and tracking, document analysis, human-computer dialogue, and multimedia search/extraction.  Future test collections will include mixed type data and applications.

Variability (rate of change)

Evaluation of tradeoffs between accuracy and data rates as well as variable numbers of data streams and variable stream quality.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

The creation and measurement of the uncertainty associated with the ground-truthing process – especially when humans are involved – is challenging.  The manual ground-truthing processes that have been used in the past are not scalable.  Performance measurement of complex analytics must include measurement of intrinsic uncertainty as well as ground truthing error to be useful.

Visualization

Visualization of analytic technology performance results and diagnostics including significance and various forms of uncertainty. Evaluation of analytic presentation methods to users for usability, utility, efficiency, and accuracy.

Data Quality (syntax)

The performance of analytic technologies is highly impacted by the quality of the data they are employed against with regard to a variety of domain- and application-specific variables.  Quantifying these variables is a challenging research task in itself.  Mixed sources of data and performance measurement of analytic flows pose even greater challenges with regard to data quality.

Data Types

Unstructured and semi-structured text, still images, video, audio, multimedia (audio+video).

Data Analytics

Information extraction, filtering, search, and summarization; image and voice biometrics; speech recognition and understanding; machine translation; video person/object detection and tracking; event detection; imagery/document matching; novelty detection; a variety of structural/semantic/temporal analytics and many subtypes of the above.

Big Data Specific Challenges (Gaps)

Scaling ground-truthing to larger data, intrinsic and annotation uncertainty measurement, performance measurement for incompletely annotated data, measuring analytic performance for heterogeneous data and analytic flows involving users.

Big Data Specific Challenges in Mobility

Moving training, development, and test data to evaluation participants or moving evaluation participants’ analytic algorithms to computational testbeds for performance assessment.  Providing developmental tools and data. Supporting agile developmental testing approaches.

 

Security & Privacy

Requirements

Analytic algorithms working with written language, speech, human imagery, etc. must generally be tested against real or realistic data.  It’s extremely challenging to engineer artificial data that sufficiently captures the variability of real data involving humans. Engineered data may provide artificial challenges that may be directly or indirectly modeled by analytic algorithms and result in overstated performance.  The advancement of analytic technologies themselves is increasing privacy sensitivities. Future performance testing methods will need to isolate analytic technology algorithms from the data the algorithms are tested against.  Advanced architectures are needed to support security requirements for protecting sensitive data while enabling meaningful developmental performance evaluation. Shared evaluation testbeds must protect the intellectual property of analytic algorithm developers.

Highlight issues for generalizing this use case (e.g. for ref. architecture)

 

Scalability of analytic technology performance testing methods, source data creation, and ground truthing; approaches and architectures supporting developmental testing; protecting intellectual property of analytic algorithms and PII and other personal information in test data; measurement of uncertainty using partially-annotated data; composing test data with regard to qualities impacting performance and estimating test set difficulty;  evaluating complex analytic flows involving multiple analytics, data types, and user interactions; multiple heterogeneous data streams and massive numbers of streams; mixtures of structured, semi-structured, and unstructured data sources; agile scalable developmental testing approaches and mechanisms.

 

More Information (URLs)

 

www.nist.gov/itl/iad/

 

Note: <additional comments>

       

The Ecosystem for Research

DataNet Federation Consortium DFC

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

DataNet Federation Consortium (DFC)

Vertical (area)

Collaboration Environments

Author/Company/Email

Reagan Moore / University of North Carolina at Chapel Hill / rwmoore@renci.org

Actors/Stakeholders and their roles and responsibilities

National Science Foundation research projects:  Ocean Observatories Initiative (sensor archiving); Temporal Dynamics of Learning Center (Cognitive science data grid); the iPlant Collaborative (plant genomics); Drexel engineering digital library; Odum Institute for social science research (data grid federation with Dataverse).

Goals

Provide national infrastructure (collaboration environments) that enables researchers to collaborate through shared collections and shared workflows.  Provide policy-based data management systems that enable the formation of collections, data grid, digital libraries, archives, and processing pipelines.  Provide interoperability mechanisms that federate existing data repositories, information catalogs, and web services with collaboration environments.

Use Case Description

Promote collaborative and interdisciplinary research through federation of data management systems across federal repositories, national academic research initiatives, institutional repositories, and international collaborations.  The collaboration environment runs at scale: petabytes of data, hundreds of millions of files, hundreds of millions of metadata attributes, tens of thousands of users, and a thousand storage resources.

Current

Solutions

Compute(System)

Interoperability with workflow systems (NCSA Cyberintegrator, Kepler, Taverna)

Storage

Interoperability across file systems, tape archives, cloud storage, object-based storage

Networking

Interoperability across TCP/IP, parallel TCP/IP, RBUDP, HTTP

Software

Integrated Rule Oriented Data System (iRODS)

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Manage internationally distributed data

Volume (size)

Petabytes, hundreds of millions of files

Velocity

(e.g. real time)

Support sensor data streams, satellite imagery, simulation output, observational data, experimental data

Variety

(multiple datasets, mashup)

Support logical collections that span administrative domains, data aggregation in containers, metadata, and workflows as objects

Variability (rate of change)

Support active collections (mutable data), versioning of data, and persistent identifiers

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Provide reliable data transfer, audit trails, event tracking, periodic validation of assessment criteria (integrity, authenticity), distributed debugging

Visualization

Support execution of external visualization systems through automated workflows (GRASS)

Data Quality

Provide mechanisms to verify quality through automated workflow procedures

Data Types

Support parsing of selected formats (NetCDF, HDF5, Dicom), and provide mechanisms to invoke other data manipulation methods

Data Analytics

Provide support for invoking analysis workflows, tracking workflow provenance, sharing of workflows, and re-execution of workflows

Big Data Specific Challenges (Gaps)

Provide standard policy sets that enable a new community to build upon data management plans that address federal agency requirements

Big Data Specific Challenges in Mobility

Capture knowledge required for data manipulation, and apply resulting procedures at either the storage location, or a computer server.

 

Security & Privacy

Requirements

Federate across existing authentication environments through Generic Security Service API and Pluggable Authentication Modules (GSI, Kerberos, InCommon, Shibboleth).  Manage access controls on files independently of the storage location.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Currently 25 science and engineering domains have projects that rely on the iRODS policy-based data management system:

Astrophysics                         Auger supernova search

Atmospheric science          NASA Langley Atmospheric Sciences Center

Biology                                   Phylogenetics at CC IN2P3

Climate                                   NOAA National Climatic Data Center

Cognitive Science                                Temporal Dynamics of Learning Center

Computer Science               GENI experimental network

Cosmic Ray                            AMS experiment on the International Space Station

Dark Matter Physics            Edelweiss II

Earth Science                        NASA Center for Climate Simulations

Ecology                                  CEED Caveat Emptor Ecological Data

Engineering                           CIBER-U

High Energy Physics             BaBar

Hydrology                              Institute for the Environment, UNC-CH; Hydroshare

Genomics                              Broad Institute, Wellcome Trust Sanger Institute

Medicine                               Sick Kids Hospital

Neuroscience                       International Neuroinformatics Coordinating Facility

Neutrino Physics                  T2K and dChooz neutrino experiments

Oceanography                      Ocean Observatories Initiative

Optical Astronomy               National Optical Astronomy Observatory

Particle Physics                    Indra

Plant genetics                       the iPlant Collaborative

Quantum Chromodynamics               IN2P3

Radio Astronomy                 Cyber Square Kilometer Array, TREND, BAOradio

Seismology                            Southern California Earthquake Center

Social Science                       Odum Institute for Social Science Research, TerraPop

 

More Information (URLs)

The DataNet Federation Consortium:  http://www.datafed.org

iRODS:  http://www.irods.org

Note: <additional comments>A major challenge is the ability to capture knowledge needed to interact with the data products of a research domain.  In policy-based data management systems, this is done by encapsulating the knowledge in procedures that are controlled through policies.  The procedures can automate retrieval of data from external repositories, or execute processing workflows, or enforce management policies on the resulting data products.  A standard application is the enforcement of data management plans and the verification that the plan has been successfully applied.

       

Figure Policy-based Data Management Concept Graph (iRODS)

DataNet Federation Consortium (DFC).png

The ‘Discinnet process’, metadata <-> big data global experiment

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

The ‘Discinnet process’, metadata <-> big data global experiment

Vertical (area)

Scientific Research: Interdisciplinary Collaboration

Author/Company/Email

P. Journeau / Discinnet Labs / phjourneau@discinnet.org

Actors/Stakeholders and their roles and responsibilities

Actors Richeact, Discinnet Labs and I4OpenResearch fund France/Europe. American equivalent pending. Richeact  is fundamental R&D epistemology, Discinnet Labs applied in web 2.0 www.discinnet.org, I4 non-profit warrant.

Goals

Richeact scientific goal is to reach predictive interdisciplinary model of research fields’ behavior (with related meta-grammar). Experimentation through global sharing of now multidisciplinary, later interdisciplinary Discinnet process/web mapping and new scientific collaborative communication and publication system. Expected sharp impact to reducing uncertainty and time between theoretical, applied, technology R&D steps.

Use Case Description

Currently 35 clusters started, close to 100 awaiting more resources and potentially much more open for creation, administration and animation by research communities. Examples range from optics, cosmology, materials, microalgae, health to applied maths, computation, rubber and other chemical products/issues.

How does a typical case currently work:

-       A researcher or group wants to see how a research field is faring and in a minute defines the field on Discinnet as a ‘cluster’

-       Then it takes another 5 to 10 mn to parameter the first/main dimensions, mainly measurement units and categories, but possibly later on some variable limited time for more dimensions

-       Cluster then may be filled either by doctoral students or reviewing researchers and/or communities/researchers for projects/progress

Already significant value but now needs to be disseminated and advertised although maximal value to come from interdisciplinary/projective next version. Value is to detect quickly a paper/project of interest for its results and next step is trajectory of the field under types of interactions from diverse levels of oracles (subjects/objects) + from interdisciplinary context.

Current

Solutions

Compute(System)

Currently on OVH (Hosting company http://www.ovh.co.uk/) servers (mix shared + dedicated)

Storage

OVH

Networking

To be implemented with desired integration with others

Software

Current version with Symfony-PHP, Linux, MySQL

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Currently centralized, soon distributed per country and even per hosting institution interested by own platform

Volume (size)

Not significant : this is a metadata base, not big data

Velocity

(e.g. real time)

Real time

Variety

(multiple datasets, mashup)

Link to Big data still to be established in a Meta<->Big relationship not yet implemented (with experimental databases and already 1st level related metadata)

Variability (rate of change)

Currently Real time, for further multiple locations and distributed architectures, periodic (such as nightly)

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Methods to detect overall consistency, holes, errors, misstatements, known but mostly to be implemented

Visualization

Multidimensional (hypercube)

Data Quality (syntax)

A priori correct (directly human captured) with sets of checking + evaluation processes partly implemented

Data Types

‘cluster displays’ (image), vectors, categories, PDFs

Data Analytics

 

Big Data Specific Challenges (Gaps)

Our goal is to contribute to Big 2 Metadata challenge by systematic reconciling between metadata from many complexity levels with ongoing input from researchers from ongoing research process.

Current relationship with Richeact is to reach the interdisciplinary model, using meta-grammar itself to be experimented and its extent fully proven to bridge efficiently the gap between as remote complexity levels as semantic and most elementary (big) signals. Example with cosmological models versus many levels of intermediary models (particles, gases, galactic, nuclear, geometries). Others with computational versus semantic levels.

Big Data Specific Challenges in Mobility

Appropriate graphic interface power

 

Security & Privacy

Requirements

Several levels already available and others planned, up to physical access keys and isolated servers. Optional anonymity, usual protected exchanges

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Through 2011-2013, we have shown on www.discinnet.org that all kinds of research fields could easily get into Discinnet type of mapping, yet developing and filling a cluster requires time and/or dedicated workers.

 

More Information (URLs)

On www.discinnet.org the already started or starting clusters can be watched in one click on ‘cluster’ (field) title and even more detail is available through free registration (more resource available when registering as researcher (publications) or pending (doctoral student)

Maximum level of detail is free for contributing researchers in order to protect communities but available to external observers for symbolic fee: all suggestions for improvements and better sharing welcome.

We are particularly open to provide and support experimental appropriation by doctoral schools to build and study the past and future behavior of clusters in Earth sciences, Cosmology, Water, Health, Computation, Energy/Batteries, Climate models, Space, etc..

Note: <additional comments>: We are open to facilitate wide appropriation of both global, regional and local versions of the platform (for instance by research institutions, publishers, networks with desirable maximal data sharing for the greatest benefit of advancement of science.

       

Enabling Face-Book like Semantic Graph-search on Scientific Chemical and Text-based Data

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Enabling Face-Book like Semantic Graph-search on Scientific Chemical and Text-based Data

Vertical (area)

Management of Information from Research Articles

Author/Company/Email

Talapady Bhat, bhat@nist.gov

Actors/Stakeholders and their roles and responsibilities

Chemical structures, Protein Data Bank, Material Genome Project, Open-GOV initiative, Semantic Web, Integrated Data-graphs, Scientific social media

Goals

Establish infrastructure, terminology and semantic data-graphs to annotate and present technology information using ‘root’ and rule-based methods used primarily by some Indo-European languages like Sanskrit and Latin.

 

Use Case Description

·         Social media hype

o   Internet and social media play a significant role in modern information exchange. Every day most of us use social-media both to distribute and receive information. Two of the special features of many social media like Face-Book are

§  the community is both data-providers and data-users

§  they store information in a pre-defined ‘data-shelf’ of a data-graph

§  Their core infrastructure for managing information is reasonably language free

·         What this has to do with managing scientific information?

During the last few decades science has truly evolved to become a community activity involving every country and almost every household. We routinely ‘tune-in’ to internet resources to share and seek scientific information.

o   What are the challenges in creating social media for science

o    Creating a social media of scientific information needs an infrastructure where many scientists from various parts of the world can participate and deposit results of their experiment. Some of the issues that one has to resolve prior to establishing a scientific social media are:

§  How to minimize challenges related to local language and its grammar?

§  How to determining the ‘data-graph’ to place an information in an intuitive way without knowing too much about the data management?

§  How to find relevant scientific data without spending too much time on the internet?

Approach: Most languages and more so Sanskrit and Latin use a novel ‘root’-based method to facilitate the creation of on-demand, discriminating words to define concepts. Some such examples from English are Bio-logy, Bio-chemistry. Youga, Yogi, Yogendra, Yogesh are examples from Sanskrit.  Genocide is an example from Latin. These words are created on-demand based on best-practice terms and their capability to serve as node in a discriminating data-graph with self-explained meaning.

Current

Solutions

Compute(System)

Cloud for the participation of community

Storage

Requires expandable on-demand based resource that is suitable for global users location and requirements

Networking

Needs good network for the community participation

Software

Good database tools and servers for data-graph manipulation are needed

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Distributed resource with a limited centralized capability

Volume (size)

Undetermined. May be few terabytes at the beginning

Velocity

(e.g. real time)

Evolving with time to accommodate new best-practices

Variety

(multiple datasets, mashup)

Wildly varying depending on the types available technological information

Variability (rate of change)

Data-graphs are likely to change in time based on customer preferences and best-practices

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Technological information is likely to be stable and robust

Visualization

Efficient data-graph based visualization is needed

Data Quality

Expected to be good

Data Types

All data types, image to text, structures to protein sequence

Data Analytics

Data-graphs is expected to provide robust data-analysis methods

Big Data Specific Challenges (Gaps)

This is a community effort similar to many social media. Providing a robust, scalable, on-demand infrastructures in a manner that is use-case and user-friendly is a real-challenge by any existing conventional methods

Big Data Specific Challenges in Mobility

A community access is required for the data and thus it has to be media and location independent and thus requires high mobility too.

 

Security & Privacy

Requirements

None since the effort is initially focused on publicly accessible data provided by open-platform projects like open-gov, MGI and protein data bank.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

 

This effort includes many local and networked resources. Developing an infrastructure to automatically integrate information from all these resources using data-graphs is a challenge that we are trying to solve.

 

More Information (URLs)

http://www.eurekalert.org/pub_releases/2013-07/aiop-ffm071813.php

http://xpdb.nist.gov/chemblast/pdb.pl

http://xpdb.nist.gov/chemblast/pdb.pl

 

Note: <additional comments>

       

Light source beamlines

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Light source beamlines

Vertical (area)

Research (Biology, Chemistry, Geophysics, Materials Science, others)

Author/Company/Email

Eli Dart, LBNL (eddart@lbl.gov)

Actors/Stakeholders and their roles and responsibilities

Research groups from a variety of scientific disciplines (see above)

Goals

Use of a variety of experimental techniques to determine structure, composition, behavior, or other attributes of a sample relevant to scientific enquiry.

 

Use Case Description

 

Samples are exposed to X-rays in a variety of configurations depending on the experiment.  Detectors (essentially high-speed digital cameras) collect the data.  The data are then analyzed to reconstruct a view of the sample or process being studied.  The reconstructed images are used by scientists analysis.

 

Current

Solutions

Compute(System)

Computation ranges from single analysis hosts to high-throughput computing systems at computational facilities

Storage

Local storage on the order of 1-40TB on Windows or Linux data servers at facility for temporary storage, over 60TB on disk at NERSC, over 300TB on tape at NERSC

Networking

10Gbps Ethernet at facility, 100Gbps to NERSC

Software

A variety of commercial and open source software is used for data analysis – examples include:

·         Octopus (http://www.inct.be/en/software/octopus) for Tomographic Reconstruction

·         Avizo (http://vsg3d.com) and FIJI (a distribution of ImageJ; http://fiji.sc) for Visualization and Analysis

Data transfer is accomplished using physical transport of portable media (severely limits performance) or using high-performance GridFTP, managed by Globus Online or workflow systems such as SPADE.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Centralized (high resolution camera at facility).  Multiple beamlines per facility with high-speed detectors.

Volume (size)

3GB to 30GB per sample – up to 15 samples/day

Velocity

(e.g. real time)

Near-real-time analysis needed for verifying experimental parameters (lower resolution OK). Automation of analysis would dramatically improve scientific productivity.

Variety

(multiple datasets, mashup)

Many detectors produce similar types of data (e.g. TIFF files), but experimental context varies widely

Variability (rate of change)

Detector capabilities are increasing rapidly.  Growth is essentially Moore’s Law.  Detector area is increasing exponentially (1k x 1k, 2k x 2k, 4k x 4k, …) and readout is increasing exponentially (1Hz, 10Hz, 100Hz, 1kHz, …).  Single detector data rates are expected to reach 1 Gigabyte per second within 2 years.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Near real time analysis required to verify experimental parameters.  In many cases, early analysis can dramatically improve experiment productivity by providing early feedback.  This implies high-throughput computing, high-performance data transfer, and high-speed storage are routinely available.

Visualization

Visualization is key to a wide variety of experiments at all light source facilities

Data Quality

Data quality and precision are critical (especially since beam time is scarce, and re-running an experiment is often impossible).

Data Types

Many beamlines generate image data (e.g. TIFF files)

Data Analytics

Volume reconstruction, feature identification, others

Big Data Specific Challenges (Gaps)

Rapid increase in camera capabilities, need for automation of data transfer and near-real-time analysis.

Big Data Specific Challenges in Mobility

Data transfer to large-scale computing facilities is becoming necessary because of the computational power required to conduct the analysis on time scales useful to the experiment.  Large number of beamlines (e.g. 39 at LBNL ALS) means that aggregate data load is likely to increase significantly over the coming years.

 

Security & Privacy

Requirements

Varies with project.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

There will be significant need for a generalized infrastructure for analyzing gigabytes per second of data from many beamline detectors at multiple facilities.  Prototypes exist now, but routine deployment will require additional resources.

 

More Information (URLs)

http://www-als.lbl.gov/

http://www.aps.anl.gov/

https://portal.slac.stanford.edu/sites/lcls_public/Pages/Default.aspx

 

 

Note: <additional comments>

       

Astronomy and Physics

Catalina Real-Time Transient Survey (CRTS): a digital, panoramic, synoptic sky survey

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Catalina Real-Time Transient Survey (CRTS): a digital, panoramic, synoptic sky survey

Vertical (area)

Scientific Research: Astronomy

Author/Company/Email

S. G. Djorgovski / Caltech / george@astro.caltech.edu

Actors/Stakeholders and their roles and responsibilities

The survey team: data processing, quality control, analysis and interpretation, publishing, and archiving.

Collaborators: a number of research groups world-wide: further work on data analysis and interpretation, follow-up observations, and publishing.

User community: all of the above, plus the astronomical community world-wide: further work on data analysis and interpretation, follow-up observations, and publishing.

Goals

The survey explores the variable universe in the visible light regime, on time scales ranging from minutes to years, by searching for variable and transient sources.  It discovers a broad variety of astrophysical objects and phenomena, including various types of cosmic explosions (e.g., Supernovae), variable stars, phenomena associated with accretion to massive black holes (active galactic nuclei) and their relativistic jets, high proper motion stars, etc.

 

Use Case Description

The data are collected from 3 telescopes (2 in Arizona and 1 in Australia), with additional ones expected in the near future (in Chile).  The original motivation is a search for near-Earth (NEO) and potential planetary hazard (PHO) asteroids, funded by NASA, and conducted by a group at the Lunar and Planetary Laboratory (LPL) at the Univ. of Arizona (UA); that is the Catalina Sky Survey proper (CSS).  The data stream is shared by the CRTS for the purposes for exploration of the variable universe, beyond the Solar system, led by the Caltech group.  Approximately 83% of the entire sky is being surveyed through multiple passes (crowded regions near the Galactic plane, and small areas near the celestial poles are excluded).

 

The data are preprocessed at the telescope, and transferred to LPL/UA, and hence to Caltech, for further analysis, distribution, and archiving.  The data are processed in real time, and detected transient events are published electronically through a variety of dissemination mechanisms, with no proprietary period (CRTS has a completely open data policy).

 

Further data analysis includes automated and semi-automated classification of the detected transient events, additional observations using other telescopes, scientific interpretation, and publishing.  In this process, it makes a heavy use of the archival data from a wide variety of geographically distributed resources connected through the Virtual Observatory (VO) framework.

 

Light curves (flux histories) are accumulated for ~ 500 million sources detected in the survey, each with a few hundred data points on average, spanning up to 8 years, and growing.  These are served to the community from the archives at Caltech, and shortly from IUCAA, India.  This is an unprecedented data set for the exploration of time domain in astronomy, in terms of the temporal and area coverage and depth.

 

CRTS is a scientific and methodological testbed and precursor of the grander surveys to come, notably the Large Synoptic Survey Telescope (LSST), expected to operate in 2020’s.

Current

Solutions

Compute(System)

Instrument and data processing computers: a number of desktop and small server class machines, although more powerful machinery is needed for some data analysis tasks.

 

This is not so much a computationally-intensive project, but rather a data-handling-intensive one.

Storage

Several multi-TB / tens of TB servers.

Networking

Standard inter-university internet connections.

Software

Custom data processing pipeline and data analysis software, operating under Linux.  Some archives on Windows machines, running a MS SQL server databases.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Distributed:

1.     Survey data from 3 (soon more?) telescopes

2.     Archival data from a variety of resources connected through the VO framework

3.     Follow-up observations from separate telescopes

Volume (size)

The survey generates up to ~ 0.1 TB per clear night; ~ 100 TB in current data holdings.  Follow-up observational data amount to no more than a few % of that.

Archival data in external (VO-connected) archives are in PBs, but only a minor fraction is used.

Velocity

(e.g. real time)

Up to ~ 0.1 TB / night of the raw survey data.

Variety

(multiple datasets, mashup)

The primary survey data in the form of images, processed to catalogs of sources (db tables), and time series for individual objects (light curves).

Follow-up observations consist of images and spectra.

Archival data from the VO data grid include all of the above, from a wide variety of sources and different wavelengths.

Variability (rate of change)

Daily data traffic fluctuates from ~ 0.01 to ~ 0.1 TB / day, not including major data transfers between the principal archives (Caltech, UA, and IUCAA).

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

A variety of automated and human inspection quality control mechanisms is implemented at all stages of the process.

Visualization

Standard image display and data plotting packages are used.  We are exploring visualization mechanisms for highly dimensional data parameter spaces.

Data Quality (syntax)

It varies, depending on the observing conditions, and it is evaluated automatically: error bars are estimated for all relevant quantities.

Data Types

Images, spectra, time series, catalogs.

Data Analytics

A wide variety of the existing astronomical data analysis tools, plus a large amount of custom developed tools and software, some of it a research project in itself.

Big Data Specific Challenges (Gaps)

Development of machine learning tools for data exploration, and in particular for an automated, real-time classification of transient events, given the data sparsity and heterogeneity.

 

Effective visualization of hyper-dimensional parameter spaces is a major challenge for all of us.

Big Data Specific Challenges in Mobility

Not a significant limitation at this time.

 

Security & Privacy

Requirements

None.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

·         Real-time processing and analysis of massive data streams from a distributed sensor network (in this case telescopes), with a need to identify, characterize, and respond to the transient events of interest in (near) real time.

·         Use of highly distributed archival data resources (in this case VO-connected archives) for data analysis and interpretation.

·         Automated classification given the very sparse and heterogeneous data, dynamically evolving in time as more data come in, and follow-up decision making given limited and sparse resources (in this case follow-up observations with other telescopes).

 

More Information (URLs)

CRTS survey: http://crts.caltech.edu

CSS survey: http://www.lpl.arizona.edu/css

For an overview of the classification challenges, see, e.g., http://arxiv.org/abs/1209.1681

For a broader context of sky surveys, past, present, and future, see, e.g., the review http://arxiv.org/abs/1209.1681

 

Note:

 

CRTS can be seen as a good precursor to the astronomy’s flagship project, the Large Synoptic Sky Survey (LSST; http://www.lsst.org), now under development.  Their anticipated data rates (~ 20-30 TB per clear night, tens of PB over the duration of the survey) are directly on the Moore’s law scaling from the current CRTS data rates and volumes, and many technical and methodological issues are very similar.

 

It is also a good case for real-time data mining and knowledge discovery in massive data streams, with distributed data sources and computational resources.

 

       

Figure Catalina Real-Time Transient Survey (CRTS): a digital, panoramic, synoptic sky survey

Catalina Real-Time Transient Survey (CRTS) a digital, panoramic, synoptic sky survey.jpg


Figure: One possible schematic architecture for a cyber-infrastructure for time domain astronomy.  Transient event data streams are produced by survey pipelines from the telescopes on the ground or in space, and the events with their observational descriptions are ingested by one or more depositories, from which they can be disseminated electronically to human astronomers or robotic telescopes.  Each event is assigned an evolving portfolio of information, which would include all of the available data on that celestial position, from a wide variety of data archives unidied under the Virtual Observatory framework, expert annotations, etc.  Representations of such federated information can be both human-readable and machine-readable.  They are fed into one or more automated event characterization, classification, and prioritization engines that deploy a variety of machine learning tools for these tasks.  Their output, which evolves dynamically as new information arrives and is processed, informs the follow-up observations of the selected events, and the resulting data are communicated back to the event portfolios, for the next iteration.  Users (human or robotic) can tap into the system at multiple points, both for an information retrieval, and to contribute new information, through a standardized set of formats and protocols.  This could be done in a (near) real time, or in an archival (not time critical) modes.

DOE Extreme Data from Cosmological Sky Survey and Simulations

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

DOE Extreme Data from Cosmological Sky Survey and Simulations

Vertical (area)

Scientific Research: Astrophysics

Author/Company/Email

PIs: Salman Habib, Argonne National Laboratory; Andrew Connolly, University of Washington

Actors/Stakeholders and their roles and responsibilities

Researchers studying dark matter, dark energy, and the structure of the early universe.

Goals

Clarify the nature of dark matter, dark energy, and inflation, some of the most exciting, perplexing, and challenging questions facing modern physics. Emerging, unanticipated measurements are pointing toward a need for physics beyond the successful Standard Model of particle physics.

Use Case Description

This investigation requires an intimate interplay between big data from experiment and simulation as well as massive computation. The melding of all will

1) Provide the direct means for cosmological discoveries that require a strong connection between theory and observations (‘precision cosmology’);

2) Create an essential ‘tool of discovery’ in dealing with large datasets generated by complex instruments; and,

3) Generate and share results from high-fidelity simulations that are necessary to understand and control systematics, especially astrophysical systematics.

 

Current

Solutions

Compute(System)

Hours: 24M (NERSC / Berkeley Lab), 190M (ALCF / Argonne), 10M (OLCF / Oak Ridge)

Storage

180 TB (NERSC / Berkeley Lab)

Networking

ESNet connectivity to the national labs is adequate today.

Software

MPI, OpenMP, C, C++, F90, FFTW, viz packages, python, FFTW, numpy, Boost, OpenMP, ScaLAPCK, PSQL & MySQL databases, Eigen, cfitsio, astrometry.net, and Minuit2

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Observational data will be generated by the Dark Energy Survey (DES) and the Zwicky Transient Factory in 2015 and by the Large Synoptic Sky Survey starting in 2019. Simulated data will generated at DOE supercomputing centers.

Volume (size)

DES: 4 PB, ZTF 1 PB/year, LSST 7 PB/year, Simulations > 10 PB in 2017

Velocity

(e.g. real time)

LSST: 20 TB/day

Variety

(multiple datasets, mashup)

1) Raw Data from sky surveys 2) Processed Image data 3) Simulation data

Variability (rate of change)

Observations are taken nightly; supporting simulations are run throughout the year, but data can be produced sporadically depending on access to resources

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

 

Visualization and Analytics

Interpretation of results from detailed simulations requires advanced analysis and visualization techniques and capabilities. Supercomputer I/O subsystem limitations are forcing researchers to explore “in-situ” analysis to replace post-processing methods.

Data Quality

 

Data Types

Image data from observations must be reduced and compared with physical quantities derived from simulations. Simulated sky maps must be produced to match observational formats.

Big Data Specific Challenges (Gaps)

Storage, sharing, and analysis of 10s of PBs of observational and simulated data.

Big Data Specific Challenges in Mobility

LSST will produce 20 TB of data per day. This must be archived and made available to researchers world-wide.

 

Security & Privacy

Requirements

 

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

 

 

 

More Information (URLs)

http://www.lsst.org/lsst/

http://www.nersc.gov/

http://science.energy.gov/hep/research/non-accelerator-physics/

http://www.nersc.gov/assets/Uploads/HabibcosmosimV2.pdf

 

 

 

 

Note: <additional comments>

       

Large Survey Data for Cosmology

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Large Survey Data for Cosmology

Vertical (area)

Scientific Research: Cosmic Frontier

Author/Company/Email

Peter Nugent / LBNL / penugent@lbl.gov

Actors/Stakeholders and their roles and responsibilities

Dark Energy Survey, Dark Energy Spectroscopic Instrument, Large Synoptic Survey Telescope. ANL, BNL, FNAL, LBL & SLAC: Create the instruments/telescopes, run the survey and perform the cosmological analysis. 

Goals

Provide a way to reduce photometric data in real-time for supernova discovery and follow-up and to handle the large volume of observational data (in conjunction with simulation data) to reduce systematic uncertainties in the measurement of the cosmological parameters via baryon acoustic oscillations, galaxy cluster counting and weak lensing measurements. 

Use Case Description

For DES the data are sent from the mountaintop via a microwave link to La Serena, Chile. From there, an optical link forwards them to the NCSA as well as NERSC for storage and "reduction". Subtraction pipelines are run using extant imaging data to find new optical transients through machine learning algorithms. Then galaxies and stars in both the individual and stacked images are identified, catalogued, and finally their properties measured and stored in a database.

Current

Solutions

Compute(System)

Linux cluster, Oracle RDBMS server, large memory machines, standard Linux interactive hosts. For simulations, HPC resources.

Storage

Oracle RDBMS, Postgres psql, as well as GPFS and Lustre file systems and tape archives. 

Networking

Provided by NERSC

Software

Standard astrophysics reduction software as well as Perl/Python wrapper scripts, Linux Cluster scheduling and comparison to large amounts of simulation data via techniques like Cholesky decomposition.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Distributed. Typically between observation and simulation data.

Volume (size)

 LSST will generate 60PB of imaging data and 15PB of catalog data and a correspondingly large (or larger) amount of simulation data. Over 20TB of data per night.

Velocity

(e.g. real time)

20TB of data will have to be subtracted each night in as near real-time as possible in order to maximize the science for supernovae.

Variety

(multiple datasets, mashup)

While the imaging data is similar, the analysis for the 4 different types of cosmological measurements and comparisons to simulation data is quite different.

Variability (rate of change)

Weather and sky conditions can radically change both the quality and quantity of data.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Astrophysical data is a statistician’s nightmare as the both the uncertainties in a given measurement change from night-to-night in addition to the cadence being highly unpredictable. Also, most all of the cosmological measurements are systematically limited, and thus understanding these as best possible is the highest priority for a given survey.

Visualization

Interactive speed of web UI on very large data sets is an ongoing challenge. Basic querying and browsing of data to find new transients as well as monitoring the quality of the survey is a must. Ability to download large amounts of data for offline analysis is another requirement of the system. Ability to combine both simulation and observational data is also necessary.

Data Quality

Understanding the systematic uncertainties in the observational data is a prerequisite to a successful cosmological measurement. Beating down the uncertainties in the simulation data to under this level is a huge challenge for future surveys.

Data Types

Cf. above on “Variety”

Data Analytics

 

Big Data Specific Challenges (Gaps)

New statistical techniques for understanding the limitations in simulation data would be beneficial. Often it is the case where there is not enough computing time to generate all the simulations one wants and thus there is a reliance on emulators to bridge the gaps. Techniques for handling Cholesky decompostion for thousands of simulations with matricies of order 1M on a side.

Big Data Specific Challenges in Mobility

Performing analysis on both the simulation and observational data simultaneously.

 

Security & Privacy

Requirements

No special challenges.  Data is either public or requires standard login with password.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Parallel databases which could handle imaging data would be an interesting avenue for future research.

 

 

More Information (URLs)

http://www.lsst.org/lsst, http://desi.lbl.gov, & http://www.darkenergysurvey.org

 

 

Note: <additional comments>

       

 

Particle Physics: Analysis of LHC Large Hadron Collider Data: Discovery of Higgs particle 

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Particle Physics: Analysis of LHC (Large Hadron Collider) Data (Discovery of Higgs particle)

Vertical (area)

Scientific Research: Physics

Author/Company/Email

Michael Ernst mernst@bnl.gov, Lothar Bauerdick bauerdick@fnal.gov based on an initial version written by Geoffrey Fox, Indiana University gcf@indiana.edu, Eli Dart, LBNL eddart@lbl.gov,

Actors/Stakeholders and their roles and responsibilities

Physicists(Design and Identify need for Experiment, Analyze Data) Systems Staff (Design, Build and Support distributed Computing Grid), Accelerator Physicists (Design, Build and Run Accelerator), Government (funding based on long term importance of discoveries in field))

Goals

Understanding properties of fundamental particles

Use Case Description

CERN LHC Detectors and Monte Carlo producing events describing particle-apparatus interaction. Processed information defines physics properties of events (lists of particles with type and momenta). These events are analyzed to find new effects; both new particles (Higgs) and present evidence that conjectured particles (Supersymmetry) not seen.

Current

Solutions

Compute(System)

WLCG and Open Science Grid in the US integrate computer centers worldwide that provide computing and storage resources into a single infrastructure accessible by all LHC physicists.

350,000 cores running “continuously” arranged in 3 tiers (CERN, “Continents/Countries”. “Universities”). Uses “Distributed High Throughput Computing (DHTC)”; 200PB storage, >2million jobs/day.

Storage

ATLAS:

·         Brookhaven National Laboratory Tier1 tape: 10PB ATLAS data on tape managed by HPSS (incl. RHIC/NP the total data volume is 35PB)

·         Brookhaven National Laboratory Tier1 disk: 11PB; using dCache to virtualize a set of ~60 heterogeneous storage servers with high-density disk backend systems

·         US Tier2 centers, disk cache: 16PB

CMS:

·         Fermilab US Tier1, reconstructed, tape/cache: 20.4PB

·         US Tier2 centers, disk cache: 7PB

·         US Tier3 sites, disk cache: 1.04PB

 

Networking

·         As experiments have global participants (CMS has 3600 participants from 183 institutions in 38 countries), the data at all levels is transported and accessed across continents.

·         Large scale automated data transfers occur over science networks across the globe.  LHCOPN and LHCONE network overlay provide dedicated network allocations and traffic isolation for LHC data traffic

·         ATLAS Tier1 data center at BNL has 160Gbps internal paths (often fully loaded).  70Gbps WAN connectivity provided by ESnet.

·         CMS Tier1 data center at FNAL has 90Gbps WAN connectivity provided by ESnet

·         Aggregate wide area network traffic for LHC experiments is about 25Gbps steady state worldwide

Software

The scalable ATLAS workload/workflow management system PanDA manages ~1 million production and user analysis jobs on globally distributed computing resources (~100 sites) per day.

The new ATLAS distributed data management system Rucio is the core component keeping track of an inventory of currently ~130PB of data distributed across grid resources and to orchestrate data movement between sites. The data volume is expected to grow to exascale size in the next few years. Based on the xrootd system ATLAS has developed FAX, a federated storage system that allows remote data access.

 

Similarly, CMS is using the OSG glideinWMS infrastructure to manage its workflows for production and data analysis the PhEDEx system to orchestrate data movements, and the AAA/xrootd system to allow remote data access.

 

Experiment-specific physics software including simulation packages, data processing, advanced statistic packages, etc.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

High speed detectors produce large data volumes:

·         ATLAS detector at CERN: Originally 1 PB/sec raw data rate, reduced to 300MB/sec by multi-stage trigger.

·         CMS detector at CERN: similar

Data distributed to Tier1 centers globally, which serve as data sources for Tier2 and Tier3 analysis centers

 

Volume (size)

15 Petabytes per year from Detectors and Analysis

Velocity

(e.g. real time)

·         Real time with some long LHC "shut downs" (to improve accelerator and detectors) with no data except Monte Carlo.

·         Besides using programmatically and dynamically replicated datasets, real-time remote I/O (using XrootD) is increasingly used by analysis which requires reliable high-performance networking capabilities to reduce file copy and storage system overhead

Variety

(multiple datasets, mashup)

Lots of types of events with from 2- few hundred final particle but all data is collection of particles after initial analysis. Events are grouped into datasets; real detector data is segmented into ~20 datasets (with partial overlap) on the basis of event characteristics determined through real-time trigger system, while different simulated datasets are characterized by the physics process being simulated.

Variability (rate of change)

Data accumulates and does not change character. What you look for may change based on physics insight.  As understanding of detectors increases, large scale data reprocessing tasks are undertaken.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

One can lose modest amount of data without much pain as errors proportional to 1/SquareRoot(Events gathered), but such data loss must be carefully accounted. Importance that accelerator and experimental apparatus work both well and in understood fashion. Otherwise data too "dirty" / "uncorrectable".

Visualization

Modest use of visualization outside histograms and model fits. Nice event displays but discovery requires lots of events so this type of visualization of secondary importance

Data Quality

Huge effort to make certain complex apparatus well understood (proper calibrations) and "corrections" properly applied to data. Often requires data to be re-analyzed

Data Types

Raw experimental data in various binary forms with conceptually a name: value syntax for name spanning “chamber readout” to “particle momentum”. Reconstructed data is processed to produce dense data formats optimized for analysis

Data Analytics

Initial analysis is processing of experimental data specific to each experiment (ALICE, ATLAS, CMS, LHCb) producing summary information. Second step in analysis uses “exploration” (histograms, scatter-plots) with model fits. Substantial Monte-Carlo computations are necessary to estimate analysis quality.

A large fraction (~60%) of the available CPU resources available to the ATLAS collaboration at the Tier-1 and the Tier-2 centers is used for simulated event production. The ATLAS simulation requirements are completely driven by the physics community in terms of analysis needs and corresponding physics goals. The current physics analyses are looking at real data samples of roughly 2 billion (B) events taken in 2011 and 3B events taken in 2012 (this represents ~5 PB of experimental data), and ATLAS has roughly 3.5B MC events for 2011 data, and 2.5B MC events for 2012 (this represents ~6 PB of simulated data). Given the resource requirements to fully simulate an event using the GEANT 4 package, ATLAS can currently produce about 4 million events per day using the entire processing capacity available to production worldwide.

Due to its high CPU cost, the outputs of full Geant4 simulation (HITS) are stored in one custodial tape copy on Tier1 tapes to be re-used in several Monte-Carlo re-processings. The HITS from faster simulation flavors will be only of transient nature in LHC Run 2.

Big Data Specific Challenges (Gaps)

The translation of scientific results into new knowledge, solutions, policies and decisions is foundational to the science mission associated with LHC data analysis and HEP in general. However, while advances in experimental and computational technologies have led to an exponential growth in the volume, velocity, and variety of data available for scientific discovery, advances in technologies to convert this data into actionable knowledge have fallen far short of what the HEP community needs to deliver timely and immediately impacting outcomes. Acceleration of the scientific knowledge discovery process is essential if DOE scientists are to continue making major contributions in HEP.

 

Today’s worldwide analysis engine, serving several thousand scientists, will have to be commensurately extended in the cleverness of its algorithms, the automation of the processes, and the reach (discovery) of the computing, to enable scientific understanding of the detailed nature of the Higgs boson. E.g. the approximately forty different analysis methods used to investigate the detailed characteristics of the Higgs boson (many using machine learning techniques) must be combined in a mathematically rigorous fashion to have an agreed upon publishable result.

 

Specific challenges:

Federated semantic discovery: Interfaces, protocols and environments that support access to, use of, and interoperation across federated sets of resources governed and managed by a mix of different policies and controls that interoperate across streaming and “at rest” data sources. These include: models, algorithms, libraries, and reference implementations for a distributed non-hierarchical discovery service; semantics, methods, interfaces for life-cycle management (subscription, capture, provenance, assessment, validation, rejection) of heterogeneous sets of distributed tools, services and resources; a global environment that is robust in the face of failures and outages; and flexible high-performance data stores (going beyond schema driven) that scale and are friendly to interactive analytics

 

Resource description and understanding: Distributed methods and implementations that allow resources (people, software, computing incl. data) to publish varying state and function for use by diverse clients. Mechanisms to handle arbitrary entity types in a uniform and common framework – including complex types such as heterogeneous data, incomplete and evolving information, and rapidly changing availability of computing, storage and other computational resources. Abstract data streaming and file-based data movement over the WAN/LAN and on exascale architectures to allow for real-time, collaborative decision making for scientific processes.

Big Data Specific Challenges in Mobility

The agility to use any appropriate available resources and to ensure that all data needed is dynamically available at that resource is fundamental to future discoveries in HEP. In this context “resource” has a broad meaning and includes data and people as well as computing and other non-computer based entities: thus, any kind of data—raw data, information, knowledge, etc., and any type of resource—people, computers, storage systems, scientific instruments, software, resource, service, etc. In order to make effective use of such resources, a wide range of management capabilities must be provided in an efficient, secure, and reliable manner, encompassing for example collection, discovery, allocation, movement, access, use, release, and reassignment. These capabilities must span and control large ensembles of data and other resources that are constantly changing and evolving, and will often be in-deterministic and fuzzy in many aspects.

 

Specific Challenges:

Globally optimized dynamic allocation of resources: These need to take account of the lack of strong consistency in knowledge across the entire system.

Minimization of time-to-delivery of data and services: Not only to reduce the time to delivery of the data or service but also allow for a predictive capability, so physicists working on data analysis can deal with uncertainties in the real-time decision making processes.

Security & Privacy

Requirements

While HEP data itself is not proprietary unintended alteration and/or cyber-security related facility service compromises could potentially be very disruptive to the analysis process. Besides the need of having personal credentials and the related virtual organization credential management systems to maintain access rights to a certain set of resources, a fair amount of attention needs to be devoted to the development and operation of the many software components the community needs to conduct computing in this vastly distributed environment. 

The majority of software and systems development for LHC data analysis is carried out inside the HEP community or by adopting software components from other parties which involves numerous assumptions and design decisions from the early design stages throughout its lifecycle. Software systems make a number of assumptions about their environment - how they are deployed, configured, who runs it, what sort of network is it on, is its input or output sensitive, can it trust its input, does it preserve privacy, etc.? When multiple software components are interconnected, for example in the deep software stacks used in DHTC, without clear understanding of their security assumptions, the security of the resulting system becomes an unknown.

 

A trust framework is a possible way of addressing this problem. A DHTC trust framework, by describing what software, systems and organizations provide and expect of their environment regarding policy enforcement, security and privacy, allows for a system to be analyzed for gaps in trust, fragility and fault tolerance.

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Large scale example of an event based analysis with core statistics needed. Also highlights importance of virtual organizations as seen in global collaboration.

The LHC experiments are pioneers of distributed Big Data science infrastructure, and several aspects of the LHC experiments’ workflow highlight issues that other disciplines will need to solve.  These include automation of data distribution, high performance data transfer, and large-scale high-throughput computing.

More Information (URLs)

http://grids.ucs.indiana.edu/ptliupages/publications/ Where%20does%20all%20the%20data%20come%20from%20v7.pdf

http://www.es.net/assets/pubs_presos/High-throughput-lessons-from-the-LHC-experience.Johnston.TNC2013.pdf

 

Note: <additional comments>

       

 

Use Case Stages

Data Sources

Data Usage

Transformations
(Data Analytics)

Infrastructure

Security
& Privacy

Particle Physics: Analysis of LHC Large Hadron Collider Data, Discovery of Higgs particle (Scientific Research: Physics)

Record Raw Data

CERN LHC Accelerator

This data is staged at CERN and then distributed across the globe for next stage in processing

LHC has 109 collisions per second; the hardware + software trigger selects “interesting events”. Other utilities distribute data across the globe with fast transport

Accelerator and sophisticated data selection (trigger process) that uses ~7000 cores at CERN to record ~100-500 events each second (~1 megabyte each)

N/A

Process Raw Data to Information

Disk Files of Raw Data

Iterative calibration and checking of analysis which has for example “heuristic” track finding algorithms.

Produce “large” full physics files and stripped down Analysis Object Data (AOD) files that are ~10% original size

Full analysis code that builds in complete understanding of complex experimental detector.

Also Monte Carlo codes to produce simulated data to evaluate efficiency of experimental detection.

~300,000 cores arranged in 3 tiers.

Tier 0: CERN

Tier 1: “Major Countries”

Tier 2: Universities and laboratories.

 

Note processing is compute and data intensive

N/A

Physics Analysis

Information to Knowledge/Discovery

Disk Files of Information including accelerator and Monte Carlo data.

 

Include wisdom from lots of physicists (papers) in analysis choices

Use simple statistical techniques (like histogramming,

multi-variate analysis methods and other data analysis techniques and model fits to discover new effects (particles) and put limits on effects not seen

Data reduction and processing steps with advanced physics algorithms to identify event properties, particle hypothesis etc. For interactive data analysis of those reduced and selected data sets the classic program is Root from CERN that reads multiple event (AOD, NTUP) files from selected data sets and use physicist generated C++ code to calculate new quantities such as implied mass of an unstable (new) particle

While the bulk of data processing  is done at Tier 1 and Tier 2 resources, the end stage analysis is usually done by users at a local Tier 3 facility. The scale of computing resources at Tier 3 sites range from workstations to small clusters. ROOT is the most common software stack used to analyze compact data formats generated on distributed computing resources. Data transfer is done using ATLAS and CMS DDM tools, which mostly rely on gridFTP middleware. XROOTD based direct data access is also gaining importance wherever high network bandwidth is available.

 

Physics discoveries and results are confidential until certified by group and presented at meeting/journal. Data preserved so results reproducible

Figure 1 The LHC Collider location at CERN

Figure 1 The LHC Collider location at CERN.jpg

Figure 2 The Multi-tier LHC computing infrastructure

Figure 2 The Multi-tier LHC computing infrastructure.jpg

Belle II High Energy Physics Experiment

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Belle II Experiment

Vertical (area)

Scientific Research: High Energy Physics

Author/Company/Email

David Asner & Malachi Schram, PNNL, david.asner@pnnl.gov & malachi.schram@pnnl.gov

Actors/Stakeholders and their roles and responsibilities

David Asner is the Chief Scientist for the US Belle II Project

Malachi Schram is Belle II network and data transfer coordinator and the PNNL Belle II computing center manager

Goals

Perform precision measurements to search for new phenomena beyond the Standard Model of Particle Physics

Use Case Description

Study numerous decay modes at the Upsilon(4S) resonance to search for new phenomena beyond the Standard Model of Particle Physics

Current

Solutions

Compute(System)

Distributed (Grid computing using DIRAC)

Storage

Distributed (various technologies)

Networking

Continuous RAW data transfer of ~20Gbps at designed luminosity between Japan and US

Additional transfer rates are currently being investigated

Software

Open Science Grid, Geant4, DIRAC, FTS, Belle II framework

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Distributed data centers

Primary data centers are in Japan (KEK) and US (PNNL)

Volume (size)

Total integrated RAW data ~120PB and physics data ~15PB and ~100PB MC samples

Velocity

(e.g. real time)

Data will be re-calibrated and analyzed incrementally

Data rates will increase based on the accelerator luminosity

Variety

(multiple datasets, mashup)

Data will be re-calibrated and distributed incrementally.

Variability (rate of change)

Collisions will progressively increase until the designed luminosity is reached (3000 BB pairs per sec).

Expected event size is ~300kB per events.        

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Validation will be performed using known reference physics processes

Visualization

N/A

Data Quality

Output data will be re-calibrated and validated incrementally

Data Types

Tuple based output

Data Analytics

Data clustering and classification is an integral part of the computing model. Individual scientists define event level analytics.

Big Data Specific Challenges (Gaps)

Data movement and bookkeeping (file and event level meta-data).

Big Data Specific Challenges in Mobility

Network infrastructure required for continuous data transfer between Japan (KEK) and US (PNNL).

Security & Privacy

Requirements

No special challenges.  Data is accessed using grid authentication.

Highlight issues for generalizing this use case (e.g. for ref. architecture)

 

 

 

More Information (URLs)

http://belle2.kek.jp

Note: <additional comments>

       

Earth, Environmental and Polar Science

EISCAT 3D incoherent scatter radar system

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

EISCAT 3D incoherent scatter radar system

Vertical (area)

Environmental Science

Author/Company/Email

Yin Chen /Cardiff University/ chenY58@cardiff.ac.uk

Ingemar Häggström, Ingrid Mann, Craig Heinselman/

EISCAT Science Association/  {Ingemar.Haggstrom, Ingrid.mann, Craig.Heinselman}@eiscat.se

Actors/Stakeholders and their roles and responsibilities

The EISCAT Scientific Association is an international research organisation operating incoherent scatter radar systems in Northern Europe. It is funded and operated by research councils of Norway, Sweden, Finland, Japan, China and the United Kingdom (collectively, the EISCAT Associates). In addition to the incoherent scatter radars, EISCAT also operates an Ionospheric Heater facility, as well as two Dynasondes.

Goals

EISCAT, the European Incoherent Scatter Scientific Association, is established to conduct research on the lower, middle and upper atmosphere and ionosphere using the incoherent scatter radar technique. This technique is the most powerful ground-based tool for these research applications. EISCAT is also being used as a coherent scatter radar for studying instabilities in the ionosphere, as well as for investigating the structure and dynamics of the middle atmosphere and as a diagnostic instrument in ionospheric modification experiments with the Heating facility.

Use Case Description

The design of the next generation incoherent scatter radar system, EISCAT_3D, opens up opportunities for physicists to explore many new research fields. On the other hand, it also introduces significant challenges in handling large-scale experimental data which will be massively generated at great speeds and volumes. This challenge is typically referred to as a big data problem and requires solutions from beyond the capabilities of conventional database technologies.

Current

Solutions

Compute(System)

EISCAT 3D data e-Infrastructure plans to use the high performance computers for central site data processing and high throughput computers for mirror sites data processing

Storage

32TB

Networking

The estimated data rates in local networks at the active site run from 1 Gb/s to 10 Gb/s. Similar capacity is needed to connect  the sites through dedicated high-speed network links. Downloading the full data is not time critical, but operations require real-time information about certain pre-defined events to be sent from the sites to the operation centre and a real-time link from the operation centre to the sites to set the mode of radar operation on with immediate action.

Software

·         Mainstream operating systems, e.g., Windows, Linux, Solaris, HP/UX, or FreeBSD

·         Simple, flat file storage with required capabilities e.g., compression, file striping and file journaling

·         Self-developed software

o    Control & monitoring tools including, system configuration, quick-look, fault reporting, etc.

o    Data dissemination utilities

o    User software e.g., for cyclic buffer, data cleaning, RFI detection and excision, auto-correlation, data integration, data analysis, event identification, discovery & retrieval, calculation of value-added data products, ingestion/extraction, plot

o    User-oriented computing

o    APIs into standard software environments

o    Data processing chains and workflow

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

EISCAT_3D will consist of a core site with a transmitting and receiving radar arrays and four sites with receiving antenna arrays at some 100 km from the core.

Volume (size)

·         The fully operational 5-site system will generate 40 PB/year in 2022.

·         It is expected to operate for 30 years, and data products to be stored at less 10 years

Velocity

(e.g. real time)

At each of 5-receiver-site:

·         each antenna generates 30 Msamples/s (120MB/s);

·         each antenna group (consists of 100 antennas) to form beams at speed of 2 Gbit/s/group;

·         these data are temporary stored in a ringbuffer: 160 groups ->125 TB/h.

Variety

(multiple datasets, mashup)

·         Measurements: different versions, formats, replicas, external sources ...

·         System information: configuration, monitoring, logs/provenance ...

·         Users’ metadata/data: experiments, analysis, sharing, communications …

Variability (rate of change)

In time, instantly, a few ms. 

Along the radar beams, 100ns.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

·         Running 24/7, EISCAT_3D have very high demands on robustness.

·         Data and performance assurance is vital for the ring-buffer and archive systems. These systems must be able to guarantee to meet minimum data rate acceptance at all times or scientific data will be lost.

·         Similarly the systems must guarantee that data held is not volatile or corrupt. This latter requirement is particularly vital at the permanent archive where data is most likely to be accessed by scientific users and least easy to check; data corruption here has a significant possibility of being non-recoverable and of poisoning the scientific literature.

Visualization

·         Real-time visualisation of analysed data, e.g., with a figure of updating panels showing electron density, temperatures and ion velocity to those data for each beam.

·         non-real-time (post-experiment) visualisation of the physical parameters of interest, e.g.,

o    by standard plots,

o    using three-dimensional block to show to spatial variation (in the user selected cuts),

o    using animations to show the temporal variation,

o    allow the visualisation of 5 or higher dimensional data, e.g., using the 'cut up and stack' technique to reduce the dimensionality, that is take one or more independent coordinates as discrete; or volume rendering technique to display a 2D projection of a 3D discretely sampled data set.

·         (Interactive) Visualisation. E.g., to allow users to combine the information on several spectral features, e.g., by using colour coding, and to provide real-time visualisation facility to allow the users to link or plug in tailor-made data visualisation functions, and more importantly functions to signal for special observational conditions.

Data Quality

·         Monitoring software will be provided which allows The Operator to see incoming data via the Visualisation system in real-time and react appropriately to scientifically interesting events.  

·         Control software will be developed to time-integrate the signals and reduce the noise variance and the total data throughput of the system that reached the data archive.

Data Types

HDF-5

Data Analytics

Pattern recognition, demanding correlation routines, high level parameter extraction

Big Data Specific Challenges (Gaps)

·         High throughput of data for reduction into higher levels.

·         Discovery of meaningful insights from low-value-density data needs new approaches to the deep, complex analysis e.g., using machine learning, statistical modelling, graph algorithms etc. which go beyond traditional approaches to the space  physics.

Big Data Specific Challenges in Mobility

Is not likely in mobile platforms

 

Security & Privacy

Requirements

Lower level of data has restrictions for 1 year within the associate countries. All data open after 3 years.

Highlight issues for generalizing this use case (e.g. for ref. architecture)

EISCAT 3D data e-Infrastructure shares similar architectural characteristics with other ISR radars, and many existing big data systems, such as  LOFAR, LHC, and SKA

 

 

More Information (URLs)

https://www.eiscat3d.se/

Note: <additional comments>

       

Figure EISCAT 3D incoherent scatter radar system

EISCAT 3D incoherent scatter radar system.png

ENVRI, Common Operations of Environmental Research Infrastructure

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

ENVRI, Common Operations of Environmental Research Infrastructure

Vertical (area)

Environmental Science 

Author/Company/Email

Yin Chen/ Cardiff University/ ChenY58@cardiff.ac.uk

Actors/Stakeholders and their roles and responsibilities

The ENVRI project is a collaboration conducted within the European Strategy Forum on Research Infrastructures (ESFRI) Environmental Cluster.  The ESFRI Environmental research infrastructures involved in ENVRI including:

·         ICOS is a European distributed infrastructure dedicated to the monitoring of greenhouse gases (GHG) through its atmospheric, ecosystem and ocean networks.

·         EURO-Argo is the European contribution to Argo, which is a global ocean observing system.

·         EISCAT-3D is a European new-generation incoherent-scatter research radar for upper atmospheric science.

·         LifeWatch is an e-science Infrastructure for biodiversity and ecosystem research.

·         EPOS is a European Research Infrastructure on earthquakes, volcanoes, surface dynamics and tectonics. 

·         EMSO is a European network of seafloor observatories for the long-term monitoring of environmental processes related to ecosystems, climate change and geo-hazards.

ENVRI also maintains close contact with the other not-directly involved ESFRI Environmental research infrastructures by inviting them for joint meetings. These projects are:

·         IAGOS                    Aircraft for global observing system

·         SIOS                      Svalbard arctic Earth observing system

ENVRI IT community provides common policies and technical solutions for the research infrastructures,  which involves a number of organization partners including, Cardiff University, CNR-ISTI, CNRS (Centre National de la Recherche Scientifique), CSC, EAA (Umweltbundesamt Gmbh), EGI, ESA-ESRIN, University of Amsterdam, and University of Edinburgh.

Goals

The ENVRI project gathers 6 EU ESFRI environmental science infra-structures (ICOS, EURO-Argo, EISCAT-3D, LifeWatch, EPOS, and EMSO) in order to develop common data and software services. The results will accelerate the construction of these infrastructures and improve interoperability among them.

    The primary goal of ENVRI is to agree on a reference model for joint operations. The ENVRI Reference Model (ENVRI RM) is a common ontological framework and standard for the description and characterisation of computational and storage infrastructures in order to achieve seamless interoperability between the heterogeneous resources of different infrastructures. The ENVRI RM serves as a common language for community communication, providing a uniform framework into which the infrastructure’s components can be classified and compared, also serving to identify common solutions to common problems. This may enable  reuse, share of resources and experiences, and avoid duplication of efforts.

Use Case Description

ENVRI project implements harmonised solutions and draws up guidelines for the common needs of the environmental ESFRI projects, with a special focus on issues as architectures, metadata frameworks, data discovery in scattered repositories, visualisation and data curation. This will empower the users of the collaborating environmental research infrastructures and enable multidisciplinary scientists to access, study and correlate data from multiple domains for "system level" research.

        ENVRI investigates a collection of representative research infrastructures for environmental sciences, and provides a projection of Europe-wide requirements they have; identifying in particular, requirements they have in common. Based on the analysis evidence, the ENVRI Reference Model (http://www.envri.eu/rm) is developed using ISO standard Open Distributed Processing. Fundamentally the model serves to provide a universal reference framework for discussing many common technical challenges facing all of the ESFRI-environmental research infrastructures. By drawing analogies between the reference components of the model and the actual elements of the infrastructures (or their proposed designs) as they exist now, various gaps and points of overlap can be identified.

Current

Solutions

Compute(System)

 

Storage

File systems and relational databases

Networking

 

Software

Own

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Most of the ENVRI Research Infrastructures (ENV RIs) are distributed, long-term, remote controlled observational networks focused on understanding processes, trends, thresholds, interactions and feedbacks and increasing the predictive power to address future environmental challenges. They are spanning from the Arctic areas to the European Southernmost areas and from Atlantic on west to the Black Sea on east. More precisely:

·         EMSO, network of fixed-point, deep-seafloor and water column observatories, is geographically distributed in key sites of European waters, presently consisting of thirteen sites.

·         EPOS aims at integrating the existing European facilities in solid Earth science into one coherent multidisciplinary RI, and to increase the accessibility and usability of multidisciplinary data from seismic and geodetic monitoring networks, volcano observatories, laboratory experiments and computational simulations enhancing worldwide interoperability in Earth Science.

·         ICOS dedicates to the monitoring of greenhouse gases (GHG) through its atmospheric, ecosystem and ocean networks. The ICOS network includes more than 30 atmospheric and more than 30 ecosystem primary long term sites located across Europe, and additional secondary sites. It also includes three Thematic Centres to process the data from all the stations from each network, and provide access to these data.

·         LifeWatch is a “virtual” infrastructure for biodiversity and ecosystem research with services mainly provided through the Internet. Its Common Facilities is coordinated and managed at a central European level; and the LifeWatch Centres serve as specialized facilities from member countries (regional partner facilities) or research communities.

·         Euro-Argo provides, deploys and operates an array of around 800 floats contributing to the global array (3,000 floats) and thus provide enhanced coverage in the European regional seas.

·         EISCAT- 3D, makes continuous measurements of the geospace environment and its coupling to the Earth's atmosphere from its location in the auroral zone at the southern edge of the northern polar vortex, and is a distributed infrastructure.

Volume (size)

Variable data size. e.g.,

·         The amount of data within the EMSO is depending on the instrumentation and configuration of the observatory between several MBs to several GB per data set.

·         Within EPOS, the EIDA network is currently providing access to continuous raw data coming from approximately more than 1000 stations recording about 40GB per day, so over 15 TB per year. EMSC stores a Database of 1.85 GB of earthquake parameters, which is constantly growing and updated with refined information.

-       222705 – events

-       632327 – origins

-       642555 – magnitudes

·         Within EISCAT 3D raw voltage data will reach 40PB/year in 2023.

Velocity

(e.g. real time)

Real-time data handling is a common request of the environmental research infrastructures

Variety

(multiple datasets, mashup)

Highly complex and heterogeneous

Variability (rate of change)

Relative low rate of change

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Normal

Visualization

Most of the projects have not yet developed the visualization technique to be fully operational.

·         EMSO is not yet fully operational, currently only simple graph plotting tools.

·         Visualization techniques are not yet defined for EPOS.

·         Within ICOS Level-1.b data products such as near real time GHG measurements are available to users via ATC web portal. Based on Google Chart Tools, an interactive time series line chart with optional annotations allows user to scroll and zoom inside a time series of CO2 or CH4 measurement at an ICOS Atmospheric station. The chart is rendered within the browser using Flash. Some Level-2 products are also available to ensure instrument monitoring to PIs. It is mainly instrumental and comparison data plots automatically generated (R language & Python Matplotlib 2D plotting library) and daily pushed on ICOS web server. Level-3 data products such as gridded GHG fluxes derived from ICOS observations increase the scientific impact of ICOS. For this purpose ICOS supports its community of users. The Carbon portal is expected to act as a platform that will offer visualization of the flux products that incorporate ICOS data. Example of candidate Level-3 products from future ICOS GHG concentration data are for instance maps of European high-resolution CO2 or CH4 fluxes obtained by atmospheric inversion modelers in Europe. Visual tools for comparisons between products will be developed by the Carbon Portal. Contributions will be open to any product of high scientific quality.

·         LifeWatch will provide common visualization techniques, such as the plotting of species on maps. New techniques will allow visualizing the effect of changing data and/or parameters in models.

Data Quality (syntax)

Highly important

Data Types

·         Measurements (often in file formats),

·         Metadata,

·         Ontology,

·         Annotations

Data Analytics

·         Data assimilation,

·         (Statistical) analysis,

·         Data mining,

·         Data extraction,

·         Scientific modeling and simulation,

·         Scientific workflow

Big Data Specific Challenges (Gaps)

·         Real-time handling of extreme high volume of data

·         Data staging to mirror archives

·         Integrated Data access and discovery

·         Data processing and analysis  

Big Data Specific Challenges in Mobility

The need for efficient and high performance mobile detectors and instrumentation is common:

·         In ICOS,  various mobile instruments are used to collect data from marine observations, atmospheric observations, and ecosystem monitoring.

·         In Euro-Argo, thousands of submersible robots to obtain observations of all of the oceans

·         In Lifewatch, biologists use mobile instruments for observations and measurements.

Security & Privacy

Requirements

Most of the projects follow the open data sharing policy. E.g.,

·         The vision of EMSO is to allow scientists all over the world to access observatories data following an open access model.

·         Within EPOS, EIDA data and Earthquake parameters are generally open and free to use. Few restrictions are applied on few seismic networks and the access is regulated depending on email based authentication/authorization.

·         The ICOS data will be accessible through a license with full and open access. No particular restriction in the access and eventual use of the data is anticipated, expected the inability to redistribute the data. Acknowledgement of ICOS and traceability of the data will be sought in a specific, way (e.g. DOI of dataset). A large part of relevant data and resources are generated using public funding from national and international sources.

·         LifeWatch is following the appropriate European policies, such as: the European Research Council (ERC) requirement; the European Commission’s open access pilot mandate in 2008. For publications, initiatives such as Dryad instigated by publishers and the Open Access Infrastructure for Research in Europe (OpenAIRE). The private sector may deploy their data in the LifeWatch infrastructure. A special company will be established to manage such commercial contracts.

·         In EISCAT 3D, lower level of data has restrictions for 1 year within the associate countries. All data open after 3 years.

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Different research infrastructures are designed for different purposes and evolve over time. The designers describe their approaches from different points of view, in different levels of detail and using different typologies. The documentation provided is often incomplete and inconsistent. What is needed is a uniform platform for interpretation and discussion, which helps to unify understanding.

In ENVRI, we choose to use a standard model, Open Distributed Processing (ODP), to interpret the design of the research infrastructures, and place their requirements into the ODP framework for further analysis and comparison.

More Information (URLs)

·         ENVRI Project website: www.envri.eu

·         ENVRI Reference Model www.envri.eu/rm

·         ENVRI deliverable D3.2 : Analysis of common requirements of Environmental Research Infrastructures

·         ICOS: http://www.icos-infrastructure.eu/

·         Euro-Argo: http://www.euro-argo.eu/

·         EISCAT 3D: http://www.eiscat3d.se/

·         LifeWatch: http://www.lifewatch.com/

·         EPOS: http://www.epos-eu.org/

·         EMSO http://www.emso-eu.org/management/

Note: <additional comments>

       


Figure 1: ENVRI Common Subsystems

Figure 1 ENVRI Common Subsystems.png

Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (A) ICOS Architecture

Figure 2 Architectures of the ESFRI Environmental Research Infrastructures (A) ICOS Architecture.png

Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (B) LifeWatch Architecture

Figure 2 Architectures of the ESFRI Environmental Research Infrastructures (B) LifeWatch Architecture.png

Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (C) EMSO Architecture

Figure 2 Architectures of the ESFRI Environmental Research Infrastructures (C) EMSO Architecture.png

Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (D) Eura-Argo Architecture

Figure 2 Architectures of the ESFRI Environmental Research Infrastructures (D) Eura-Argo Architecture.png

Figure 2: Architectures of the ESFRI Environmental Research Infrastructures (E) EISCAT 3D Architecture

Figure 2 Architectures of the ESFRI Environmental Research Infrastructures (E) EISCAT 3D Architecture.png

Radar Data Analysis for CReSIS

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Radar Data Analysis for CReSIS

Vertical (area)

Scientific Research: Polar Science and Remote Sensing of Ice Sheets

Author/Company/Email

Geoffrey Fox, Indiana University gcf@indiana.edu

Actors/Stakeholders and their roles and responsibilities

Research funded by NSF and NASA with relevance to near and long term climate change. Engineers designing novel radar with “field expeditions” for 1-2 months to remote sites. Results used by scientists building models and theories involving Ice Sheets

Goals

Determine the depths of glaciers and snow layers to be fed into higher level scientific analyses

 

Use Case Description

Build radar; build UAV or use piloted aircraft; overfly remote sites (Arctic, Antarctic, Himalayas). Check in field that experiments configured correctly with detailed analysis later. Transport data by air-shipping disk as poor Internet connection. Use image processing to find ice/snow sheet depths. Use depths in scientific discovery of melting ice caps etc.

Current

Solutions

Compute(System)

Field is a low power cluster of rugged laptops plus classic 2-4 CPU servers with ~40 TB removable disk array. Off line is about 2500 cores

Storage

Removable disk in field. (Disks suffer in field so 2 copies made) Lustre or equivalent for offline

Networking

Terrible Internet linking field sites to continental USA.

Software

Radar signal processing in Matlab. Image analysis is MapReduce or MPI plus C/Java. User Interface is a Geographical Information System

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Aircraft flying over ice sheets in carefully planned paths with data downloaded to disks.

Volume (size)

~0.5 Petabytes per year raw data

Velocity

(e.g. real time)

All data gathered in real time but analyzed incrementally and stored with a GIS interface

Variety

(multiple datasets, mashup)

Lots of different datasets – each needing custom signal processing but all similar in structure. This data needs to be used with wide variety of other polar data.

Variability (rate of change)

Data accumulated in ~100 TB chunks for each expedition

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Essential to monitor field data and correct instrumental problems. Implies must analyze fully portion of data in field

Visualization

Rich user interface for layers and glacier simulations

Data Quality

Main engineering issue is to ensure instrument gives quality data

Data Types

Radar Images

Data Analytics

Sophisticated signal processing; novel new image processing to find layers (can be 100’s one per year)

Big Data Specific Challenges (Gaps)

Data volumes increasing. Shipping disks clumsy but no other obvious solution. Image processing algorithms still very active research

Big Data Specific Challenges in Mobility

Smart phone interfaces not essential but LOW power technology essential in field

 

Security & Privacy

Requirements

Himalaya studies fraught with political issues and require UAV. Data itself open after initial study

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Loosely coupled clusters for signal processing. Must support Matlab.

 

 

More Information (URLs)

http://polargrid.org/polargrid

https://www.cresis.ku.edu/

See movie at http://polargrid.org/polargrid/gallery

 

Note: <additional comments>

       

 

Use Case Stages

Data Sources

Data Usage

Transformations
(Data Analytics)

Infrastructure

Security
& Privacy

Radar Data Analysis for CReSIS (Scientific Research: Polar Science and Remote Sensing of Ice Sheets)

Raw Data: Field Trip

Raw Data from Radar instrument on Plane/Vehicle

Capture Data on Disks for L1B.

Check Data to monitor instruments.

Robust Data Copying Utilities.

Version of Full Analysis to check data.

Rugged Laptops with small server (~2 CPU with ~40TB removable disk system)

N/A

Information:

Offline Analysis L1B

Transported Disks copied to (LUSTRE) File System

Produce processed data  as radar images

Matlab Analysis code running in parallel and independently on each data sample

~2500 cores running standard cluster tools

N/A except results checked before release on CReSIS web site

Information:

L2/L3 Geolocation & Layer Finding

Radar Images from L1B

Input to Science as database with GIS frontend

GIS and Metadata Tools

Environment to support automatic and/or manual layer determination

GIS (Geographical Information System).

Cluster for Image Processing.

As above

Knowledge, Wisdom, Discovery:

Science

GIS interface to L2/L3 data

Polar Science Research integrating multiple data sources e.g. for Climate change.

Glacier bed data used in simulations of glacier flow

 

Exploration on a cloud style GIS supporting access to data.

Simulation is 3D partial differential equation solver on large cluster.

Varies according to science use. Typically results open after research complete.

Figure 1: Typical Radar Data after analysis

Figure 1 Typical Radar Data after analysis.png

Figure 2: Typical flight paths of data gathering in survey region

Figure 2 Typical flight paths of data gathering in survey region.png

Figure 3. Typical echogram with Detected Boundaries.  The upper (green) boundary is between air and ice layer while the lower (red) boundary is between ice and terrain

Figure 3 Typical echogram with Detected Boundaries.  The upper (green) boundary is between air and ice layer while the lower (red) boundary is between ice and terrain.jpg

UAVSAR Data Processing, Data Product Delivery, and Data Services

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

UAVSAR Data Processing, Data Product Delivery, and Data Services

Vertical (area)

Scientific Research: Earth Science

Author/Company/Email

Andrea Donnellan, NASA JPL, andrea.donnellan@jpl.nasa.gov; Jay Parker, NASA JPL, jay.w.parker@jpl.nasa.gov

Actors/Stakeholders and their roles and responsibilities

NASA UAVSAR team, NASA QuakeSim team, ASF (NASA SAR DAAC), USGS, CA Geological Survey

Goals

Use of Synthetic Aperture Radar (SAR) to identify landscape changes caused by seismic activity, landslides, deforestation, vegetation changes, flooding, etc; increase its usability and accessibility by scientists.

Use Case Description

A scientist who wants to study the after effects of an earthquake examines multiple standard SAR products made available by NASA.  The scientist may find it useful to interact with services provided by intermediate projects that add value to the official data product archive.

Current

Solutions

Compute(System)

Raw data processing at NASA AMES Pleiades, Endeavour.  Commercial clouds for storage and service front ends have been explored.

Storage

File based.

Networking

Data require one time transfers between instrument and JPL, JPL and other NASA computing centers (AMES), and JPL and ASF. 

 

Individual data files are not too large for individual users to download, but entire data set is unwieldy to transfer. This is a problem to downstream groups like QuakeSim who want to reformat and add value to data sets.

Software

ROI_PAC, GeoServer, GDAL, GeoTIFF-supporting tools.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Data initially acquired by unmanned aircraft.  Initially processed at NASA JPL.  Archive is centralized at ASF (NASA DAAC).  QuakeSim team maintains separate downstream products (GeoTIFF conversions).

Volume (size)

Repeat Pass Interferometry (RPI) Data: ~ 3 TB.  Increasing about 1-2 TB/year.

 

Polarimetric Data: ~40 TB (processed)

 

Raw Data: 110 TB

 

Proposed satellite missions (Earth Radar Mission, formerly DESDynI) could dramatically increase data volumes (TBs per day).

Velocity

(e.g. real time)

RPI Data: 1-2 TB/year.  Polarimetric data is faster.

Variety

(multiple datasets, mashup)

Two main types: Polarimetric and RPI.  Each RPI product is a collection of files (annotation file, unwrapped, etc).   Polarimetric products also consist of several files each.

Variability (rate of change)

Data products change slowly. Data occasionally get reprocessed: new processing methods or parameters.   There may be additional quality assurance and quality control issues.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Provenance issues need to be considered.  This provenance has not been transparent to downstream consumers in the past.  Versioning used now; versions described in the UAVSAR web page in notes. 

Visualization

Uses Geospatial Information System tools, services, standards.

Data Quality (syntax)

Many frames and collections are found to be unusable due to unforseen flight conditions.

Data Types

GeoTIFF and related imagery data

Data Analytics

Done by downstream consumers (such as edge detections): research issues.

Big Data Specific Challenges (Gaps)

Data processing pipeline requires human inspection and intervention. Limited downstream data pipelines for custom users.

Cloud architectures for distributing entire data product collections to downstream consumers should be investigated, adopted.

Big Data Specific Challenges in Mobility

Some users examine data in the field on mobile devices, requiring interactive reduction of large data sets to understandable images or statistics.

Security & Privacy

Requirements

Data is made immediately public after processing (no embargo period). 

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Data is geolocated, and may be angularly specified. Categories:  GIS; standard instrument data processing pipeline to produce standard data products.

 

More Information (URLs)

http://uavsar.jpl.nasa.gov/, http://www.asf.alaska.edu/program/sdc, http://quakesim.org

Note: <additional comments>

       

NASA LARC/GSFC iRODS Federation Testbed

NBD(NIST Big Data) Requirements WG Use Case Aug 15 2013

Use Case Title

NASA LARC/GSFC iRODS Federation Testbed

Vertical (area)

Earth Science Research and Applications

Author/Company/Email

Michael Little, Roger Dubois, Brandi Quam, Tiffany Mathews, Andrei Vakhnin, Beth Huffer, Christian Johnson / NASA Langley Research Center (LaRC) / M.M.Little@NASA.gov, Roger.A.Dubois@nasa.gov, Brandi.M.Quam@NASA.gov, Tiffany.J.Mathews@NASA.gov, & Andrei.A.Vakhnin@NASA.gov

 

John Schnase,,Daniel Duffy, Glenn Tamkin, Scott Sinno, John Thompson, & Mark McInerney / NASA Goddard Space Flight Center (GSFC) / John.L.Schnase@NASA.gov, Daniel.Q.Duffy@NASA.gov, Glenn.S.Tamkin@nasa.gov. Scott.S.Sinno@nasa.gov, John.H.Thompson@nasa.gov, & Mark.Mcinerney@nasa.gov

Actors/Stakeholders and their roles and responsibilities

NASA’s Atmospheric Science Data Center (ASDC) at Langley Research Center (LaRC) in Hampton, Virginia, and the Center for Climate Simulation (NCCS) at Goddard Space Flight Center (GSFC) both ingest, archive, and distribute data that is essential to stakeholders including the climate research community, science applications community, and a growing community of government and private-sector customers who have a need for atmospheric and climatic data.

Goals

To implement a data federation ability to improve and automate the discovery of heterogeneous data, decrease data transfer latency, and meet customizable criteria based on data content, data quality, metadata, and production.

To support/enable applications and customers that require the integration of multiple heterogeneous data collections.

Use Case Description

ASDC and NCCS have complementary data sets, each containing vast amounts of data that is not easily shared and queried. Climate researchers, weather forecasters, instrument teams, and other scientists need to access data from across multiple datasets in order to compare sensor measurements from various instruments, compare sensor measurements to model outputs, calibrate instruments, look for correlations across multiple parameters, etc. To analyze, visualize and otherwise process data from heterogeneous datasets is currently a time consuming effort that requires scientists to separately access, search for, and download data from multiple servers and often the data is duplicated without an understanding of the authoritative source. Many scientists report spending more time in accessing data than in conducting research. Data consumers need mechanisms for retrieving heterogeneous data from a single point-of-access. This can be enabled through the use of iRODS, a Data grid software system that enables parallel downloads of datasets from selected replica servers that can be geographically dispersed, but still accessible by users worldwide. Using iRODS in conjunction with semantically enhanced metadata, managed via a highly precise Earth Science ontology, the ASDC’s Data Products Online (DPO) will be federated with the data at the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center (GSFC). The heterogeneous data products at these two NASA facilities are being semantically annotated using common concepts from the NASA Earth Science ontology. The semantic annotations will enable the iRODS system to identify complementary datasets and aggregate data from these disparate sources, facilitating data sharing between climate modelers, forecasters, Earth scientists, and scientists from other disciplines that need Earth science data. The iRODS data federation system will also support cloud-based data processing services in the Amazon Web Services (AWS) cloud.

Current

Solutions

Compute (System)

NASA Center for Climate Simulation (NCCS) and
NASA Atmospheric Science Data Center (ASDC): Two GPFS systems

Storage

The ASDC’s Data Products Online (DPO) GPFS File system consists of 12 x IBM DC4800 and 6 x IBM DCS3700 Storage subsystems, 144 Intel 2.4 GHz cores, 1,400 TB usable storage. NCCS data is stored in the NCCS MERRA cluster, which is a 36 node Dell cluster, 576 Intel 2.6 GHz SandyBridge cores, 1,300 TB raw storage, 1,250 GB RAM, 11.7 TF theoretical peak compute capacity.

Networking

A combination of Fibre Channel SAN and 10GB LAN. The NCCS cluster nodes are connected by an FDR Infiniband network with peak TCP/IP speeds >20 Gbps.

Software

SGE Univa Grid Engine Version 8.1, iRODS version 3.2 and/or 3.3, IBM Global Parallel File System (GPFS) version 3.4, Cloudera version 4.5.2-1.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

iRODS will be leveraged to share data collected from CERES Level 3B data products including: CERES EBAF-TOA and CERES-Surface products.

Surface fluxes in EBAF-Surface are derived from two CERES data products: 1) CERES SYN1deg-Month Ed3 - which provides computed surface fluxes to be adjusted and 2) CERES EBAFTOA Ed2.7 – which uses observations to provide CERES-derived TOA flux constraints. Access to these products will enable the NCCS at GSFC to run data from the products in a simulation model in order to produce an assimilated flux.

The NCCS will introduce Modern-Era Retrospective Analysis for Research and Applications (MERRA) data to the iRODS federation. MERRA integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. MERRA data files are created from the Goddard

Earth Observing System version 5 (GEOS-5) model and are stored in HDF-EOS and (Network Common Data Form) NetCDF formats.

Spatial resolution is 1/2 ̊ latitude × 2/3 ̊ longitude ×

72 vertical levels extending through the stratosphere. Temporal resolution is 6-hours for three-dimensional, full spatial resolution, extending from 1979-present, nearly the entire satellite era.

Each file contains a single grid with multiple 2D and

3D variables. All data are stored on a longitude-latitude grid with a vertical dimension applicable for all 3D variables. The GEOS-5 MERRA products are divided into 25 collections: 18 standard products, chemistry products. The collections comprise monthly means files and daily files at six-hour intervals running from 1979 – 2012. MERRA data are typically packaged as multi-dimensional binary data within a self-describing NetCDF file format. Hierarchical metadata in the NetCDF header contain the representation information that allows NetCDF- aware software to work with the data. It also contains arbitrary preservation description and policy information that can be used to bring the data into use-specific compliance.

Volume (size)

Currently, Data from the EBAF-TOA Product is about 420MB and Data from the EBAF-Surface Product is about 690MB. Data grows with each version update (about every six months). The MERRA collection represents about 160 TB of total data (uncompressed); compressed is ~80 TB.

Velocity

(e.g. real time)

Periodic since updates are performed with each new version update.

Variety

(multiple datasets, mashup)

There is a need in many types of applications to combine MERRA reanalysis data with other reanalyses and observational data such as CERES. The NCCS is using the Climate Model Intercomparison Project (CMIP5) Reference standard for ontological alignment across multiple, disparate data sets.

Variability (rate of change)

The MERRA reanalysis grows by approximately one TB per month.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Validation and testing of semantic metadata, and of federated data products will be provided by data producers at NASA Langley Research Center and at Goddard through regular testing. Regression testing will be implemented to ensure that updates and changes to the iRODS system, newly added data sources, or newly added metadata do not introduce errors to federated data products. MERRA validation is provided by the data producers, NASA Goddard's Global Modeling and Assimilation Office (GMAO).

Visualization

There is a growing need in the scientific community for data management and visualization services that can aggregate data from multiple sources and display it in a single graphical display. Currently, such capabilities are hindered by the challenge of finding and downloading comparable data from multiple servers, and then transforming each heterogeneous dataset to make it usable by the visualization software. Federation of NASA datasets using iRODS will enable scientists to quickly find and aggregate comparable datasets for use with visualization software.

Data Quality

For MERRA, quality controls are applied by the data producers, GMAO.

Data Types

See above.

Data Analytics

Pursuant to the first goal of increasing accessibility and discoverability through innovative technologies, the ASDC and NCCS are exploring a capability to improve data access capabilities. Using iRODS, the ASDC’s Data Products Online (DPO) can be federated with data at GSFC’s NCCS creating a data access system that can serve a much broader customer base than is currently being served. Federating and sharing information will enable the ASDC and NCCS to fully utilize multi-year and multi-instrument data and will improve and automate the discovery of heterogeneous data, increase data transfer latency, and meet customizable criteria based on data content, data quality, metadata, and production.

Big Data Specific Challenges (Gaps)

 

Big Data Specific Challenges in Mobility

A major challenge includes defining an enterprise architecture that can deliver real-time analytics via communication with multiple APIs and cloud computing systems. By keeping the computation resources on cloud systems, the challenge with mobility resides in not overpowering mobile devices with displaying CPU intensive visualizations that may hinder the performance or usability of the data being presented to the user.

Security & Privacy

Requirements

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

This federation builds on several years of iRODS research and development performed at the NCCS. During this time, the NCCS vetted the iRODS features while extending its core functions with domain-specific extensions. For example, the NCCS created and installed Python-based scientific kits within iRODS that automatically harvest metadata when the associated data collection is registered. One of these scientific kits was developed for the MERRA collection. This kit in conjunction with iRODS bolsters the strength of the LaRC/GSFC federation by providing advanced search capabilities.  LaRC is working through the establishment of an advanced architecture that leverages multiple technology pilots and tools (access, discovery, and analysis) designed to integrate capabilities across the earth science community – the R&D completed by both data centers is complementary and only further enhances this use case.

 

Other scientific kits that have been developed include: NetCDF, Intergovernmental Panel on Climate Change (IPCC), and Ocean Modeling and Data Assimilation (ODAS). The combination of iRODS and these scientific kits has culminated in a configurable technology stack called the virtual Climate Data Server (vCDS), meaning that this runtime environment can be deployed to multiple destinations (e.g., bare metal, virtual servers, cloud) to support various scientific needs. The vCDS, which can be viewed as a reference architecture for easing the federation of disparate data repositories, is leveraged by but not limited to LaRC and GSFC.

More Information (URLs)

Please contact the authors for additional information.

 

Note: <additional comments>

       

MERRA Analytic Services MERRA/AS

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

MERRA Analytic Services (MERRA/AS)

Vertical (area)

Scientific Research: Earth Science

Author/Company/Email

John L. Schnase & Daniel Q. Duffy / NASA Goddard Space Flight Center  John.L.Schnase@NASA.gov, Daniel.Q.Duffy@NASA.gov

Actors/Stakeholders and their roles and responsibilities

NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. Actors and stakeholders who have an interest in MERRA include the climate research community, science  applications community, and a growing number of government and private-sector customers who have a need for the MERRA data in their  decision support systems.

Goals

Increase the usability and use of large-scale scientific data collections, such as MERRA.

Use Case Description

MERRA Analytic Services enables MapReduce analytics over the MERRA collection. MERRA/AS is an example of cloud-enabled Climate  Analytics-as-a-Service, which is an approach to meeting the Big Data  challenges of climate science through the combined use of 1) high performance, data proximal analytics, (2) scalable data management, (3)  software appliance virtualization, (4) adaptive analytics, and (5) a  domain-harmonized API. The effectiveness of MERRA/AS is being  demonstrated in several applications, including data publication to the  Earth System Grid Federation (ESGF) in support of Intergovernmental  Panel on Climate Change (IPCC) research, the NASA/Department of  Interior RECOVER wild land fire decision support system, and data  interoperability testbed evaluations between NASA Goddard Space  Flight Center and the NASA Langley Atmospheric Data Center.

Current

Solutions

Compute(System)

NASA Center for Climate Simulation (NCCS)

Storage

The MERRA Analytic Services Hadoop Filesystem (HDFS) is a 36 node Dell cluster, 576 Intel 2.6 GHz  SandyBridge cores, 1300 TB raw storage, 1250 GB  RAM, 11.7 TF theoretical peak compute capacity.

Networking

Cluster nodes are connected by an FDR Infiniband network with peak TCP/IP speeds >20 Gbps.

Software

Cloudera, iRODS, Amazon AWS

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

MERRA data files are created from the Goddard Earth Observing System version 5 (GEOS-5) model and are stored in HDF-EOS and NetCDF formats. Spatial resolution is 1/2 °latitude ×2/3 °longitude × 72 vertical levels extending through the stratosphere. Temporal resolution is 6-hours for three-dimensional, full spatial resolution, extending from 1979-present, nearly the entire satellite era. Each file contains a single grid with multiple 2D and 3D variables. All data are stored on a longitude latitude grid with a vertical dimension applicable for all 3D variables. The GEOS-5 MERRA products are divided into 25 collections: 18 standard products, 7 chemistry products. The collections comprise monthly means files and daily files at six-hour intervals running from 1979 –2012. MERRA data are typically packaged as multi-dimensional binary data within a self-describing NetCDF file format. Hierarchical metadata in the NetCDF header contain the representation information that allows NetCDF aware software to work with the data. It also contains arbitrary preservation description and policy information that can be used to bring the data into use-specific compliance.

Volume (size)

480TB

Velocity

(e.g. real time)

Real-time or batch, depending on the analysis. We're developing a set of "canonical ops" -early stage, near-data operations common to many analytic workflows. The goal is for the canonical ops to run in near real-time.

Variety

(multiple datasets, mashup)

There is a need in many types of applications to combine MERRA reanalysis data with other re-analyses and observational data. We are using the Climate Model Inter-comparison Project (CMIP5) Reference standard for ontological alignment across multiple, disparate data sets.

Variability (rate of change)

The MERRA reanalysis grows by approximately one TB per month.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Validation provided by data producers, NASA Goddard's Global Modeling and Assimilation Office (GMAO).

Visualization

There is a growing need for distributed visualization of analytic outputs.

Data Quality (syntax)

Quality controls applied by data producers, GMAO.

Data Types

See above.

Data Analytics

In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing.

Big Data Specific Challenges (Gaps)

A big question is how to use cloud computing to enable better use of climate science's earthbound compute and data resources. Cloud Computing is providing for us a new tier in the data services stack —a cloud-based layer where agile customization occurs and enterprise-level products are transformed to meet the specialized requirements of applications and consumers. It helps us close the gap between the world of traditional, high-performance computing, which, at least for now, resides in a finely-tuned climate modeling environment at the enterprise level and our new customers, whose expectations and manner of work are increasingly influenced by the smart mobility megatrend.

Big Data Specific Challenges in Mobility

Most modern smartphones, tablets, etc. actually consist of just the display and user interface components of sophisticated applications that run in cloud data centers. This is a mode of work that CAaaS is intended to accommodate.

Security & Privacy

Requirements

No critical issues identified at this time.

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

MapReduce and iRODS fundamentally make analytics and data aggregation easier; our approach to software appliance virtualization in makes it easier to transfer capabilities to new users and simplifies their ability to build new applications; the social construction of extended capabilities facilitated by the notion of canonical operations enable adaptability; and the Climate Data Services API that we're developing enables ease of mastery. Taken together, we believe that these core technologies behind Climate Analytics-as-a-Service creates a generative context where inputs from diverse people and groups, who may or may not be working in concert, can contribute capabilities that help address the Big Data challenges of climate science.

More Information (URLs)

Please contact the authors for additional information.

Note: <additional comments>

       

Figure Typical MERRA/AS Output

Figure Typical MERRA-AS Output.png

Atmospheric Turbulence - Event Discovery and Predictive Analytic

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Atmospheric Turbulence - Event Discovery and Predictive Analytics

Vertical (area)

Scientific Research: Earth Science

Author/Company/Email

Michael Seablom, NASA Headquarters, michael.s.seablom@nasa.gov

Actors/Stakeholders and their roles and responsibilities

Researchers with NASA or NSF grants, weather forecasters, aviation interests (for the generalized case, any researcher who has a role in studying phenomena-based events).

Goals

Enable the discovery of high-impact phenomena contained within voluminous Earth Science data stores and which are difficult to characterize using traditional numerical methods (e.g., turbulence). Correlate such phenomena with global atmospheric re-analysis products to enhance predictive capabilities.

 

Use Case Description

 

Correlate aircraft reports of turbulence (either from pilot reports or from automated aircraft measurements of eddy dissipation rates) with recently completed atmospheric re-analyses of the entire satellite-observing era. Reanalysis products include the North American Regional Reanalysis (NARR) and the Modern-Era Retrospective-Analysis for Research (MERRA) from NASA.

 

Current

Solutions

Compute(System)

NASA Earth Exchange (NEX) - Pleiades supercomputer.

Storage

Re-analysis products are on the order of 100TB each; turbulence data are negligible in size.

Networking

Re-analysis datasets are likely to be too large to relocate to the supercomputer of choice (in this case NEX), therefore the fastest networking possible would be needed.

Software

MapReduce or the like; SciDB or other scientific database.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Distributed

Volume (size)

200TB (current), 500TB within 5 years

Velocity

(e.g. real time)

Data analyzed incrementally

Variety

(multiple datasets, mashup)

Re-analysis datasets are inconsistent in format, resolution, semantics, and metadata. Likely each of these input streams will have to be interpreted/analyzed into a common product.

Variability (rate of change)

Turbulence observations would be updated continuously; re-analysis products are released about once every five years.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues)

Validation would be necessary for the output product (correlations).

Visualization

Useful for interpretation of results.

Data Quality

Input streams would have already been subject to quality control.

Data Types

Gridded output from atmospheric data assimilation systems and textual data from turbulence observations.

Data Analytics

Event-specification language needed to perform data mining / event searches.

Big Data Specific Challenges (Gaps)

Semantics (interpretation of multiple reanalysis products); data movement; database(s) with optimal structuring for 4-dimensional data mining.

Big Data Specific Challenges in Mobility

Development for mobile platforms not essential at this time.

 

Security & Privacy

Requirements

 

No critical issues identified.

Highlight issues for generalizing this use case (e.g. for ref. architecture)

 

Atmospheric turbulence is only one of many phenomena-based events that could be useful for understanding anomalies in the atmosphere or the ocean that are connected over long distances in space and time. However the process has limits to extensibility, i.e., each phenomena may require very different processes for data mining and predictive analysis.

 

More Information (URLs)

 

http://oceanworld.tamu.edu/resources/oceanography-book/teleconnections.htm

http://www.forbes.com/sites/toddwood...t-the-weather/

 

Note: <additional comments>

       

Figure Typical NASA image of turbulent waves

Figure Typical NASA image of turbulent waves.jpg
 

Climate Studies Using the Community Earth System Model at DOE’s NERSC Center

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Climate Studies using the Community Earth System Model at DOE’s NERSC center

Vertical (area)

Research: Climate

Author/Company/Email

PI: Warren Washington, NCAR

Actors/Stakeholders and their roles and responsibilities

Climate scientists, U.S. policy makers

Goals

The goals of the Climate Change Prediction (CCP) group at NCAR are to understand and quantify contributions of natural and anthropogenic-induced patterns of climate variability and change in the 20th and 21st centuries by means of simulations with the Community Earth System Model (CESM).

Use Case Description

With these model simulations, researchers are able to investigate mechanisms of climate variability and change, as well as to detect and attribute past climate changes, and to project and predict future changes. The simulations are motivated by broad community interest and are widely used by the national and international research communities.

 

 

Current

Solutions

Compute(System)

NERSC (24M Hours), DOE LCF (41M), NCAR CSL (17M)

Storage

1.5 PB at NERSC

Networking

ESNet

Software

NCAR PIO library and utilities NCL and NCO, parall el NetCDF

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Data is produced at computing centers. The Earth Systems Grid is an open source effort providing a robust, distributed data and computation platform,

enabling world wide access to Peta/Exa-scale scientific data. ESGF manages the first-ever decentralized database for handling climate science data, with multiple petabytes of data at dozens of federated sites worldwide. It is recognized as the leading infrastructure for the management and access of large distributed data volumes for climate change research. It supports the Coupled Model Intercomparison Project (CMIP), whose protocols enable the periodic assessments carried out by the Intergovernmental Panel on Climate Change (IPCC).

Volume (size)

30 PB at NERSC (assuming 15 end-to-end climate change experiments) in 2017; many times more worldwide

Velocity

(e.g. real time)

42 GBytes/sec are produced by the simulations

Variety

(multiple datasets, mashup)

Data must be compared among  those from from observations, historical reanalysis, and a number of independently produced simulations. The Program for Climate Model Diagnosis and Intercomparison develops methods and tools for the diagnosis and intercomparison of general circulation models (GCMs) that simulate the global climate. The need for innovative analysis of GCM climate simulations is apparent, as increasingly more complex models are developed, while the disagreements among these simulations and relative to climate observations remain significant and poorly understood. The nature and causes of these disagreements must be accounted for in a systematic fashion in order to confidently use GCMs for simulation of putative global climate change.

Variability (rate of change)

Data is produced by codes running at supercomputer centers. During runtime, intense periods of data i/O occur regularly, but typically consume only a few percent of the total run time. Runs are carried out routinely, but spike as deadlines for reports approach.

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues) and Quality

Data produced by climate simulations is plays a large role in informing discussion of climate change simulations. Therefore it must be robust, both from the standpoint of providing a scientifically valid representation of processes that influence climate, but also as that data is stored long term and transferred world-wide to collaborators and other scientists.

Visualization

Visualization is crucial to understanding a system as complex as the Earth ecosystem.

Data Types

        Earth system scientists are being inundated by an explosion of data generated by ever-increasing resolution in both global models and remote sensors.

Data Analytics

There is a need to provide data reduction and analysis web services through the Earth System Grid (ESG). A pressing need is emerging for data analysis capabilities closely linked to data archives.

Big Data Specific Challenges (Gaps)

The rapidly growing size of datasets makes scientific analysis a challenge. The need to write data from simulations is outpacing supercomputers’ ability to accommodate this need.

Big Data Specific Challenges in Mobility

 

Data from simulations and observations must be shared among a large widely distributed community.

Security & Privacy

Requirements

 

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

ESGF is in the early stages of being adapted for use in two additional domains: biology (to accelerate drug design and development) and energy (infrastructure for California Energy Systems for the 21st Century (CES21)).

 

 

More Information (URLs)

http://esgf.org/

http://www-pcmdi.llnl.gov/

http://www.nersc.gov/

http://science.energy.gov/ber/research/cesd/

http://www2.cisl.ucar.edu/

 

 

 

 

 

Note: <additional comments>

       

DOE-BER Subsurface Biogeochemistry Scientific Focus Are

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

DOE-BER Subsurface Biogeochemistry Scientific Focus Area

Vertical (area)

Research: Earth Science

Author/Company/Email

Deb Agarwal, Lawrence Berkeley Lab. daagarwal@lbl.gov

Actors/Stakeholders and their roles and responsibilities

LBNL Sustainable Systems SFA 2.0, Subsurface Scientists, Hydrologists, Geophysicists, Genomics Experts, JGI, Climate scientists, and DOE SBR.

Goals

The Sustainable Systems Scientific Focus Area 2.0 Science Plan (“SFA 2.0”) has been developed to advance predictive understanding of complex and multiscale terrestrial environments relevant to the DOE mission through specifically considering the scientific gaps defined above.

Use Case Description

Development of a Genome-Enabled Watershed Simulation Capability (GEWaSC) that will provide a predictive framework for understanding how genomic information stored in a subsurface microbiome affects biogeochemical watershed functioning, how watershed-scale processes affect microbial functioning, and how these interactions co-evolve. While modeling capabilities developed by our team and others in the community have represented processes occurring over an impressive range of scales (ranging from a single bacterial cell to that of a contaminant plume), to date little effort has been devoted to developing a framework for systematically connecting scales, as is needed to identify key controls and to simulate important feedbacks. A simulation framework that formally scales from genomes to watersheds is the primary focus of this GEWaSC deliverable.

 

Current

Solutions

Compute(System)

NERSC

Storage

NERSC

Networking

ESNet

Software

PFLOWTran, postgres, HDF5, Akuna, NEWT, etc

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Terabase-scale sequencing data from JGI, subsurface and surface hydrological and biogeochemical data from a variety of sensors (including dense geophysical datasets) experimental data from field and lab analysis

Volume (size)

 

Velocity

(e.g. real time)

 

Variety

(multiple datasets, mashup)

Data crosses all scales from genomics of the microbes in the soil to watershed hydro-biogeochemistry. The SFA requires the synthesis of diverse and disparate field, laboratory, and simulation datasets across different semantic, spatial, and temporal scales through GEWaSC. Such datasets will be generated by the different research areas and include simulation data, field data (hydrological, geochemical, geophysical), ‘omics data, and data from laboratory experiments.

 

Variability (rate of change)

Simulations and experiments

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues) and Quality

Each of the sources samples different properties with different footprints – extremely heterogeneous. Each of the soruces has different levels of uncertainty and precision associated with it. In addition, the translation across scales and domains introduces uncertainty as does the data mining. Data quality is critical. 

Visualization

Visualization is crucial to understanding the data.

Data Types

        Described in “Variety” above.

Data Analytics

Data mining, data quality assessment, cross-correlation across datasets, reduced model development, statistics, quality assessment, data fusion, etc.

Big Data Specific Challenges (Gaps)

Translation across diverse and large datasets that cross domains and scales.

Big Data Specific Challenges in Mobility

 

Field experiment data taking would be improved by access to existing data and automated entry of new data via mobile devices.

Security & Privacy

Requirements

 

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

A wide array of programs in the earth sciences are working on challenges that cross the same domains as this project.

 

 

More Information (URLs)

Under development

 

 

 

 

Note: <additional comments>

       

DOE-BER AmeriFlux and FLUXNET Networks

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

DOE-BER AmeriFlux and FLUXNET Networks

Vertical (area)

Research: Earth Science

Author/Company/Email

Deb Agarwal, Lawrence Berkeley Lab. daagarwal@lbl.gov

Actors/Stakeholders and their roles and responsibilities

AmeriFlux scientists, Data Management Team, ICOS, DOE TES, USDA, NSF, and Climate modelers.

Goals

AmeriFlux Network and FLUXNET measurements provide the crucial linkage between organisms, ecosystems, and process-scale studies at climate-relevant scales of landscapes, regions, and continents, which can be incorporated into biogeochemical and climate models. Results from individual flux sites provide the foundation for a growing body of synthesis and modeling analyses.

Use Case Description

AmeriFlux network observations enable scaling of trace gas fluxes (CO2, water vapor) across a broad spectrum of times (hours, days, seasons, years, and decades) and space. Moreover, AmeriFlux and FLUXNET datasets provide the crucial linkages among organisms, ecosystems, and process-scale studies—at climate-relevant scales of landscapes, regions, and continents—for incorporation into biogeochemical and climate models

Current

Solutions

Compute(System)

NERSC

Storage

NERSC

Networking

ESNet

Software

EddyPro, Custom analysis software, R, python, neural networks, Matlab.

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

~150 towers in AmeriFlux and over 500 towers distributed globally collecting flux measurements.

Volume (size)

 

Velocity

(e.g. real time)

 

Variety

(multiple datasets, mashup)

 

The flux data is relatively uniform, however, the biological, disturbance, and other ancillary data needed to process and to interpret the data is extensive and varies widely. Merging this data with the flux data is challenging in today’s systems.

Variability (rate of change)

 

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues) and Quality

Each site has unique measurement and data processing techniques. The network brings this data together and performs a common processing, gap-filling, and quality assessment. Thousands of users

Visualization

Graphs and 3D surfaces are used to visualize the data.

Data Types

        Described in “Variety” above.

Data Analytics

Data mining, data quality assessment, cross-correlation across datasets, data assimilation, data interpolation, statistics, quality assessment, data fusion, etc.

Big Data Specific Challenges (Gaps)

Translation across diverse datasets that cross domains and scales.

Big Data Specific Challenges in Mobility

 

Field experiment data taking would be improved by access to existing data and automated entry of new data via mobile devices.

Security & Privacy

Requirements

 

 

Highlight issues for generalizing this use case (e.g. for ref. architecture)

 

 

More Information (URLs)

Ameriflux.lbl.gov

www.fluxdata.org

 

 

Note: <additional comments>

       

Energy

Consumption forecasting in Smart Grids

NBD(NIST Big Data) Requirements WG Use Case Template Aug 11 2013

Use Case Title

Consumption forecasting in Smart Grids

Vertical (area)

Energy Informatics

Author/Company/Email

Yogesh Simmhan, University of Southern California, simmhan@usc.edu

Actors/Stakeholders and their roles and responsibilities

Electric Utilities, Campus MicroGrids, Building Managers, Power Consumers, Energy Markets

Goals

Develop scalable and accurate forecasting models to predict the energy consumption (kWh) within the utility service area under different spatial and temporal granularities to help improve grid reliability and efficiency.

Use Case Description

Deployment of smart meters are making available near-realtime energy usage data (kWh) every 15-mins at the granularity individual consumers within the service area of smart power utilities. This unprecedented and growing access to fine-grained energy consumption information allows novel analytics capabilities to be developed for predicting energy consumption for customers, transformers, sub-stations and the utility service area. Near-term forecast can be used by utilities and microgrid managers to take preventive action before consumption spikes cause brown/blackouts through demand-response optimization by engaging consumers, bringing peaker units online, or purchasing power from the energy markets. These form an OODA feedback loop. Customers can also use them for energy use planning and budgeting. Medium- to long-term predictions can help utilities and building managers plan generation capacity, renewable portfolio, energy purchasing contracts and sustainable building improvements.

 

Steps involved include 1) Data Collection & Storage: time-series data from (potentially) millions of smart meters in near-realtime, features on consumers, facilities and regions, weather forecasts, archival of data for training, testing and validating models; 2) Data Cleaning & Normalization: Spatio-temporal normalization, gap filling/Interpolation, outlier detection, semantic annotation; 3) Training Forecast Models: Using univariate  timeseries models like ARIMA, and data-driven machine learning models like regression tree, ANN, for different spatial (consumer, transformer) and temporal (15-min, 24-hour) granularities; 4) Prediction: Predict consumption for different spatio-temporal granularities and prediction horizons using near-realtime and historic data fed to the forecast model with thresholds on prediction latencies.

Current

Solutions

Compute(System)

Many-core servers, Commodity Cluster, Workstations

Storage

SQL Databases, CSV Files, HDFS, Meter Data Management

Networking

Gigabit Ethernet

Software

R/Matlab, Weka, Hadoop

Big Data
Characteristics

 

 

Data Source (distributed/centralized)

Head-end of smart meters (distributed), Utility databases (Customer Information, Network topology; centralized), US Census data (distributed), NOAA weather data (distributed), Microgrid building information system (centralized), Microgrid sensor network (distributed)

Volume (size)

10 GB/day; 4 TB/year (City scale)

Velocity

(e.g. real time)

Los Angeles: Once every 15-mins (~100k streams); Once every 8-hours (~1.4M streams) with finer grain data aggregated to 8-hour interval

Variety

(multiple datasets, mashup)

Tuple-based: Timeseries, database rows; Graph-based: Network topology, customer connectivity; Some semantic data for normalization.

Variability (rate of change)

Meter and weather data change, and are collected/used, on hourly basis. Customer/building/grid topology information is slow changing on a weekly basis

Big Data Science (collection, curation,

analysis,

action)

Veracity (Robustness Issues, semantics)

Versioning and reproducibility is necessary to validate/compare past and current models. Resilience of storage and analytics is important for operational needs. Semantic normalization can help with inter-disciplinary analysis (e.g. utility operators, building managers, power engineers, behavioral scientists)

Visualization

Map-based visualization of grid service topology, stress; Energy heat-maps; Plots of demand forecasts vs. capacity, what-if analysis; Realtime information display; Apps with push notification of alerts

Data Quality (syntax)

Gaps in smart meters and weather data; Quality issues in sensor data; Rigorous checks done for “billing quality” meter data;

Data Types

Timeseries (CSV, SQL tuples), Static information (RDF, XML), topology (shape files)

Data Analytics

Forecasting models, machine learning models, time series analysis, clustering, motif detection, complex event processing, visual network analysis,

Big Data Specific Challenges (Gaps)

Scalable realtime analytics over large data streams

Low-latency analytics for operational needs

Federated analytics at utility and microgrid levels

Robust time series analytics over millions of customer consumption data

Customer behavior modeling, targeted curtailment requests

Big Data Specific Challenges in Mobility

Apps for engaging with customers: Data collection from customers/premises for behavior modeling, feature extraction; Notification of curtailment requests by utility/building managers; Suggestions on energy efficiency; Geo-localized display of energy footprint.

 

Security & Privacy

Requirements

Personally identifiable customer data requires careful handling. Customer energy usage data can reveal behavior patterns. Anonymization of information. Data aggregation to avoid customer identification. Data sharing restrictions by federal and state energy regulators. Surveys by behavioral scientists may have IRB restrictions.

Highlight issues for generalizing this use case (e.g. for ref. architecture)

Realtime data-driven analytics for cyber physical systems

 

 

More Information (URLs)

http://smartgrid.usc.edu

http://ganges.usc.edu/wiki/Smart_Grid

https://www.ladwp.com/ladwp/faces/ladwp/aboutus/a-power/a-p-smartgridla

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6475927

Note: <additional comments>

       
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