Table of contents
  1. Story
  2. Slides
    1. Slide 1 The Evolution of Semantic Technologies-The Value of Merging Smart Data with Big Data
    2. Slide 2 Who is Modus Operandi?
    3. Slide 3 IT's common challenge
    4. Slide 4 Modus Approach
    5. Slide 5 The "Cognitive Evolution" of Intelligent Software
    6. Slide 6 New & Expanding Tech Areas
    7. Slide 7 Innovation is Key in These Types of Tech Spaces
    8. Slide 8 Semantic Technology
    9. Slide 9 Semantics and Reasoning
    10. Slide 10 Semantic Approach Improves Data Access
    11. Slide 11 Semantic Approach Simplifies Queries
    12. Slide 12 Building Semantic Profiles From Raw Data
    13. Slide 13 Utilizing Semantic to Integrate Disparate Medical Data
    14. Slide 14 Classification Schemas Must Reflect Subject Matter Expertise
    15. Slide 15 Federated Ontology Layers Allow for Advanced Data Modeling
    16. Slide 16 Putting It All Together Into a Platform
    17. Slide 17 Big Data-Now That You Have Semantic, How To Scale..
    18. Slide 18 The Problem of Big Data is Real (And Closing In)
    19. Slide 19 Big Data Analytics Challenge for Intelligent Systems
    20. Slide 20 Capturing Complex Data Is Difficult
    21. Slide 21 Scaling Semantics 1
    22. Slide 22 Scaling Semantics 2
    23. Slide 23 Avoiding the Hype Cycle
    24. Slide 24 Easy-To-Use Data
    25. Slide 25 Easy UI's Leverage Common Models
    26. Slide 26 Driving the Knowledge to Multiple Users
    27. Slide 27 Visualizing Patterns
    28. Slide 28 Big Data Results in a Highly Intuitive UIs in Key
    29. Slide 29 Providing an End-to-End Solution
    30. Slide 30 Thank You and Questions?
  3. Story
  4. Slides
    1. Slide 1 The Best Way to Get BIG DATA is By Starting Small
    2. Slide 2 BIG DATA
    3. Slide 3 Subcommittee on Networking and Information Technology Research and Development (NITRD Subcommittee)
    4. Slide 4 Data Science Team Example: Chief Data Science Officer
    5. Slide 5 Generic Problems
    6. Slide 6 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Work Flow
    7. Slide 7 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Semantic Medline Database Application
    8. Slide 8 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Visualization and Linking to Original Text
    9. Slide 9 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Bioinformatics Publication
    10. Slide 10 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Semantic Medline at NIH-NLM
    11. Slide 11 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Graphs and Traditional Technologies
    12. Slide 12 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: The YarcData Approach
    13. Slide 13 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: New Use Cases
    14. Slide 14 Modus Operandi: Mantra, Performance, and Vision
    15. Slide 15 Modus Operandi: Finding the Right Needle in the Right Haystack
    16. Slide 16 Data Science Team Example: President of Modus Operandi
    17. Slide 17 Wave and the vMDC (virtual metadata catalog – which is a query translator for non-semantic queries)
    18. Slide 18 How Wave Drives the BLADE Semantic Wiki and Other Kinds of Analytic Visualizations
    19. Slide 19 Possible Scenario
    20. Slide 20 Knowledge Base: Modus Operandi Web Intelligence in MindTouch
    21. Slide 21 Big Data in Memory: Innovation Story
    22. Slide 22 Future: Possibility Panève’s ZettaLeaf & ZettaTree Products
  5. Story
  6. Slides
    1. Slide 1 The Challenges of Big Data for Intelligence Missions
    2. Slide 2 Who is Modus Operandi?
    3. Slide 3 Big Data Analytics Challenge for Intelligence
    4. Slide 4 The Problem of Big Data is Real
    5. Slide 5 Mitigating the Big Data Hype Cycle
    6. Slide 6 Intel Involves Understanding Situational Awareness
    7. Slide 7 Fusing Data Requires Complex Architectures
    8. Slide 8 Capturing Complex Events Using Semantics
    9. Slide 9 Driving the Knowledge to Multiple Users
    10. Slide 10 Querying Events Using Graph Analytics
    11. Slide 11 Delivering Big Data Results in a Highly Intuitive UI
    12. Slide 12 Linking Information Together
    13. Slide 13 Accessing Data Via A Semantic Wiki
    14. Slide 14 Finding Big Data Expertise is a Growing Issue
    15. Slide 15 As With Many New Technologies – “Draft & Develop”
    16. Slide 16 Thank You Questions?
    17. Slide 17 Modus Operandi: The Challenges of Big Data for Intelligence Missions
    18. Slide 18 Modus Operandi: Finding the Right Needle in the Right Haystack
    19. Slide 19 Purpose and Goal
    20. Slide 20 Knowledge Base: MO Web Intelligence in MindTouch
    21. Slide 21 Spreadsheet: MO Knowledge Base in Excel
    22. Slide 22 Big Data Database: Project TYCO Data for Health in Excel
    23. Slide 23 Dashboard: MO Knowledge Base and Project TYCO Data in Spotfire 1
    24. Slide 24 Dashboard: MO Knowledge Base and Project TYCO Data in Spotfire 2
    25. Slide 25 Some Next Steps
    26. Slide 26 Florida Institute of Technology: Learning/Mining and the Internet
  7. Spotfire Dashboard
  8. Research Notes
  9. Modus Operandi
    1. Create DDMS Metacards Automatically
    2. Need To Share?
    3. Drowning in Text?
    4. Big Mission, Small Budget?
    5. Modus Operandi NEWS
  10. Solutions
    1. DI2E Solutions
      1. Our DI2E Solution Advantages
      2. Metacard Generator and XSLT Generator Brochures
      3. Accelerating Sharing Within the DI2E
      4. Quickly Produce DDMS Metacards
      5. Get Expert Configuration, Consulting, and Support Services
    2. Wave Exploitation Framework
      1. Advanced Text Exploitation
      2. The Wave-EF Cost and Time Advantage
      3. Supercharge the Soft Side of Your Analysis Environment
      4. Complex Concept Extraction
      5. Content Enrichment with Semantic Tags
  11. Customers
    1. C4ISR Customers
      1. Our Government Customers
      2. Department of Defense (DoD)
      3. ISR Technology Challenges
      4. Intelligence Community
      5. Complex Event Processing and Consequence Management
    2. Metadata Engineer
      1. Get Metadata Guru Services
      2. Publish Your Data to the DIB
      3. Metadata in the News
      4. Better Image Tagging Improves Warfighter Situational Awareness
    3. Intelligence Analyst
      1. Be Bold
      2. Wave-EF Semantic Filtering
      3. Get More Tech Savy
      4. Catch The Buzz
    4. R & D Organizations
      1. Our R&D Sponsors
      2. R & D Technology Insertion
      3. Wave-EF Semantic Filtering
      4. R & D Productization
      5. Recent Publications
        1. Tactical Semantics: Extracting Situational Knowledge from Voice Transcripts using Ontology-Driven Text Analysis
        2. Introduction to RDF, RDFS, & OWL
        3. Semantic Fusion of Multi-INT Data
        4. SMASHUP: Secure Mashup for Defense Transformation and Net-Centric Systems
        5. Rapid Prototyping with Jena Command Line Utilities
        6. Case Study Year 5—USAF 45th Space Wing Knowledge Management Initiative: Representing Semantic Points-of-View using SITREP and Mission Ontologies
    5. Prime Contractor
      1. Our Government Customers Include
      2. IDIQ Contracts
      3. Past Performance
    6. Government Agencies
      1. DoD Community Sources Advantages
      2. Modus Operandi Offers GOTS Products, Open Source and License-free Software
      3. DoD Community Source in the News
      4. GOTS Software
  12. News & Events
    1. Recent Editorial Coverage
      1. Finding the Right Needle in the Right Haystack
        1. Introduction
        2. Re-Fitting, Re-Focusing
        3. It’s A Matter of Semantics
        4. Growth in Personnel and Products
    2. News Releases
      1. 2013
        1. Aug. 12, 2013 - U.S. Navy Selects Modus Operandi To Develop Software That Will Use Crowdsourcing To Target Assistance During Disasters (HTML) (PDF)
        2. July 23, 2013 - Scott Camden Joins Modus Operandi as Director of C4ISR Programs and Technologies (HTML) (PDF)
        3. April 2, 2013 - Modus Operandi Names Eric Little Vice President and Chief Scientist (HTML) (PDF)
        4. March 18, 2013 - Modus Operandi Awarded $1.5 Million Contract to Develop U.S. Marine Corps Intelligence Analysis Capability (HTML) (PDF)
      2. 2012
        1. Dec. 12, 2012 - Modus Operandi Awarded $1 Million U.S. Army Contract For Enemy And Criminal Behavioral Recognition System (HTML) (PDF)
        2. Nov. 26, 2012 - Modus Operandi Chairman and CEO to Present at FIT Visiting Entrepreneur Program (HTML) (PDF)
        3. Nov. 5, 2012 - Modus Operandi Awarded $1.6 Million U.S. Special Operations Forces Contract (HTML) (PDF)
        4. Oct. 15, 2012 - Modus Operandi Names Jeff Lessner Vice President of Business Development (HTML) (PDF)
        5. May 29, 2012 - Modus Operandi to Present at 2012 Semantic Technology and Business Conference (HTML) (PDF)
        6. May 23, 2012 - Modus Operandi Awarded Contract by the U.S. Office of Naval Research to Develop Streamlined Intelligence Analysis System (HTML) (PDF)
        7. May 14, 2012 - U.S. Navy Selects Modus Operandi For Intelligence Analysis / Counterinsurgency Targeting System (HTML) (PDF)
        8. March 29, 2012 - Modus Operandi Awarded U.S. Air Force Contract to Develop Secure Cloud-Based Information Exchange (HTML) (PDF)
        9. Feb. 21, 2012 - Modus Operandi Awarded U.S. Office of Naval Research Contract to Expand Intelligence Analysis Wiki (HTML) (PDF)
        10. Feb. 7, 2012 - Modus Operandi Appoints Richard McNeight President (HTML)(PDF)
      3. 2011
        1. Oct. 19, 2011 - Modus Operandi Awarded U.S. Navy Anti-Submarine Warfare STTR Contract (HTML) (PDF)
        2. Sept. 28, 2011 - Modus Operandi Completes New Software To Provide Faster Access to Intelligence Reports (HTML) (PDF)
        3. Aug. 24, 2011 - Modus Operandi Selected to Develop System to Monitor Health of U.S. Air Force Satellites (HTML) (PDF)
        4. Aug. 1, 2011 - Modus Operandi Awarded U.S. Air Force Contract to Develop Formal Framework for Secure Mashup Technologies (HTML) (PDF)
        5. July 19, 2011 - Modus Operandi Awarded U.S. Army Contract to Develop a Social Network Approach to Intelligence Analysis (HTML) (PDF)
        6. July 12, 2011 - Tony Barrett Joins Modus Operandi As Senior Business Development Manager (HTML) (PDF)
        7. March 28, 2011 - Modus Operandi Awarded $9.9 Million U.S. Army Contract for Software Technology and Services (HTML) (PDF)
        8. March 15, 2011 - Modus Operandi Awarded U.S. Navy Contract For Intelligence Analysis/Counterinsurgency Targeting System (HTML) (PDF)
        9. Jan. 31, 2011 - Modus Operandi Awarded U.S. Army Contract to Improve Surveillance Sensors' Ability to Collect Intelligence (HTML) (PDF)
        10. Jan. 11, 2011 - Modus Operandi Awarded U.S. Army Contract To Create 'Vocabulary Mining System' to Improve Intelligence Analysis (HTML) (PDF)
      4. 2010
        1. Dec. 6, 2010 - Modus Operandi Names Peter Mozloom Vice President of Cyber Solutions (HTML) (PDF)
        2. July 19, 2010 - Modus Operandi To Participate in U.S. Joint Forces Command Empire Challenge (HTML) (PDF)
        3. June 28, 2010 - Modus Operandi Awarded U.S. Office of Naval Research Contract to Establish Semantic Wiki (HTML) (PDF)
        4. May 24, 2010 - Modus Operandi Personnel to Speak at SemTech (HTML) (PDF)
        5. April 12, 2010 - Modus Operandi Awarded $600,000 U.S. Navy Contract to Develop a Data Fusion SOA Framework for Submarines (HTML) (PDF)
        6. March 29, 2010 - Modus Operandi Awarded $730,000 U.S. Army Contract to Develop Intelligence Analysis Software (HTML) (PDF)
        7. March 22, 2010 - Modus Operandi Awarded $340,000 Project to Enhance the U.S. Air Force's 45th Space Wing Knowledge Management Framework (HTML)(PDF)
        8. March 10, 2010 - Modus Operandi Awarded $425,000 Project to Provide the U.S. Air Force with Expanded Intelligence Analysis Capabilities (HTML) (PDF)
        9. Feb. 22, 2010 - Modus Operandi Opens Aberdeen, Maryland Office (HTML) (PDF)
        10. Jan. 26, 2010 - Modus Operandi Awarded $1.2 Million U.S. Marines Contract for Semantic Intelligence Capabilities (HTML) (PDF)
      5. 2009
        1. Oct. 19, 2009 - Modus Operandi Awarded $730,000 U.S. Army Contract for Predictive Intelligence Capabilities (HTML) (PDF)
        2. Aug. 10, 2009 - Modus Operandi Awarded U.S. Army Contract to Develop Automated Natural Language Processing Programs (HTML) (PDF)
        3. July 9, 2009 - Modus Operandi Awarded DARPA Contract to Develop State-of-the-Art Semantic Intelligence Processing System (HTML) (PDF)
        4. June 16, 2009 - Modus Operandi Joins Lockheed Martin Team For Army Battle Command Modernization Contract (HTML) (PDF)
        5. May 18, 2009 - Modus Operandi Personnel to Participate in SemTech Conference Panel (HTML) (PDF)
        6. April 20, 2009 - Modus Operandi Opens New Headquarters to Address Strong Growth in Business (HTML) (PDF)
        7. March 30, 2009 - Modus Operandi Chief Scientist and VP to Present at SPIE Conference (HTML) (PDF)
        8. March 12, 2009 - Modus Operandi Executive to Speak at SSTC Conference (HTML) (PDF)
        9. Jan. 27, 2009 - Modus Operandi Awarded Navy Contract to Enhance Intelligence Gathering and Understanding on Submarines (HTML) (PDF)
        10. Jan. 12, 2009 - Modus Operandi Releases Wave® Exploitation Framework 3.0.2 (HTML) (PDF)
        11. Jan. 5, 2009 - Modus Operandi Awarded Army Contract to Develop Framework Supporting Intelligence Analysis (HTML) (PDF)
    3. Calendar of Events
      1. Comm & C2 Capabilities Showcase - AFRL
    4. Editorial Coverage
      1. Spacecoast BUSINESS - Sept. 25, 2013
      2. Government Technology - Sept. 9, 2013
      3. Signal Magazine - Aug. 20, 2013
      4. Orlando Sentinel - Aug. 12, 2013
      5. Washington Technology - July 16, 2013
      6. Big Data Republic - June 24, 2013
      7. Bloomberg Businessweek - June 14, 2013
      8. Government Executive - April 2013
      9. Orlando Business Journal - March 20, 2013
      10. National Defense Magazine - March 2013
      11. Florida Today - Nov. 11, 2012
      12. Florida Today - July 4, 2012
      13. National Defense - May 2012
      14. Emergency Management - Jan. 26, 2012
      15. Popular Science - Nov. 2, 2011
      16. Geospatial Intelligence Forum - Oct. 2011
      17. Intelligent Utility - July 13, 2011
      18. Government Security News - Dec. 17, 2010
      19. Search SOA - Nov. 2, 2010
      20. SYS-CON Media - Oct. 5, 2010
      21. Defense Systems - July 2010
      22. Signal Magazine - June 2010
      23. Military Embedded Systems - Jan/Feb 2010
      24. Government Executive - Feb. 1, 2010
      25. C4ISR Journal - Jan. 2010
      26. Military Embedded Systems - Nov.-Dec. 2009
  13. Company
    1. About Modus Operandi
      1. Accolades
      2. Modus Operandi
      3. Our Values
    2. Leadership
      1. Leadership Team
        1. Peter Dyson, Chairman and CEO
        2. Richard McNeight, President
        3. Jeffrey Lessner, VP, Business Development
        4. Dr. Eric Little, VP and Chief Scientist
        5. Charles Keuthan, VP, Finance and Administration
      2. Board of Directors
        1. Peter Dyson, Chairman and CEO
        2. Dr. Anthony Catanese, Member
        3. Richard McNeight, President and Member
        4. James W. Thomas, Member
    3. Contracts
      1. Active IDIQ Contracts
    4. Partners
      1. Our Partners
    5. Past Performance
      1. Case Studies
      2. Our Government Customers
    6. Publications
      1. Publications
        1. Semantic Enrichment and Fusion of Multi-Intelligence Data
      2. Presentations
        1. What Makes A Wiki Semantic?
        2. Tactical Semantics: Extracting Situational Knowledge from Voice Transcripts using Ontology-Driven Text Analysis
        3. Introduction to RDF, RDFS, & OWL
        4. Semantic Fusion of Multi-INT Data
        5. SMASHUP: Secure Mashup for Defense Transformation and Net-Centric Systems
        6. Rapid Prototyping with Jena Command Line Utilities
        7. Case Study Year 5—USAF 45th Space Wing Knowledge Management Initiative: Representing Semantic Points-of-View using SITREP and Mission Ontologies
  14. Careers
    1. Current Openings
    2. Talent Search
    3. Our Values
    4. Employee Benefits
  15. Contact Us
    1. Corporate Headquarters
      1. Driving Directions
    2. Field Office
    3. Contact Us
  16. Semantic Wiki
    1. Fact Sheet
      1. Background
      2. Problems
      3. Solution
      4. Results
    2. Slides
      1. What Makes a Wiki Semantic?
      2. What's a Wiki?
      3. Example: Wikipedia
      4. Wiki Issue
      5. What's a Semantic Wiki?
      6. How are Features Achieved?
      7. Demonstration
      8. Take-aways
  17. Semantic Enrichment and Fusion Of Multi-Intelligence Data
    1. 1 Abstract
    2. 2 Introduction
      1. Discovery services
      2. Semantics
      3. Semantic enrichment
        1. Figure 1. Example of HUMINT/COMINT data streams (USMTF messages) are first transformed into XML (Step 1)
      4. Wave-EF
        1. Figure 2. Wave-EF semantically enriches multi-source intelligence data
      5. Identifying vehicle theft events
    3. 3 Methodology
      1. Extraction patterns
    4. 4 Data
      1. Figure 3. An example of a USMTF message describing a vehicle theft
    5. 5 Results
      1. Table 1. Comparison of Wave-EF, search engine and random precision, recall and F-measure statistics
      2. Table 2.
      3. Figure 4. These screenshots show the original 200 events (left)
      4. Figure 5. Semantic metadata tags generated for a vehicle theft event
    6. 6 Conclusions
    7. 7 Acknowledgements
    8. 8 References
      1. [1]
      2. [2]
      3. [3]
      4. [4]
      5. [5]
      6. [6]
      7. [7]
      8. [8]
      9. [9]
      10. [10]
      11. [11]
      12. [12]
      13. [13]
      14. [14]
      15. [15]
      16. [16]
      17. [17]
      18. [18]
      19. [19]
      20. [20]
      21. [21]
      22. [22]
  18. NEXT

Modus Operandi

Last modified
Table of contents
  1. Story
  2. Slides
    1. Slide 1 The Evolution of Semantic Technologies-The Value of Merging Smart Data with Big Data
    2. Slide 2 Who is Modus Operandi?
    3. Slide 3 IT's common challenge
    4. Slide 4 Modus Approach
    5. Slide 5 The "Cognitive Evolution" of Intelligent Software
    6. Slide 6 New & Expanding Tech Areas
    7. Slide 7 Innovation is Key in These Types of Tech Spaces
    8. Slide 8 Semantic Technology
    9. Slide 9 Semantics and Reasoning
    10. Slide 10 Semantic Approach Improves Data Access
    11. Slide 11 Semantic Approach Simplifies Queries
    12. Slide 12 Building Semantic Profiles From Raw Data
    13. Slide 13 Utilizing Semantic to Integrate Disparate Medical Data
    14. Slide 14 Classification Schemas Must Reflect Subject Matter Expertise
    15. Slide 15 Federated Ontology Layers Allow for Advanced Data Modeling
    16. Slide 16 Putting It All Together Into a Platform
    17. Slide 17 Big Data-Now That You Have Semantic, How To Scale..
    18. Slide 18 The Problem of Big Data is Real (And Closing In)
    19. Slide 19 Big Data Analytics Challenge for Intelligent Systems
    20. Slide 20 Capturing Complex Data Is Difficult
    21. Slide 21 Scaling Semantics 1
    22. Slide 22 Scaling Semantics 2
    23. Slide 23 Avoiding the Hype Cycle
    24. Slide 24 Easy-To-Use Data
    25. Slide 25 Easy UI's Leverage Common Models
    26. Slide 26 Driving the Knowledge to Multiple Users
    27. Slide 27 Visualizing Patterns
    28. Slide 28 Big Data Results in a Highly Intuitive UIs in Key
    29. Slide 29 Providing an End-to-End Solution
    30. Slide 30 Thank You and Questions?
  3. Story
  4. Slides
    1. Slide 1 The Best Way to Get BIG DATA is By Starting Small
    2. Slide 2 BIG DATA
    3. Slide 3 Subcommittee on Networking and Information Technology Research and Development (NITRD Subcommittee)
    4. Slide 4 Data Science Team Example: Chief Data Science Officer
    5. Slide 5 Generic Problems
    6. Slide 6 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Work Flow
    7. Slide 7 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Semantic Medline Database Application
    8. Slide 8 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Visualization and Linking to Original Text
    9. Slide 9 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Bioinformatics Publication
    10. Slide 10 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Semantic Medline at NIH-NLM
    11. Slide 11 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Graphs and Traditional Technologies
    12. Slide 12 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: The YarcData Approach
    13. Slide 13 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: New Use Cases
    14. Slide 14 Modus Operandi: Mantra, Performance, and Vision
    15. Slide 15 Modus Operandi: Finding the Right Needle in the Right Haystack
    16. Slide 16 Data Science Team Example: President of Modus Operandi
    17. Slide 17 Wave and the vMDC (virtual metadata catalog – which is a query translator for non-semantic queries)
    18. Slide 18 How Wave Drives the BLADE Semantic Wiki and Other Kinds of Analytic Visualizations
    19. Slide 19 Possible Scenario
    20. Slide 20 Knowledge Base: Modus Operandi Web Intelligence in MindTouch
    21. Slide 21 Big Data in Memory: Innovation Story
    22. Slide 22 Future: Possibility Panève’s ZettaLeaf & ZettaTree Products
  5. Story
  6. Slides
    1. Slide 1 The Challenges of Big Data for Intelligence Missions
    2. Slide 2 Who is Modus Operandi?
    3. Slide 3 Big Data Analytics Challenge for Intelligence
    4. Slide 4 The Problem of Big Data is Real
    5. Slide 5 Mitigating the Big Data Hype Cycle
    6. Slide 6 Intel Involves Understanding Situational Awareness
    7. Slide 7 Fusing Data Requires Complex Architectures
    8. Slide 8 Capturing Complex Events Using Semantics
    9. Slide 9 Driving the Knowledge to Multiple Users
    10. Slide 10 Querying Events Using Graph Analytics
    11. Slide 11 Delivering Big Data Results in a Highly Intuitive UI
    12. Slide 12 Linking Information Together
    13. Slide 13 Accessing Data Via A Semantic Wiki
    14. Slide 14 Finding Big Data Expertise is a Growing Issue
    15. Slide 15 As With Many New Technologies – “Draft & Develop”
    16. Slide 16 Thank You Questions?
    17. Slide 17 Modus Operandi: The Challenges of Big Data for Intelligence Missions
    18. Slide 18 Modus Operandi: Finding the Right Needle in the Right Haystack
    19. Slide 19 Purpose and Goal
    20. Slide 20 Knowledge Base: MO Web Intelligence in MindTouch
    21. Slide 21 Spreadsheet: MO Knowledge Base in Excel
    22. Slide 22 Big Data Database: Project TYCO Data for Health in Excel
    23. Slide 23 Dashboard: MO Knowledge Base and Project TYCO Data in Spotfire 1
    24. Slide 24 Dashboard: MO Knowledge Base and Project TYCO Data in Spotfire 2
    25. Slide 25 Some Next Steps
    26. Slide 26 Florida Institute of Technology: Learning/Mining and the Internet
  7. Spotfire Dashboard
  8. Research Notes
  9. Modus Operandi
    1. Create DDMS Metacards Automatically
    2. Need To Share?
    3. Drowning in Text?
    4. Big Mission, Small Budget?
    5. Modus Operandi NEWS
  10. Solutions
    1. DI2E Solutions
      1. Our DI2E Solution Advantages
      2. Metacard Generator and XSLT Generator Brochures
      3. Accelerating Sharing Within the DI2E
      4. Quickly Produce DDMS Metacards
      5. Get Expert Configuration, Consulting, and Support Services
    2. Wave Exploitation Framework
      1. Advanced Text Exploitation
      2. The Wave-EF Cost and Time Advantage
      3. Supercharge the Soft Side of Your Analysis Environment
      4. Complex Concept Extraction
      5. Content Enrichment with Semantic Tags
  11. Customers
    1. C4ISR Customers
      1. Our Government Customers
      2. Department of Defense (DoD)
      3. ISR Technology Challenges
      4. Intelligence Community
      5. Complex Event Processing and Consequence Management
    2. Metadata Engineer
      1. Get Metadata Guru Services
      2. Publish Your Data to the DIB
      3. Metadata in the News
      4. Better Image Tagging Improves Warfighter Situational Awareness
    3. Intelligence Analyst
      1. Be Bold
      2. Wave-EF Semantic Filtering
      3. Get More Tech Savy
      4. Catch The Buzz
    4. R & D Organizations
      1. Our R&D Sponsors
      2. R & D Technology Insertion
      3. Wave-EF Semantic Filtering
      4. R & D Productization
      5. Recent Publications
        1. Tactical Semantics: Extracting Situational Knowledge from Voice Transcripts using Ontology-Driven Text Analysis
        2. Introduction to RDF, RDFS, & OWL
        3. Semantic Fusion of Multi-INT Data
        4. SMASHUP: Secure Mashup for Defense Transformation and Net-Centric Systems
        5. Rapid Prototyping with Jena Command Line Utilities
        6. Case Study Year 5—USAF 45th Space Wing Knowledge Management Initiative: Representing Semantic Points-of-View using SITREP and Mission Ontologies
    5. Prime Contractor
      1. Our Government Customers Include
      2. IDIQ Contracts
      3. Past Performance
    6. Government Agencies
      1. DoD Community Sources Advantages
      2. Modus Operandi Offers GOTS Products, Open Source and License-free Software
      3. DoD Community Source in the News
      4. GOTS Software
  12. News & Events
    1. Recent Editorial Coverage
      1. Finding the Right Needle in the Right Haystack
        1. Introduction
        2. Re-Fitting, Re-Focusing
        3. It’s A Matter of Semantics
        4. Growth in Personnel and Products
    2. News Releases
      1. 2013
        1. Aug. 12, 2013 - U.S. Navy Selects Modus Operandi To Develop Software That Will Use Crowdsourcing To Target Assistance During Disasters (HTML) (PDF)
        2. July 23, 2013 - Scott Camden Joins Modus Operandi as Director of C4ISR Programs and Technologies (HTML) (PDF)
        3. April 2, 2013 - Modus Operandi Names Eric Little Vice President and Chief Scientist (HTML) (PDF)
        4. March 18, 2013 - Modus Operandi Awarded $1.5 Million Contract to Develop U.S. Marine Corps Intelligence Analysis Capability (HTML) (PDF)
      2. 2012
        1. Dec. 12, 2012 - Modus Operandi Awarded $1 Million U.S. Army Contract For Enemy And Criminal Behavioral Recognition System (HTML) (PDF)
        2. Nov. 26, 2012 - Modus Operandi Chairman and CEO to Present at FIT Visiting Entrepreneur Program (HTML) (PDF)
        3. Nov. 5, 2012 - Modus Operandi Awarded $1.6 Million U.S. Special Operations Forces Contract (HTML) (PDF)
        4. Oct. 15, 2012 - Modus Operandi Names Jeff Lessner Vice President of Business Development (HTML) (PDF)
        5. May 29, 2012 - Modus Operandi to Present at 2012 Semantic Technology and Business Conference (HTML) (PDF)
        6. May 23, 2012 - Modus Operandi Awarded Contract by the U.S. Office of Naval Research to Develop Streamlined Intelligence Analysis System (HTML) (PDF)
        7. May 14, 2012 - U.S. Navy Selects Modus Operandi For Intelligence Analysis / Counterinsurgency Targeting System (HTML) (PDF)
        8. March 29, 2012 - Modus Operandi Awarded U.S. Air Force Contract to Develop Secure Cloud-Based Information Exchange (HTML) (PDF)
        9. Feb. 21, 2012 - Modus Operandi Awarded U.S. Office of Naval Research Contract to Expand Intelligence Analysis Wiki (HTML) (PDF)
        10. Feb. 7, 2012 - Modus Operandi Appoints Richard McNeight President (HTML)(PDF)
      3. 2011
        1. Oct. 19, 2011 - Modus Operandi Awarded U.S. Navy Anti-Submarine Warfare STTR Contract (HTML) (PDF)
        2. Sept. 28, 2011 - Modus Operandi Completes New Software To Provide Faster Access to Intelligence Reports (HTML) (PDF)
        3. Aug. 24, 2011 - Modus Operandi Selected to Develop System to Monitor Health of U.S. Air Force Satellites (HTML) (PDF)
        4. Aug. 1, 2011 - Modus Operandi Awarded U.S. Air Force Contract to Develop Formal Framework for Secure Mashup Technologies (HTML) (PDF)
        5. July 19, 2011 - Modus Operandi Awarded U.S. Army Contract to Develop a Social Network Approach to Intelligence Analysis (HTML) (PDF)
        6. July 12, 2011 - Tony Barrett Joins Modus Operandi As Senior Business Development Manager (HTML) (PDF)
        7. March 28, 2011 - Modus Operandi Awarded $9.9 Million U.S. Army Contract for Software Technology and Services (HTML) (PDF)
        8. March 15, 2011 - Modus Operandi Awarded U.S. Navy Contract For Intelligence Analysis/Counterinsurgency Targeting System (HTML) (PDF)
        9. Jan. 31, 2011 - Modus Operandi Awarded U.S. Army Contract to Improve Surveillance Sensors' Ability to Collect Intelligence (HTML) (PDF)
        10. Jan. 11, 2011 - Modus Operandi Awarded U.S. Army Contract To Create 'Vocabulary Mining System' to Improve Intelligence Analysis (HTML) (PDF)
      4. 2010
        1. Dec. 6, 2010 - Modus Operandi Names Peter Mozloom Vice President of Cyber Solutions (HTML) (PDF)
        2. July 19, 2010 - Modus Operandi To Participate in U.S. Joint Forces Command Empire Challenge (HTML) (PDF)
        3. June 28, 2010 - Modus Operandi Awarded U.S. Office of Naval Research Contract to Establish Semantic Wiki (HTML) (PDF)
        4. May 24, 2010 - Modus Operandi Personnel to Speak at SemTech (HTML) (PDF)
        5. April 12, 2010 - Modus Operandi Awarded $600,000 U.S. Navy Contract to Develop a Data Fusion SOA Framework for Submarines (HTML) (PDF)
        6. March 29, 2010 - Modus Operandi Awarded $730,000 U.S. Army Contract to Develop Intelligence Analysis Software (HTML) (PDF)
        7. March 22, 2010 - Modus Operandi Awarded $340,000 Project to Enhance the U.S. Air Force's 45th Space Wing Knowledge Management Framework (HTML)(PDF)
        8. March 10, 2010 - Modus Operandi Awarded $425,000 Project to Provide the U.S. Air Force with Expanded Intelligence Analysis Capabilities (HTML) (PDF)
        9. Feb. 22, 2010 - Modus Operandi Opens Aberdeen, Maryland Office (HTML) (PDF)
        10. Jan. 26, 2010 - Modus Operandi Awarded $1.2 Million U.S. Marines Contract for Semantic Intelligence Capabilities (HTML) (PDF)
      5. 2009
        1. Oct. 19, 2009 - Modus Operandi Awarded $730,000 U.S. Army Contract for Predictive Intelligence Capabilities (HTML) (PDF)
        2. Aug. 10, 2009 - Modus Operandi Awarded U.S. Army Contract to Develop Automated Natural Language Processing Programs (HTML) (PDF)
        3. July 9, 2009 - Modus Operandi Awarded DARPA Contract to Develop State-of-the-Art Semantic Intelligence Processing System (HTML) (PDF)
        4. June 16, 2009 - Modus Operandi Joins Lockheed Martin Team For Army Battle Command Modernization Contract (HTML) (PDF)
        5. May 18, 2009 - Modus Operandi Personnel to Participate in SemTech Conference Panel (HTML) (PDF)
        6. April 20, 2009 - Modus Operandi Opens New Headquarters to Address Strong Growth in Business (HTML) (PDF)
        7. March 30, 2009 - Modus Operandi Chief Scientist and VP to Present at SPIE Conference (HTML) (PDF)
        8. March 12, 2009 - Modus Operandi Executive to Speak at SSTC Conference (HTML) (PDF)
        9. Jan. 27, 2009 - Modus Operandi Awarded Navy Contract to Enhance Intelligence Gathering and Understanding on Submarines (HTML) (PDF)
        10. Jan. 12, 2009 - Modus Operandi Releases Wave® Exploitation Framework 3.0.2 (HTML) (PDF)
        11. Jan. 5, 2009 - Modus Operandi Awarded Army Contract to Develop Framework Supporting Intelligence Analysis (HTML) (PDF)
    3. Calendar of Events
      1. Comm & C2 Capabilities Showcase - AFRL
    4. Editorial Coverage
      1. Spacecoast BUSINESS - Sept. 25, 2013
      2. Government Technology - Sept. 9, 2013
      3. Signal Magazine - Aug. 20, 2013
      4. Orlando Sentinel - Aug. 12, 2013
      5. Washington Technology - July 16, 2013
      6. Big Data Republic - June 24, 2013
      7. Bloomberg Businessweek - June 14, 2013
      8. Government Executive - April 2013
      9. Orlando Business Journal - March 20, 2013
      10. National Defense Magazine - March 2013
      11. Florida Today - Nov. 11, 2012
      12. Florida Today - July 4, 2012
      13. National Defense - May 2012
      14. Emergency Management - Jan. 26, 2012
      15. Popular Science - Nov. 2, 2011
      16. Geospatial Intelligence Forum - Oct. 2011
      17. Intelligent Utility - July 13, 2011
      18. Government Security News - Dec. 17, 2010
      19. Search SOA - Nov. 2, 2010
      20. SYS-CON Media - Oct. 5, 2010
      21. Defense Systems - July 2010
      22. Signal Magazine - June 2010
      23. Military Embedded Systems - Jan/Feb 2010
      24. Government Executive - Feb. 1, 2010
      25. C4ISR Journal - Jan. 2010
      26. Military Embedded Systems - Nov.-Dec. 2009
  13. Company
    1. About Modus Operandi
      1. Accolades
      2. Modus Operandi
      3. Our Values
    2. Leadership
      1. Leadership Team
        1. Peter Dyson, Chairman and CEO
        2. Richard McNeight, President
        3. Jeffrey Lessner, VP, Business Development
        4. Dr. Eric Little, VP and Chief Scientist
        5. Charles Keuthan, VP, Finance and Administration
      2. Board of Directors
        1. Peter Dyson, Chairman and CEO
        2. Dr. Anthony Catanese, Member
        3. Richard McNeight, President and Member
        4. James W. Thomas, Member
    3. Contracts
      1. Active IDIQ Contracts
    4. Partners
      1. Our Partners
    5. Past Performance
      1. Case Studies
      2. Our Government Customers
    6. Publications
      1. Publications
        1. Semantic Enrichment and Fusion of Multi-Intelligence Data
      2. Presentations
        1. What Makes A Wiki Semantic?
        2. Tactical Semantics: Extracting Situational Knowledge from Voice Transcripts using Ontology-Driven Text Analysis
        3. Introduction to RDF, RDFS, & OWL
        4. Semantic Fusion of Multi-INT Data
        5. SMASHUP: Secure Mashup for Defense Transformation and Net-Centric Systems
        6. Rapid Prototyping with Jena Command Line Utilities
        7. Case Study Year 5—USAF 45th Space Wing Knowledge Management Initiative: Representing Semantic Points-of-View using SITREP and Mission Ontologies
  14. Careers
    1. Current Openings
    2. Talent Search
    3. Our Values
    4. Employee Benefits
  15. Contact Us
    1. Corporate Headquarters
      1. Driving Directions
    2. Field Office
    3. Contact Us
  16. Semantic Wiki
    1. Fact Sheet
      1. Background
      2. Problems
      3. Solution
      4. Results
    2. Slides
      1. What Makes a Wiki Semantic?
      2. What's a Wiki?
      3. Example: Wikipedia
      4. Wiki Issue
      5. What's a Semantic Wiki?
      6. How are Features Achieved?
      7. Demonstration
      8. Take-aways
  17. Semantic Enrichment and Fusion Of Multi-Intelligence Data
    1. 1 Abstract
    2. 2 Introduction
      1. Discovery services
      2. Semantics
      3. Semantic enrichment
        1. Figure 1. Example of HUMINT/COMINT data streams (USMTF messages) are first transformed into XML (Step 1)
      4. Wave-EF
        1. Figure 2. Wave-EF semantically enriches multi-source intelligence data
      5. Identifying vehicle theft events
    3. 3 Methodology
      1. Extraction patterns
    4. 4 Data
      1. Figure 3. An example of a USMTF message describing a vehicle theft
    5. 5 Results
      1. Table 1. Comparison of Wave-EF, search engine and random precision, recall and F-measure statistics
      2. Table 2.
      3. Figure 4. These screenshots show the original 200 events (left)
      4. Figure 5. Semantic metadata tags generated for a vehicle theft event
    6. 6 Conclusions
    7. 7 Acknowledgements
    8. 8 References
      1. [1]
      2. [2]
      3. [3]
      4. [4]
      5. [5]
      6. [6]
      7. [7]
      8. [8]
      9. [9]
      10. [10]
      11. [11]
      12. [12]
      13. [13]
      14. [14]
      15. [15]
      16. [16]
      17. [17]
      18. [18]
      19. [19]
      20. [20]
      21. [21]
      22. [22]
  18. NEXT

  1. Story
  2. Slides
    1. Slide 1 The Evolution of Semantic Technologies-The Value of Merging Smart Data with Big Data
    2. Slide 2 Who is Modus Operandi?
    3. Slide 3 IT's common challenge
    4. Slide 4 Modus Approach
    5. Slide 5 The "Cognitive Evolution" of Intelligent Software
    6. Slide 6 New & Expanding Tech Areas
    7. Slide 7 Innovation is Key in These Types of Tech Spaces
    8. Slide 8 Semantic Technology
    9. Slide 9 Semantics and Reasoning
    10. Slide 10 Semantic Approach Improves Data Access
    11. Slide 11 Semantic Approach Simplifies Queries
    12. Slide 12 Building Semantic Profiles From Raw Data
    13. Slide 13 Utilizing Semantic to Integrate Disparate Medical Data
    14. Slide 14 Classification Schemas Must Reflect Subject Matter Expertise
    15. Slide 15 Federated Ontology Layers Allow for Advanced Data Modeling
    16. Slide 16 Putting It All Together Into a Platform
    17. Slide 17 Big Data-Now That You Have Semantic, How To Scale..
    18. Slide 18 The Problem of Big Data is Real (And Closing In)
    19. Slide 19 Big Data Analytics Challenge for Intelligent Systems
    20. Slide 20 Capturing Complex Data Is Difficult
    21. Slide 21 Scaling Semantics 1
    22. Slide 22 Scaling Semantics 2
    23. Slide 23 Avoiding the Hype Cycle
    24. Slide 24 Easy-To-Use Data
    25. Slide 25 Easy UI's Leverage Common Models
    26. Slide 26 Driving the Knowledge to Multiple Users
    27. Slide 27 Visualizing Patterns
    28. Slide 28 Big Data Results in a Highly Intuitive UIs in Key
    29. Slide 29 Providing an End-to-End Solution
    30. Slide 30 Thank You and Questions?
  3. Story
  4. Slides
    1. Slide 1 The Best Way to Get BIG DATA is By Starting Small
    2. Slide 2 BIG DATA
    3. Slide 3 Subcommittee on Networking and Information Technology Research and Development (NITRD Subcommittee)
    4. Slide 4 Data Science Team Example: Chief Data Science Officer
    5. Slide 5 Generic Problems
    6. Slide 6 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Work Flow
    7. Slide 7 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Semantic Medline Database Application
    8. Slide 8 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Visualization and Linking to Original Text
    9. Slide 9 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Bioinformatics Publication
    10. Slide 10 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Semantic Medline at NIH-NLM
    11. Slide 11 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Graphs and Traditional Technologies
    12. Slide 12 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: The YarcData Approach
    13. Slide 13 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: New Use Cases
    14. Slide 14 Modus Operandi: Mantra, Performance, and Vision
    15. Slide 15 Modus Operandi: Finding the Right Needle in the Right Haystack
    16. Slide 16 Data Science Team Example: President of Modus Operandi
    17. Slide 17 Wave and the vMDC (virtual metadata catalog – which is a query translator for non-semantic queries)
    18. Slide 18 How Wave Drives the BLADE Semantic Wiki and Other Kinds of Analytic Visualizations
    19. Slide 19 Possible Scenario
    20. Slide 20 Knowledge Base: Modus Operandi Web Intelligence in MindTouch
    21. Slide 21 Big Data in Memory: Innovation Story
    22. Slide 22 Future: Possibility Panève’s ZettaLeaf & ZettaTree Products
  5. Story
  6. Slides
    1. Slide 1 The Challenges of Big Data for Intelligence Missions
    2. Slide 2 Who is Modus Operandi?
    3. Slide 3 Big Data Analytics Challenge for Intelligence
    4. Slide 4 The Problem of Big Data is Real
    5. Slide 5 Mitigating the Big Data Hype Cycle
    6. Slide 6 Intel Involves Understanding Situational Awareness
    7. Slide 7 Fusing Data Requires Complex Architectures
    8. Slide 8 Capturing Complex Events Using Semantics
    9. Slide 9 Driving the Knowledge to Multiple Users
    10. Slide 10 Querying Events Using Graph Analytics
    11. Slide 11 Delivering Big Data Results in a Highly Intuitive UI
    12. Slide 12 Linking Information Together
    13. Slide 13 Accessing Data Via A Semantic Wiki
    14. Slide 14 Finding Big Data Expertise is a Growing Issue
    15. Slide 15 As With Many New Technologies – “Draft & Develop”
    16. Slide 16 Thank You Questions?
    17. Slide 17 Modus Operandi: The Challenges of Big Data for Intelligence Missions
    18. Slide 18 Modus Operandi: Finding the Right Needle in the Right Haystack
    19. Slide 19 Purpose and Goal
    20. Slide 20 Knowledge Base: MO Web Intelligence in MindTouch
    21. Slide 21 Spreadsheet: MO Knowledge Base in Excel
    22. Slide 22 Big Data Database: Project TYCO Data for Health in Excel
    23. Slide 23 Dashboard: MO Knowledge Base and Project TYCO Data in Spotfire 1
    24. Slide 24 Dashboard: MO Knowledge Base and Project TYCO Data in Spotfire 2
    25. Slide 25 Some Next Steps
    26. Slide 26 Florida Institute of Technology: Learning/Mining and the Internet
  7. Spotfire Dashboard
  8. Research Notes
  9. Modus Operandi
    1. Create DDMS Metacards Automatically
    2. Need To Share?
    3. Drowning in Text?
    4. Big Mission, Small Budget?
    5. Modus Operandi NEWS
  10. Solutions
    1. DI2E Solutions
      1. Our DI2E Solution Advantages
      2. Metacard Generator and XSLT Generator Brochures
      3. Accelerating Sharing Within the DI2E
      4. Quickly Produce DDMS Metacards
      5. Get Expert Configuration, Consulting, and Support Services
    2. Wave Exploitation Framework
      1. Advanced Text Exploitation
      2. The Wave-EF Cost and Time Advantage
      3. Supercharge the Soft Side of Your Analysis Environment
      4. Complex Concept Extraction
      5. Content Enrichment with Semantic Tags
  11. Customers
    1. C4ISR Customers
      1. Our Government Customers
      2. Department of Defense (DoD)
      3. ISR Technology Challenges
      4. Intelligence Community
      5. Complex Event Processing and Consequence Management
    2. Metadata Engineer
      1. Get Metadata Guru Services
      2. Publish Your Data to the DIB
      3. Metadata in the News
      4. Better Image Tagging Improves Warfighter Situational Awareness
    3. Intelligence Analyst
      1. Be Bold
      2. Wave-EF Semantic Filtering
      3. Get More Tech Savy
      4. Catch The Buzz
    4. R & D Organizations
      1. Our R&D Sponsors
      2. R & D Technology Insertion
      3. Wave-EF Semantic Filtering
      4. R & D Productization
      5. Recent Publications
        1. Tactical Semantics: Extracting Situational Knowledge from Voice Transcripts using Ontology-Driven Text Analysis
        2. Introduction to RDF, RDFS, & OWL
        3. Semantic Fusion of Multi-INT Data
        4. SMASHUP: Secure Mashup for Defense Transformation and Net-Centric Systems
        5. Rapid Prototyping with Jena Command Line Utilities
        6. Case Study Year 5—USAF 45th Space Wing Knowledge Management Initiative: Representing Semantic Points-of-View using SITREP and Mission Ontologies
    5. Prime Contractor
      1. Our Government Customers Include
      2. IDIQ Contracts
      3. Past Performance
    6. Government Agencies
      1. DoD Community Sources Advantages
      2. Modus Operandi Offers GOTS Products, Open Source and License-free Software
      3. DoD Community Source in the News
      4. GOTS Software
  12. News & Events
    1. Recent Editorial Coverage
      1. Finding the Right Needle in the Right Haystack
        1. Introduction
        2. Re-Fitting, Re-Focusing
        3. It’s A Matter of Semantics
        4. Growth in Personnel and Products
    2. News Releases
      1. 2013
        1. Aug. 12, 2013 - U.S. Navy Selects Modus Operandi To Develop Software That Will Use Crowdsourcing To Target Assistance During Disasters (HTML) (PDF)
        2. July 23, 2013 - Scott Camden Joins Modus Operandi as Director of C4ISR Programs and Technologies (HTML) (PDF)
        3. April 2, 2013 - Modus Operandi Names Eric Little Vice President and Chief Scientist (HTML) (PDF)
        4. March 18, 2013 - Modus Operandi Awarded $1.5 Million Contract to Develop U.S. Marine Corps Intelligence Analysis Capability (HTML) (PDF)
      2. 2012
        1. Dec. 12, 2012 - Modus Operandi Awarded $1 Million U.S. Army Contract For Enemy And Criminal Behavioral Recognition System (HTML) (PDF)
        2. Nov. 26, 2012 - Modus Operandi Chairman and CEO to Present at FIT Visiting Entrepreneur Program (HTML) (PDF)
        3. Nov. 5, 2012 - Modus Operandi Awarded $1.6 Million U.S. Special Operations Forces Contract (HTML) (PDF)
        4. Oct. 15, 2012 - Modus Operandi Names Jeff Lessner Vice President of Business Development (HTML) (PDF)
        5. May 29, 2012 - Modus Operandi to Present at 2012 Semantic Technology and Business Conference (HTML) (PDF)
        6. May 23, 2012 - Modus Operandi Awarded Contract by the U.S. Office of Naval Research to Develop Streamlined Intelligence Analysis System (HTML) (PDF)
        7. May 14, 2012 - U.S. Navy Selects Modus Operandi For Intelligence Analysis / Counterinsurgency Targeting System (HTML) (PDF)
        8. March 29, 2012 - Modus Operandi Awarded U.S. Air Force Contract to Develop Secure Cloud-Based Information Exchange (HTML) (PDF)
        9. Feb. 21, 2012 - Modus Operandi Awarded U.S. Office of Naval Research Contract to Expand Intelligence Analysis Wiki (HTML) (PDF)
        10. Feb. 7, 2012 - Modus Operandi Appoints Richard McNeight President (HTML)(PDF)
      3. 2011
        1. Oct. 19, 2011 - Modus Operandi Awarded U.S. Navy Anti-Submarine Warfare STTR Contract (HTML) (PDF)
        2. Sept. 28, 2011 - Modus Operandi Completes New Software To Provide Faster Access to Intelligence Reports (HTML) (PDF)
        3. Aug. 24, 2011 - Modus Operandi Selected to Develop System to Monitor Health of U.S. Air Force Satellites (HTML) (PDF)
        4. Aug. 1, 2011 - Modus Operandi Awarded U.S. Air Force Contract to Develop Formal Framework for Secure Mashup Technologies (HTML) (PDF)
        5. July 19, 2011 - Modus Operandi Awarded U.S. Army Contract to Develop a Social Network Approach to Intelligence Analysis (HTML) (PDF)
        6. July 12, 2011 - Tony Barrett Joins Modus Operandi As Senior Business Development Manager (HTML) (PDF)
        7. March 28, 2011 - Modus Operandi Awarded $9.9 Million U.S. Army Contract for Software Technology and Services (HTML) (PDF)
        8. March 15, 2011 - Modus Operandi Awarded U.S. Navy Contract For Intelligence Analysis/Counterinsurgency Targeting System (HTML) (PDF)
        9. Jan. 31, 2011 - Modus Operandi Awarded U.S. Army Contract to Improve Surveillance Sensors' Ability to Collect Intelligence (HTML) (PDF)
        10. Jan. 11, 2011 - Modus Operandi Awarded U.S. Army Contract To Create 'Vocabulary Mining System' to Improve Intelligence Analysis (HTML) (PDF)
      4. 2010
        1. Dec. 6, 2010 - Modus Operandi Names Peter Mozloom Vice President of Cyber Solutions (HTML) (PDF)
        2. July 19, 2010 - Modus Operandi To Participate in U.S. Joint Forces Command Empire Challenge (HTML) (PDF)
        3. June 28, 2010 - Modus Operandi Awarded U.S. Office of Naval Research Contract to Establish Semantic Wiki (HTML) (PDF)
        4. May 24, 2010 - Modus Operandi Personnel to Speak at SemTech (HTML) (PDF)
        5. April 12, 2010 - Modus Operandi Awarded $600,000 U.S. Navy Contract to Develop a Data Fusion SOA Framework for Submarines (HTML) (PDF)
        6. March 29, 2010 - Modus Operandi Awarded $730,000 U.S. Army Contract to Develop Intelligence Analysis Software (HTML) (PDF)
        7. March 22, 2010 - Modus Operandi Awarded $340,000 Project to Enhance the U.S. Air Force's 45th Space Wing Knowledge Management Framework (HTML)(PDF)
        8. March 10, 2010 - Modus Operandi Awarded $425,000 Project to Provide the U.S. Air Force with Expanded Intelligence Analysis Capabilities (HTML) (PDF)
        9. Feb. 22, 2010 - Modus Operandi Opens Aberdeen, Maryland Office (HTML) (PDF)
        10. Jan. 26, 2010 - Modus Operandi Awarded $1.2 Million U.S. Marines Contract for Semantic Intelligence Capabilities (HTML) (PDF)
      5. 2009
        1. Oct. 19, 2009 - Modus Operandi Awarded $730,000 U.S. Army Contract for Predictive Intelligence Capabilities (HTML) (PDF)
        2. Aug. 10, 2009 - Modus Operandi Awarded U.S. Army Contract to Develop Automated Natural Language Processing Programs (HTML) (PDF)
        3. July 9, 2009 - Modus Operandi Awarded DARPA Contract to Develop State-of-the-Art Semantic Intelligence Processing System (HTML) (PDF)
        4. June 16, 2009 - Modus Operandi Joins Lockheed Martin Team For Army Battle Command Modernization Contract (HTML) (PDF)
        5. May 18, 2009 - Modus Operandi Personnel to Participate in SemTech Conference Panel (HTML) (PDF)
        6. April 20, 2009 - Modus Operandi Opens New Headquarters to Address Strong Growth in Business (HTML) (PDF)
        7. March 30, 2009 - Modus Operandi Chief Scientist and VP to Present at SPIE Conference (HTML) (PDF)
        8. March 12, 2009 - Modus Operandi Executive to Speak at SSTC Conference (HTML) (PDF)
        9. Jan. 27, 2009 - Modus Operandi Awarded Navy Contract to Enhance Intelligence Gathering and Understanding on Submarines (HTML) (PDF)
        10. Jan. 12, 2009 - Modus Operandi Releases Wave® Exploitation Framework 3.0.2 (HTML) (PDF)
        11. Jan. 5, 2009 - Modus Operandi Awarded Army Contract to Develop Framework Supporting Intelligence Analysis (HTML) (PDF)
    3. Calendar of Events
      1. Comm & C2 Capabilities Showcase - AFRL
    4. Editorial Coverage
      1. Spacecoast BUSINESS - Sept. 25, 2013
      2. Government Technology - Sept. 9, 2013
      3. Signal Magazine - Aug. 20, 2013
      4. Orlando Sentinel - Aug. 12, 2013
      5. Washington Technology - July 16, 2013
      6. Big Data Republic - June 24, 2013
      7. Bloomberg Businessweek - June 14, 2013
      8. Government Executive - April 2013
      9. Orlando Business Journal - March 20, 2013
      10. National Defense Magazine - March 2013
      11. Florida Today - Nov. 11, 2012
      12. Florida Today - July 4, 2012
      13. National Defense - May 2012
      14. Emergency Management - Jan. 26, 2012
      15. Popular Science - Nov. 2, 2011
      16. Geospatial Intelligence Forum - Oct. 2011
      17. Intelligent Utility - July 13, 2011
      18. Government Security News - Dec. 17, 2010
      19. Search SOA - Nov. 2, 2010
      20. SYS-CON Media - Oct. 5, 2010
      21. Defense Systems - July 2010
      22. Signal Magazine - June 2010
      23. Military Embedded Systems - Jan/Feb 2010
      24. Government Executive - Feb. 1, 2010
      25. C4ISR Journal - Jan. 2010
      26. Military Embedded Systems - Nov.-Dec. 2009
  13. Company
    1. About Modus Operandi
      1. Accolades
      2. Modus Operandi
      3. Our Values
    2. Leadership
      1. Leadership Team
        1. Peter Dyson, Chairman and CEO
        2. Richard McNeight, President
        3. Jeffrey Lessner, VP, Business Development
        4. Dr. Eric Little, VP and Chief Scientist
        5. Charles Keuthan, VP, Finance and Administration
      2. Board of Directors
        1. Peter Dyson, Chairman and CEO
        2. Dr. Anthony Catanese, Member
        3. Richard McNeight, President and Member
        4. James W. Thomas, Member
    3. Contracts
      1. Active IDIQ Contracts
    4. Partners
      1. Our Partners
    5. Past Performance
      1. Case Studies
      2. Our Government Customers
    6. Publications
      1. Publications
        1. Semantic Enrichment and Fusion of Multi-Intelligence Data
      2. Presentations
        1. What Makes A Wiki Semantic?
        2. Tactical Semantics: Extracting Situational Knowledge from Voice Transcripts using Ontology-Driven Text Analysis
        3. Introduction to RDF, RDFS, & OWL
        4. Semantic Fusion of Multi-INT Data
        5. SMASHUP: Secure Mashup for Defense Transformation and Net-Centric Systems
        6. Rapid Prototyping with Jena Command Line Utilities
        7. Case Study Year 5—USAF 45th Space Wing Knowledge Management Initiative: Representing Semantic Points-of-View using SITREP and Mission Ontologies
  14. Careers
    1. Current Openings
    2. Talent Search
    3. Our Values
    4. Employee Benefits
  15. Contact Us
    1. Corporate Headquarters
      1. Driving Directions
    2. Field Office
    3. Contact Us
  16. Semantic Wiki
    1. Fact Sheet
      1. Background
      2. Problems
      3. Solution
      4. Results
    2. Slides
      1. What Makes a Wiki Semantic?
      2. What's a Wiki?
      3. Example: Wikipedia
      4. Wiki Issue
      5. What's a Semantic Wiki?
      6. How are Features Achieved?
      7. Demonstration
      8. Take-aways
  17. Semantic Enrichment and Fusion Of Multi-Intelligence Data
    1. 1 Abstract
    2. 2 Introduction
      1. Discovery services
      2. Semantics
      3. Semantic enrichment
        1. Figure 1. Example of HUMINT/COMINT data streams (USMTF messages) are first transformed into XML (Step 1)
      4. Wave-EF
        1. Figure 2. Wave-EF semantically enriches multi-source intelligence data
      5. Identifying vehicle theft events
    3. 3 Methodology
      1. Extraction patterns
    4. 4 Data
      1. Figure 3. An example of a USMTF message describing a vehicle theft
    5. 5 Results
      1. Table 1. Comparison of Wave-EF, search engine and random precision, recall and F-measure statistics
      2. Table 2.
      3. Figure 4. These screenshots show the original 200 events (left)
      4. Figure 5. Semantic metadata tags generated for a vehicle theft event
    6. 6 Conclusions
    7. 7 Acknowledgements
    8. 8 References
      1. [1]
      2. [2]
      3. [3]
      4. [4]
      5. [5]
      6. [6]
      7. [7]
      8. [8]
      9. [9]
      10. [10]
      11. [11]
      12. [12]
      13. [13]
      14. [14]
      15. [15]
      16. [16]
      17. [17]
      18. [18]
      19. [19]
      20. [20]
      21. [21]
      22. [22]
  18. NEXT

Story

Data Science Makes Data More Important Than Code and Ontology

Dr. Eric Little gave a outstanding presentation to our Federal Big Data Working Group Meetup on The Evolution of Semantic Technologies-The Value of Merging Smart Data with Big Data (see Slides below). He received a number of great compliments posted to the Meetup page.

Two slides were highlights for me as follows:

My first experience with Slide 5 was from the 2003 book on the Semantic Web by Michael Daconta, et al that introduced the thought that the Semantic Web could make (Smart) Data More Important Than Code. Now Eric has extended that to: Code is More Important Than Data, Data is as Important as Code, Data is More Important Than Code, and Data Retrieval at Scale is Most Important.

Next Eric's real-world example for Slide 12 was a brilliant example of doing Data Science for Business, the book tutorial I presented prior to Eric's presentation, and specifically the CRISP data mining process. Eric explained how Business Understanding and Data ​Understanding led him to be able to narrow down the data attributes needed to construct a model and ontology to enhance a natural gas mining business.

The mushroom data set example in my book tutorial showed that even with 20 some data attributes for 8,000 some mushrooms, one could not model the data set with confidence to enable people to confidentially find non-poisonous mushrooms. One could have first constructed an ontology of those data sets attributes, but it would not have led to a successful application. Doing the Data Science first provided that conclusion quickly.

Our next Meetup features Dr. Barend Mons presentation on the BRAIN (i.e. EURETOS) as follows:

Actually, BRAIN (i.e. EURETOS) has meanwhile incorporated significantly more data sources than just Semantic Medline (a.o.UniProt and ChEMBL). In fact they also did a lot of work to turn any redundancy in triples in Semantic Medline into what we call ‘cardinal assertions’ with the underpininig ‘nanopublications’ (triple-assertions with their provenance). This speeds up the reasoning process quite spectacularly. We were just awarded a substantial grant with 7 academic partners and 12 companies (among which Nature Genetics, YARCdata and Elsevier) to take this approach to the next level. 

This is a natural follow-on to the First Meetup on Semantic Medline with YarcData and Eric's presentation because it uses more than just Semantic Medline while speeding up the reasoning process! This is also a natural follow-on to our Second Meetup where we used data science on Healthcare.gov to support development of a Be Informed Metamodel-driven Healthcare.gov application that did not use code, but created RDF/OWL "under the hood" so to speak!

Finally, Eric asked me for some open data sets from Data.gov, of which I have many, so he could provide a public data example of his semantic technologies and their results. This reminded me that my previous presentation to the John Hopkins School of Medicine showed a practical example of how to get Big Data by starting small with structured and unstructured data as relational and RDF triples stored in MindTouch and Excel and visualized in Spotfire. The Modus Operandi web site was used as the source of the practical example since most of the military data it uses is highly sensitive and classified. The purpose is to show content mashup and structuring, entity extraction and storage, and retrieval and discovery in a simple way that can be understood by non-computer and data-scientists.

This illustrates the three objectives of the Meetup presentations:

  • How was the data collected?: From the Modus Operandi Web Site and Slide Presentations
  • Where is it stored?: In MIndTouch, Excel, and Spotfire
  • What are the results?: Easy search, retrieval, and visualization across all the data from where it is stored.

Slides

Slides

Slide 1 The Evolution of Semantic Technologies-The Value of Merging Smart Data with Big Data

EricLittle02182014Slide1.PNG

Slide 2 Who is Modus Operandi?

EricLittle02182014Slide2.PNG

Slide 3 IT's common challenge

EricLittle02182014Slide3.PNG

Slide 4 Modus Approach

EricLittle02182014Slide4.PNG

Slide 5 The "Cognitive Evolution" of Intelligent Software

EricLittle02182014Slide5.PNG

Slide 6 New & Expanding Tech Areas

EricLittle02182014Slide6.PNG

Slide 7 Innovation is Key in These Types of Tech Spaces

EricLittle02182014Slide7.PNG

Slide 8 Semantic Technology

EricLittle02182014Slide8.PNG

Slide 9 Semantics and Reasoning

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Slide 10 Semantic Approach Improves Data Access

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Slide 11 Semantic Approach Simplifies Queries

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Slide 12 Building Semantic Profiles From Raw Data

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Slide 13 Utilizing Semantic to Integrate Disparate Medical Data

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Slide 14 Classification Schemas Must Reflect Subject Matter Expertise

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Slide 15 Federated Ontology Layers Allow for Advanced Data Modeling

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Slide 16 Putting It All Together Into a Platform

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Slide 17 Big Data-Now That You Have Semantic, How To Scale..

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Slide 18 The Problem of Big Data is Real (And Closing In)

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Slide 19 Big Data Analytics Challenge for Intelligent Systems

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Slide 20 Capturing Complex Data Is Difficult

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Slide 21 Scaling Semantics 1

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Slide 22 Scaling Semantics 2

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Slide 23 Avoiding the Hype Cycle

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Slide 24 Easy-To-Use Data

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Slide 25 Easy UI's Leverage Common Models

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Slide 26 Driving the Knowledge to Multiple Users

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Slide 27 Visualizing Patterns

EricLittle02182014Slide27.PNG

Slide 28 Big Data Results in a Highly Intuitive UIs in Key

EricLittle02182014Slide28.PNG

Slide 29 Providing an End-to-End Solution

EricLittle02182014Slide29.PNG

Slide 30 Thank You and Questions?

EricLittle02182014Slide30.PNG

Story

The Best Way to Get BIG DATA is By Starting Small

Our Data Science Team gave a presentation to the John Hopkins School of Medicine that was very well received. We used the example of Semantic Medline running on the YarcData Graph Appliance Eurika to show how Semantic Web RDF Triples make big data smaller, smarter, and integrated.

Semantic Medline represents 10 years of work led by NLM linguist Dr.Tom Rindflesch and is available as a MYSQL data set for non-commercial use. Two new use case for Cancer and Schizophrenia were demonstrated and are publically available as videos: Schizo (7 minutes) and Cancer (21 minutes). This appears to be the "killer" Semantic Web Application for the Federal Government.

The YarcData Graph Appliance Eurika features Large Shared Memory (up to 512 TB), Massively Multi-Threaded Processors (128 threads), and Scalable IO (up to 350 TB per hour)​. It appears to be the ultimate in-memory RDF/SPARQL standards compliant computer available today.

We then proposed a Data Science Team to support the needs of the John Hopkins School of Medicine led by Modus Operandi, that specializes in the use of semantic technologies for "Finding the Right Needle in the Right Haystack", developed from many years of SBIR-supported research and development.

Modus Operandi described the following and provided a medical data scenario:

  • Wave and the vMDC (virtual metadata catalog – which is a query translator for non-semantic queries):
    • An engine that can injest any kind of data, transform that data into RDF graphs, then do lot of semantic coolness with those graphs.
  • How Wave Drives the BLADE Semantic Wiki and Other Kinds of Analytic Visualizations:
    • The wiki is just a way to view the entities and make changes and see related content without having to type any SPARQL code or really know anything about the backend model structure - just point and click at the content you want to see.
  • The Blade 2.0 Semantic Wiki would allow different researchers to view data collectively from within their areas of expertise, but connect them to other areas effortlessly:
    • Multiple scientists working with Semantic Medline could enter their data and link it to one another across the entire corpus of the Semantic Medline data set.

Semantic Community concluded by showing a practical example of how to get Big Data by starting small with structured and unstructured data as relational and RDF triples stored in MindTouch and Excel and visualized in Spotfire. The Modus Operandi web site was used as the source of the practical example since most of the military data it uses is highly sensitive and classified. The purpose is to show content mashup and structuring, entity extraction and storage, and retrieval and discovery in a simple way that can be understood by non-computer and data-scientists.

Slides

Slides (PDF)

Slide 1 The Best Way to Get BIG DATA is By Starting Small

http://semanticommunity.info/
http://semanticommunity.info/A_NITRD_Dashboard/Making_the_Most_of_Big_Data#Story
http://semanticommunity.info/Modus_Operandi

BrandNiemann12122013Slide1.PNG

Slide 2 BIG DATA

BrandNiemann12122013Slide2.PNG

Slide 3 Subcommittee on Networking and Information Technology Research and Development (NITRD Subcommittee)

http://www.nitrd.gov/ & Web Address

BrandNiemann12122013Slide3.PNG

Slide 4 Data Science Team Example: Chief Data Science Officer

BrandNiemann12122013Slide4.PNG

 

Slide 5 Generic Problems

Making the Most of Big Data

BrandNiemann12122013Slide5.PNG

 

Slide 6 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Work FlowBrandNiemann12122013Slide6.PNG

 

Slide 7 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Semantic Medline Database Application

http://skr3.nlm.nih.gov/SemMedDB/MoreInfo.do
http://skr3.nlm.nih.gov/SemMedDB/index.jsp


BrandNiemann12122013Slide7.PNG

Slide 8 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Visualization and Linking to Original Text

BrandNiemann12122013Slide8.PNG

 

Slide 9 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Bioinformatics Publication

http://bioinformatics.oxfordjournals.../23/3158.shortBrandNiemann12122013Slide9.PNG

 

Slide 10 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Semantic Medline at NIH-NLM

BrandNiemann12122013Slide10.PNG

 

Slide 11 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: Graphs and Traditional Technologies

http://yarcdata.com/

BrandNiemann12122013Slide11.PNG

 

Slide 12 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: The YarcData Approach

BrandNiemann12122013Slide12.PNG

 

Slide 13 Semantic Medline – YarcData Graph Appliance Application for Federal Big Data Senior Steering WG: New Use Cases

Semantic Medline for Schizo (7 minutes) and Cancer (21 minutes)
BrandNiemann12122013Slide13.PNG

Slide 14 Modus Operandi: Mantra, Performance, and Vision

BrandNiemann12122013Slide14.PNG

 

Slide 15 Modus Operandi: Finding the Right Needle in the Right Haystack

http://www.spacecoastbusiness.com/mo...-intelligence/

BrandNiemann12122013Slide15.PNG

 

Slide 16 Data Science Team Example: President of Modus Operandi

BrandNiemann12122013Slide16.PNG

 

Slide 17 Wave and the vMDC (virtual metadata catalog – which is a query translator for non-semantic queries)

BrandNiemann12122013Slide17.PNG

 

Slide 18 How Wave Drives the BLADE Semantic Wiki and Other Kinds of Analytic Visualizations

BrandNiemann12122013Slide18.PNG

 

Slide 19 Possible Scenario

BrandNiemann12122013Slide19.PNG

 

Slide 20 Knowledge Base: Modus Operandi Web Intelligence in MindTouch

http://semanticommunity.info/Modus_Operandi and Web Player

BrandNiemann12122013Slide20.PNG

 

Slide 21 Big Data in Memory: Innovation Story

BrandNiemann12122013Slide21.PNG

 

Slide 22 Future: Possibility Panève’s ZettaLeaf & ZettaTree Products

http://semanticommunity.info/@api/deki/files/19353/exec_summary_20120916.pdf
http://www.paneve.com/technology/

BrandNiemann12122013Slide22.PNG

Story

Modus Operandi: The Challenges of Big Data for Intelligence Missions

I heard an excellent presentation (see Slides below) at the recent Government BIG DATA Symposium by Eric Little, PhD, VP, Chief Scientist, at Modus Operandi, in Melbourne, Florida.

Eric is part of our Semantic Data Science Team that participated in the recent White House Big Data Event (see Making the Most of Big Data).

I also met Eric's colleague, Jeffrey Lessner, VP, Business Development, and we engaged in some very productive conversations.

Modus Operandi is a company that I have followed for a number of years, mostly from their presentations at the SemTech Conferences, and through my colleague Mills Davis.

I find their mantra: Speeding the Discovery, Integration, and Fusion of Information, very interesting and decided to explore what they do by repurposing their Web content into my Semantic Wiki (MindTouch). Interestingly, they are are working on a Semantic Wiki (see Semantic Wiki). I mentioned the work I did for Gus Hunt, former CIA CTO, to make the CIA World Factbook an example of how to integrate unstructured and structured information in a Semantic Wiki.

Their work for government and private clients is rooted in a white paper published in 2006-2009 entitled Semantic Enrichment and Fusion Of Multi-Intelligence Data. Interestingly, all of their 15 additional PFD files could be converted to this Semantic Wiki like this one was.

I introduced them to YarcData where our Semantic Medline and other data discovery apps are now running.

I suggested the following possible technology partnerships:

I look forward to further discussions about Semantic Wikis building on their recent presentation at SemTech 2013 (see Slides below) that I have annotated with my answers following the colons.

What's a Wiki?

  • collaborative web site: MindTouch
  • allows non-webmasters to add content: MindTouch
  • easily create/edit pages: MindTouch
  • easily link to other pages: MindTouch (semantic search)
  • keeps edit history (allows roll-back): MindTouch
  • usually supports self-registration: MindTouch

What's a Semantic Wiki?: See Semantic Wikis

  • semantics = meaning: MindTouch Tags
  • a semantic wiki has at least these features:
    • pages can be assigned a type: MindTouch Page Type
    • page links can be assigned a meaning: MindTouch Link
    • data values (in a page) can be assigned a meaning: LInked Data

(which enables…)

  • dynamic page content via queries: Google Chrome Find, MindTouch Search, Spotfire Find, and Be Informed Search

Take-aways

  • wikis are great for information collaboration: Semantic Community
  • semantic wikis go farther, creating a collaborative, query-able, semantic knowledge model: See Census Semantic Knowledge Base

A Semantic Knowledge Model:

  • Taxonomy
  • Vocabulary
  • Rules (that connect the Taxonomy to the Vocabulary)
  • Database (Triple Store or Native Graph)

So, I am a resource person for the intelligence community and I am gathering and reporting on the Web Intelligence about Modus Operandi (MO) and Project TYCO Data for Health:

  • Knowledge Base: MO Web Intelligence in MindTouch
  • Spreadsheet: MO Knowledge Base in Excel
  • Big Data Database: MO Knowledge Base and Project TYCO Data for Health in Excel
  • Dashboard: MO Knowledge Base and Project TYCO Data in Spotfire

I also consider myself to be an information designer that produces both works of art and advanced functionality.

To find the Big Health Data, I did the following: I remembered seeing some big health data at:

Data Science Central: which led to

Update about our Data Science Apprenticeship: which led to

Pitt Unlocks 125 Years of Public Health Data to Help Fight Contagious Diseases: which led to

Project TYCO Data for Health: which led to

Selecting a sample of three levels of data. See Research Notes below for more details.

MORE TO FOLLOW

Florida Institute of Technology - Future Data Science Program?

Slides

Slides

Slide 1 The Challenges of Big Data for Intelligence Missions

EricLittle11222013Slide1.png

Slide 2 Who is Modus Operandi?

EricLittle11222013Slide2.png

Slide 3 Big Data Analytics Challenge for Intelligence

EricLittle11222013Slide3.png

Slide 4 The Problem of Big Data is Real

EricLittle11222013Slide4.png

Slide 5 Mitigating the Big Data Hype Cycle

EricLittle11222013Slide5.png

Slide 6 Intel Involves Understanding Situational Awareness

EricLittle11222013Slide6.png

Slide 7 Fusing Data Requires Complex Architectures

EricLittle11222013Slide7.png

Slide 8 Capturing Complex Events Using Semantics

EricLittle11222013Slide8.png

Slide 9 Driving the Knowledge to Multiple Users

EricLittle11222013Slide9.png

Slide 10 Querying Events Using Graph Analytics

EricLittle11222013Slide10.png

Slide 11 Delivering Big Data Results in a Highly Intuitive UI

EricLittle11222013Slide11.png

Slide 12 Linking Information Together

EricLittle11222013Slide12.png

Slide 13 Accessing Data Via A Semantic Wiki

EricLittle11222013Slide13.png

Slide 14 Finding Big Data Expertise is a Growing Issue

EricLittle11222013Slide14.png

Slide 15 As With Many New Technologies – “Draft & Develop”

EricLittle11222013Slide15.png

Slide 16 Thank You Questions?

EricLittle11222013Slide16.png

Slide 17 Modus Operandi: The Challenges of Big Data for Intelligence Missions

http://semanticommunity.info/
http://datacommunitydc.org/blog/2013...ce-conference/
https://silverspotfire.tibco.com/us/...niemann/Public
http://semanticommunity.info/Modus_Operandi

https://silverspotfire.tibco.com/us/library#/users/bniemann/Public?ModusOperandi-Spotfire.dxp

BrandNiemann12022013Slide1.PNG

Slide 18 Modus Operandi: Finding the Right Needle in the Right Haystack

http://www.spacecoastbusiness.com/mo...-intelligence/

BrandNiemann12022013Slide2.PNG

Slide 19 Purpose and Goal

BrandNiemann12022013Slide3.PNG

Slide 20 Knowledge Base: MO Web Intelligence in MindTouch

http://semanticommunity.info/Modus_Operandi

BrandNiemann12022013Slide4.PNG

Slide 21 Spreadsheet: MO Knowledge Base in Excel

http://semanticommunity.info/@api/deki/files/27332/ModusOperandi.xlsx

BrandNiemann12022013Slide5.PNG

Slide 22 Big Data Database: Project TYCO Data for Health in Excel

http://www.tycho.pitt.edu/

BrandNiemann12022013Slide6.PNG

Slide 23 Dashboard: MO Knowledge Base and Project TYCO Data in Spotfire 1

BrandNiemann12022013Slide7.PNG

Slide 24 Dashboard: MO Knowledge Base and Project TYCO Data in Spotfire 2

BrandNiemann12022013Slide8.PNG

Slide 25 Some Next Steps

BrandNiemann12022013Slide9.PNG

Slide 26 Florida Institute of Technology: Learning/Mining and the Internet

http://cs.fit.edu/~pkc/classes/ml-internet/

BrandNiemann12022013Slide10.PNG

Spotfire Dashboard

For Internet Explorer Users and Those Wanting Full Screen Display Use: Web Player Get Spotfire for iPad App

Research Notes

Project TYCHO Data for Health

Level 1 data

These data have been custom tailored for specific analyses. These are the most complete and standardized data but for a limited number of diseases, locations, and years.

Current version: 1.0.0

Indicators: cases and incidence rates
Diseases: 8
Locations: 50 states and 122 cities (diphtheria only)
Years: 1916 to 2009

Go to level 1 data

Result: Downloaded Spreadsheet and Cleaned Spreadsheet

Level 2 data

These are standardized data for immediate use and include a large number of diseases, locations, and years. These data are not complete because standardization is ongoing.

Current version: 1.0.0

Indicators: cases and deaths
Diseases: 47
Locations: 50 states, 6 territories, and 1287 cities
Years: 1888 to 2013

Go to level 2 data

Result: Downloaded Spreadsheet and Cleaned Spreadsheet

Level 3 data

These data have not been standardized at all and cannot be used for analysis. These data are provided upon request.

Current version: 1.0.0

Indicators: cases, deaths, and summary statistics
Diseases: 56
Locations: 10 regions, 50 states, 6 territories, 3165 counties, and 2906 cities, towns, and parishes
Years: 1887 to 2013

Level 3 data request form

Result: Thank you. Your request for Level 3 data has been submitted. You will receive an email when your dataset is ready to be dowloaded.

Level 2 data
https://www.tycho.pitt.edu/data/level2.php
Project Tycho™ level 2 data include data counts that have been filtered from the raw data to render standardized data that can be used immediately for analysis. All level 2 data have been reported in a common format and have not been transformed into a standard format by us, except for smallpox records that included repeated counts for the same location and week, but sometimes with different numbers. These duplicate smallpox records have been averaged into one count for each location and week. Level 2 data include counts for a wide variety of diseases and locations for varying time periods. Because not all data have been reported in a standardized format, level 2 data may be missing for certain diseases, locations, or years. We will continue to transform non-standard counts in a common format and will make these available in future versions of these data. Click here to read more about different types of counts and data standardization methods. Data availability is also dependent on historical reporting priorities and not all diseases were reported by all locations every year.

Version 1.0.0

The current version (1.0.0) includes counts at the city and state level for 47 contagious diseases for the entire 1888-2013 time period. The available years vary across locations and diseases. Version 1.0.0 includes counts for cases or deaths reported, depending on the time period and disease.

Modus Operandi

Source: http://www.modusoperandi.com/index.html

Create DDMS Metacards Automatically

Feature Image 1

My Note: See Metadata Engineer

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Our DI2E solutions can help by making intelligence data from multiple sources both visible and accessible to the broader intelligence community. More>>

 
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My Note: See DI2E Solutions

Drowning in Text?

If you have to read and analyze large amounts of unstructured information, our Wave Exploitation Framework can help by identifying people, things and events of interest. More>>

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Our GOTS capabilities and integration approach reduce time and cost to deliver a solution to the warfighter. More>>

My Note: See Government Agencies

Modus Operandi NEWS

As Modus Operandi President Rick McNeight likes to say, the defense company’s software doesn’t just find a needle in a haystack, it finds the right needle in the right haystack ...

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Spacecoast BUSINESS

My Note: See Finding the Right Needle in the Right Haystack

Solutions

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GOTS solutions for sharing intelligence products within the DI2E.

Our DI2E Solution Advantages

  • Post Quicker.

    The automated generation process works much faster and more consistently than a human could do it, so products get posted to the DIB sooner.
  • Community Interoperability.

    Use validated Community Crosswalks to facilitate sharing of products.
  • Easily Adapt to Change.

    When input or output formats change, don't write new code. Instead, to add new report formats, or accommodate changes or variants of existing formats, simply modify the crosswalk rules. This will also make it easier to comply with new output formats such as UCore or DDMS 3.1.

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The Wave Metacard Generator and XSLT Generator automatically create metacards for incoming CCDF XML instances and other report types, and publish them to a DI2E Integration Backbone (DIB) Metadata Catalog.

 

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Modus Operandi offers a variety of support services to meet your needs.

 

If you are a... MO offers the following services:

 

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SIGINT Jump Start 

Configure and validate your Metacard Generator in 2 weeks per report format.
 

 

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Configure a Metacard Generator based on a community crosswalk (e.g., IMINT, HUMINT, MASINT, etc.) for your report format(s). 

 

Crosswalk Assistance 

Provide expert consulting services and tools to assist with generation and validation of community crosswalks, including transformation business rules. 

 

Impact Assessment

Perform an impact assessment for complying with new metadata requirements such as DDMS 3.1 or UCore.
 

Systems Integrator 
or Field Service Organization

 

Basic Production Support

Provide software updates plus Tier 2 email and phone support that covers software installation and basic tool operation.

 

 

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Custom support packages to meet your needs above and beyond basic production support. This could include, for example, software enhancements, training, and integration assistance.

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Source: http://www.modusoperandi.com/WaveEF.html

Exploit Unstructured Intelligence Sources

FIND AND CORRELATE ESSENTIAL ELEMENTS OF INFORMATION BURIED IN STRUCTURED AND SEMI-STRUCTURED INTELLIGENCE PRODUCTS.

Advanced Text Exploitation

Wave is a government-off-the-shelf (GOTS) technology for intelligence applications that tackles the difficult problem of processing unstructured and semi-structured data.

The Wave-EF Cost and Time Advantage

Wave solutions offer an alternative to traditional acquisition of custom software. By combining MO-developed GOTS and open source software, we bring our customers high quality, high capability solutions at a fraction of the traditional time and cost.

 

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Supercharge the Soft Side of Your Analysis Environment

In irregular warfare, often the most valuable information for intelligence analysis is buried in unstructured, or soft, information sources. Modus Operandi’s Wave Exploitation Framework (Wave-EF) automatically identifies key concepts and relationships in unstructured data, tags them, and feeds them to analysts’ tools for correlation and visualization. Wave-EF frees analysts from tedious reading, markup and search tasks so that they focus on the big picture. By putting unstructured text on a par with more structured data, Wave-EF enables true all-source analysis.

Wave Solution Concepts

Complex Concept Extraction

Wave-EF extracts 14 essential elements of information (EEIs):

persons, locations, communications, vehicles, organizations, groups, internet components, targets, activities, blue force, phone numbers, weapons, facilities, units, control measures.

Many basic text analytic tools concentrate on finding entities which are nouns. But that is not sufficient to extract the fundamental triad of intelligence fusion: Entity-Location-Time. Examples of this triad are when a High Value Individual was observed at a specific location and time, or a suicide bombing incident is planned to occur at a particular place and time.

Wave-EF takes extraction to the next level by treating these basic EEIs as concept building blocks. Relationship extractors tie concept building blocks together to describe more sophisticated constructs such as events. An IED incident, for example, is a type of event that can be linked to numerous concepts, such as multiple persons, organizations, locations, and times; to descriptive signature concepts such as detonation method, explosive type, and concealment method; and to effects such as destruction and casualties.

Content Enrichment with Semantic Tags

Content enrichment does for text what STP® Fuel Treatment does for gasoline—it makes the parts that consume it work more effectively. Content enrichment with semantic tags transforms unstructured text into content with meaning that both computers and humans can recognize. In a data mining scenario, these tags can be used by analysts to find specific intelligence products by searching for specific entities, for abstract concepts, or for relationships among entities that are contained within the product. In an alerting scenario, these tags can be examined by a semantic filter that determines whether the product is relevant to the analyst's expressed requirements. Get datasheet>> (PDF)

Customers

C4ISR Customers

Source: http://www.modusoperandi.com/Customers.html

Customers Collage

ISR systems continue to grow in complexity and mission criticality; however the ability to collect data has far outpaced the capacity to fully leverage it.

MODUS OPERANDI PROVIDES ADVISORY, DESIGN, AND IMPLEMENTATION SERVICES TO MEET OUR CUSTOMERS' NEEDS.

Our Government Customers

  • U.S. Air Force - AFRL, ESC, 45th Space Wing, ISR Agency
  • U.S. Army - CECOM, RDECOM
  • U.S. Marine Corps
  • U.S. Navy - NAVSEA, ONR, SPAWAR
  • DARPA
  • DTRA
  • Missile Defense Agency
  • Intelligence Agencies

Department of Defense (DoD)

The DoD’s network centric environment makes raw data directly available to authorized personnel. By semantically enriching that raw data, warfighters are provided with superior situational awareness.

ISR Technology Challenges

  • Data fusion
  • Text analytics
  • Semantic enrichment
  • Interoperability
  • Complex event processing
  • Reasoning/search

Intelligence Community

Agency analysts need the most complete set of relevant data possible in order to assess and communicate threats and maintain homeland security. Delivering enriched intelligence data addresses the specific needs of role-based analysts.

Complex Event Processing and Consequence Management

Operators need the best available understanding of emergency response situations so they can provide optimal notification plans to appropriate personnel. Correlating and fusing data within an expected area of threat enables dramatically improved consequence management.

Technology Services
Metadata Engineer

Modus Operandi's new Wave Metacard Generator and XSLT Generator.

DOD COMMUNITY SOURCE SOLUTION

Get Metadata Guru Services

Modus Operandi offers packaged support services for our Metacard Generator, such as:

and we are happy to customize services to meet your needs

  • Crosswalk Validation Assistance
  • Format Ingest and Transformation
  • Training

Publish Your Data to the DIB

The Wave® Metacard Generator and XSLT Generator are companion Java applications that work together to automatically create metacards for intelligence products, such as CCDF XML instances and other report types, and publish them to a DI2E Integration Backbone (DIB) Metadata Catalog. Download the data sheet. >> (PDF)

 

Wave Metacard Gen

Metadata in the News

Better Image Tagging Improves Warfighter Situational Awareness

DEFENSE SYSTEMS MAGAZINE

Defense Systems

As networking makes all sorts of data readily available to warfighters, technical planners are continuing to enhance techniques to make sure warfighters can find the files they need. Standards for tagging and data management provide structure for searches, while improved storage techniques help make those searches faster. More>>

Too MUch To Read?
Technology Services
Intelligence Analyst

Be Bold

  • Read the white paper to learn more about how to apply semantic web technologies to solve intelligence processing, exploitation, and dissemination problems.
  • - How semantic enrichment of intelligence products makes them more exploitable
  • - How to measure the "goodness" of entity extraction technology
  • - Concrete examples of some of the challenges of free or unstructured text

Wave-EF Semantic Filtering

LEARN HOW TO TAME YOUR TEXT.

Semantic Enrichment and Fusion of Multi-Intelligence Data White Paper>> (PDF)

Get More Tech Savy

 

Wave Solution Concepts

Modus Operandi's Wave Exploitation Framework harnesses the power of text analytics and semantic markup to solve intelligence exploitation problems. >>

Catch The Buzz

National Defense

TOO MUCH INFORMATION, NOT ENOUGH INTELLIGENCE

Emergency Mgmt

The Defense Department over the last decade has built up an inventory of billions of dollars worth of spy aircraft and battlefield sensors. Those systems create avalanches of data that clog military information networks and overwhelm analysts. More>>

R & D Organizations

Source: http://www.modusoperandi.com/RnDOrg.html

R & D Productization and Tech Transfer

Technology Services
R & D Organizations

Our Research Focus

TEXT ANALYTICS, DATA DISCOVERY & SHARING, AND CYBER TECHNOLOGY.

Our R&D Sponsors

  • U.S. Air Force Research Laboratory (AFRL)
  • U.S. Army Research Laboratory
  • U.S. Army RDECOM
  • Office of Naval Research (ONR)
  • U.S. Navy SPAWAR
  • DARPA

R & D Technology Insertion

Modus Operandi is serious and diligent about technology insertion and has made it an integral part of our R&D approach. We establsih an early dialog with end users and stakeholders. We foster collaboration between the development team, the R&D sponsor and the target insertion programs. We plan for achievement of a TRL that enables successful technology insertion into the mission.

Modus Operandi has a long history of successful Small Business Innovative Research (SBIR) and Broad Agency Announcement (BAA) contract awards which have helped to develop and transition our technology to programs of record.

Learn more about how Modus Operandi has applied technology developed on our R&D projects to solve problems in the defense and intelligence community.

Wave-EF Semantic Filtering

LEARN ABOUT HOW SEMANTIC FILTERING REDUCES CLUTTER.

Semantic Enrichment and Fusion of Multi-Intelligence Data White Paper>> (PDF)

R & D Productization

 

Wave Solution Concepts

Modus Operandi's Wave Exploitation Framework harnesses the power of text analytics and semantic markup to solve intelligence expoitation problems. >>

Recent Publications

Tactical Semantics: Extracting Situational Knowledge from Voice Transcripts using Ontology-Driven Text Analysis

Presentation by Dr. Kent Bimson and Dr. Teresa Nieten (PDF)
SemTech Conference 
June 2012
San Francisco, CA

Introduction to RDF, RDFS, & OWL

Presentation by Mark Wallace (PDF)
SemTech Conference 
June 2012
San Francisco, CA

Semantic Fusion of Multi-INT Data

Presentation by Dr. Richard Hull (MO) and Dr. Mark Gerken (ISS) (PDF)
IDGA ISR Summit

Nov. 14, 2011
Wash., DC

Rapid Prototyping with Jena Command Line Utilities

Presentation by Mark Wallace (PDF)
SemTech Conference 
June 2011
San Francisco, CA

Prime Contractor

Source: http://www.modusoperandi.com/PrimeContractor.html

Trusted Small Business Partner

Technology Services
Prime Contractor
Modus Operandi has mutually beneficial partnerships to help serve our customers.


A SMALL BUSINESS BRINGING REAL TECHNOLOGY SOLUTIONS TO MISSION CRITICAL SOFTWARE SYSTEMS DEVELOPMENT AND INFORMATION INTEGRATION NEEDS.

Our Government Customers Include

  • U.S. Air Force - AFRL, ESC, 45th Space Wing, ISR Agency
  • U.S. Army - CECOM, RDECOM
  • U.S. Marine Corps
  • U.S. Navy - NAVSEA, ONR, SPAWAR
  • DARPA, DTRA
  • Missile Defense Agency
  • Intelligence Agencies

IDIQ Contracts

Our IDIQ contracts - customers - primes:

  • Agile Cyber Technology - Air Force - AIS, Inc.
  • Battle Command of Products - U.S. Army - Lockheed Martin
  • C2AD - Air Force-ESC - Solers
  • ENCORE II - DISA - BAE
  • Harmony III - U.S. Army - Modus Operandi
  • MiDAESS - MDA - Cobham/ SPARTA
  • R2-3G - U.S. Army CECOM - R4 and CSC
  • SeaPort-e - Navy/USMC - eSTS
  • SETAC10 - U.S. Army SMDC - Radiance
  • S3 - U.S. Army CECOM - BAH, MSTI and Lockheed Martin
  • SSES NexGen - U.S. Army CECOM - Various

Past Performance

These case studies demonstrate how Modus Operandi delivers value to our customers.

Government Agencies

Source: http://www.modusoperandi.com/GovtAgency.html
 

GOTS Solutions For Constrained Budgets
Technology Services
Government Agency

DOD COMMUNITY SOURCE SOLUTION

DoD Community Sources Advantages

  • Zero license fee saves money up front and reduces license administration and compliance effort down the road
  • Eliminates vendor lock-in concerns
  • Reduces redundant development efforts

Modus Operandi Offers GOTS Products, Open Source and License-free Software

Modus Operandi's most recent DCS offering is a solution for automatically generating DDMS metacards, called the Wave Metacard Generator and XSLT Generator. 

Download the data sheet (PDF)

.Wave Metacard Gen

GOTS Software

Modus Operandi offers an alternative to traditional acquisition of custom software, based on MO-developed GOTS products, open source, and R&D funded software technology in order to bring our cstomers high quality, high capability solutions at a fraction of the traditional time and cost.

DoD Community Source is a category of GOTS software, like open source for government.

News & Events

What's new at Modus Operandi?

Events: Modus Operandi participates in many trade shows and conferences throughout the year. Whether you are a customer, partner, or just getting to know us, we look forward to seeing you at one of these upcoming events.

Recent Editorial Coverage

Spacecoast BUSINESS - Sept. 25, 2013

MODUS OPERANDI DELIVERS INFORMATION-BASED INTELLIGENCE: FINDING THE RIGHT NEEDLE IN THE RIGHT HAYSTACK

Spacecoast BUSINESS

Finding the Right Needle in the Right Haystack

Source: http://www.spacecoastbusiness.com/modus-operandi-delivers-information-based-intelligence/

by Carl Kotala

Introduction

As Modus Operandi President Rick McNeight likes to say, the defense company’s software doesn’t just find a needle in a haystack, it finds the right needle in the right haystack.

The Melbourne-based company has developed a way to mine specific pieces of data from a host of sources – everything from social media to bank records, e-commerce and intelligence information reports – and provide actionable intelligence to military analysts.

The software can do in minutes what it would take an analyst weeks to research considering the massive volumes of data involved. “It’s almost like you’re doing a Google search, but it’s far more powerful,” McNeight explained. “You can’t use Google across the government databases.”

Founded in 1984, the company was originally known as Software Productivity Solutions Inc. It started out providing technology for defense research labs, but in 1998 its leaders decided to venture into the commercial market. It was renamed Modus Operandi Inc. following a merger and began working with banks, insurance companies and even Florida
Power & Light.

Re-Fitting, Re-Focusing

The commercial market became roughly half of the company’s business, but that all changed in 2001 when the commercial bubble burst. “The wheels kind of fell off the commercial business, so we decided to exit that and kind of start it over,” said CEO Peter Dyson, an original company founder.

That meant a full-time return to defense-oriented business. When McNeight came on board in 2006, Modus Operandi began to target a more narrow market so it could begin to build depth in understanding the customer’s problems and needs, and of course, how to solve them. Deciding to concentrate on the intelligence base, they hired former military intelligence officers who could not only help them understand what the analysts were looking for, but also be a valuable asset when talking to potential clients.

The company also took great advantage of the government’s Small Business Innovative Research program (SBIR). “Once we decided to focus (on this particular area), then how do we find work that matches that focus?” McNeight asked. “This program is very interesting because each of the agencies, over a certain size, have to set 2-1/2 to 3 percent of its budget aside for this (SBIR) program. What they do is put out abstracts requesting research being done in specific areas.”

Modus Operandi targeted the projects that not only fit its talent base, but also followed the company’s mission to create cutting-edge technology. “We’ve been very successful in that program,” McNeight said. “As a matter of fact, over the past 10 years, we’ve won over $40 million in business in the SBIR program.”

It’s A Matter of Semantics

With that backing, Modus Operandi has developed semantic software that can comb through a multitude of data from a host of different sources and identify key areas of information. The parameters are set by the analyst. “Usually the customer, depending on the mission, has certain things they’re looking for,” Dyson said. “So a lot of what we’re doing is enabling that by making the data sources accessible and searchable.”

“(Government agencies) have thousands of different legacy systems and data sources that are all little stovepipes of information. A lot of it is knocking down the barriers to accessibility of that information. Our specialization is what we call ‘semantic technology,’ which is just a way of making the data smarter. We enrich the data with various tags to make it easier to find.”

The software also provides what McNeight called “providence,” which allows the analyst to go back and see where the source of that information came from to determine how credible it is. What happens next is up to the analyst, not the software.

“We don’t make decisions,” McNeight explained. “We just help (the analyst) to make decisions and to find the right data. He may only be interested in a certain person in a certain location at a certain time. We can bring that back to him across multiple databases.”

Growth in Personnel and Products

Modus Operandi has experienced tremendous growth recently, including hiring 40 new employees in the past year to double the number of the current staff. The company also has some big projects in the works, including a new Navy research contract that will create a “crowdsourcing” analysis system, which will analyze social media and feed information to emergency response teams dealing with disasters, crowd uprisings, fires, crimes and a host of other crises. Dyson said the technology could be ready in two years.

The company is also testing a new product, called “Blade,” which essentially puts a visual wrapper around its core search technology. “It provides all the visualization to the analyst, all the different ways they can display the data. One of the ways is that you have a person and connectors to all the little circles around him — people he’s associated with, their locations or organizations — and then you can click on those and they expand out (to see who else is in that organization),” said Dyson.

“Those visualization tools are something we’re now wrapping this with, along with more sophisticated search capabilities.” While the company has certainly come a long way since it essentially re-invented itself 10 years ago, future projects like Blade and the crowdsourcing Navy contract have the potential to take Modus Operandi to even greater heights.

“These are very exciting times for our company,” Dyson said. “Our team is very energized right now. In spite of the challenges of the federal budget sequester, we have been posting solid orders and good revenue growth. We have a lot of great talent on the team right now. Any success that Rick or I can point to is thanks to the efforts of our people.

Said Dyson, “Basically we have a convergence of a few key factors that gives us great confidence in the future — the talent and dedication of our people, a market need for innovative technology to tame the big data tsunami, and a critical mass of customer funding to build out and deploy our technology solution.”

News Releases

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2013

 

2012

 

2011

 

2010

 

2009

 

Calendar of Events

Modus Operandi participates in many trade shows and conferences throughout the year.

Trade Shows and Conferences

WHETHER YOU ARE A CUSTOMER, PARTNER, OR JUST GETTING TO KNOW US, WE LOOK FORWARD TO SEEING YOU AT ONE OF THESE UPCOMING EVENTS.

Comm & C2 Capabilities Showcase - AFRL

Oct. 29, 2013

Rome, NY

Exhibitor - Tabletop

Editorial Coverage

Read about exciting developments at Modus Operandi reported in the electronic and print media. For more information, contact us.

In The News

EDITORIAL COVERAGE

Spacecoast BUSINESS - Sept. 25, 2013

MODUS OPERANDI DELIVERS INFORMATION-BASED INTELLIGENCE: FINDING THE RIGHT NEEDLE IN THE RIGHT HAYSTACK

Spacecoast BUSINESS

As Modus Operandi President Rick McNeight likes to say, the defense company’s software doesn’t just find a needle in a haystack, it finds the right needle in the right haystack. The Melbourne-based company has developed a way to mine specific pieces of data from a host of sources – everything from social media to ...More>>

Government Technology - Sept. 9, 2013

NAVY EXPLORES POTENTIAL OF SOCIAL MEDIA CROWDSOURCING IN DISASTER RESPONSE

Government Technology

The U.S. Navy selected Modus Operandi to begin developing crowdsourcing situational awareness software that leverages data from social media. Social media is becoming a primary source of information during disasters like earthquakes, tornadoes and hurricanes. In an effort to more efficiently utilize... More>>

Signal Magazine - Aug. 20, 2013

NAVY SURFS THE CROWD

Signal

The U.S. Navy is turning to crowdsourcing as a possible situational awareness aid during disasters and social unrest. Data from eyewitnesses or participants would be fused with information from other sources to provide timely understanding and appreciation of an environment or location to response teams. More>>

Orlando Sentinel - Aug. 12, 2013

DEFENSE COMPANY TO DEVELOP NEW CROWDSOURCING TECH FOR DISASTER RESPONSE

Orlando Sentinel

A Melbourne company said Monday it has landed a Navy research contract to create a "crowdsourcing" analysis system to assist emergency-response teams in disasters, crowd uprisings, fires, crimes and other crises. More>>

Washington Technology - July 16, 2013

HOW ONE EXEC USED RESEARCH GRANTS TO FUEL HIS SMALL-BUSINESS SUCCESS

Government research grants give companies a chance to flex their innovation muscles. (Article by subscription only) More>>

Big Data Republic - June 24, 2013

WAR ZONE SEMANTICS

Big Data Republic

"Truck bombs are a big problem," said Rick McNeight, president of Modus Operandi, a software company "serving the US defense and intelligence community," on the phone to me, and I can't help but agree. The conversation we had is not one I'm used to having, but there's a big data angle on it. More accurately, there's ... More>>

Bloomberg Businessweek - June 14, 2013

NEW 'GAME-CHANGING' RULES FOR INNOVATION GRANTS FROM THE FEDS

Bloomberg Bizweek

Among the roughly 15,000 beneficiaries of the program is Richard McNeight, president of 80-employee Modus Operandi. His Melbourne (Fla.) business has received $40 million in research awards over the past decade through SBIR.More>>

Government Executive - April 2013

WELCOME TO THE DATA DRIVEN WORLD

Govt Exec

Big data is having an impact across government, though, in areas far afield from fraud detection. The data analysis company Modus Operandi received a $1 million Army contract in late 2012 to build a system called Clear Heart, which would dig through hundreds of hours of video — including footage from heavily populated areas — and pick out body movements that suggest what officials call "adversarial intent." More>>

Orlando Business Journal - March 20, 2013

MODUS OPERANDI GETS $1.5M MARINE CORPS. CONTRACT

OBJ

Modus Operandi was awarded a $1.5 million U.S. Marine Corps. contract to develop software to discover and analyze tactical intelligence. Melbourne-based Modus Operandi will enhance and package its Blade Semantic Wiki software for transition into a number of Marine Corps analytics systems. The contract is called ieDENI (improvised explosive device denial). More>>

National Defense Magazine - March 2013

COMPANIES INTRODUCE NEW SOFTWARE TO DEFEAT SUICIDE BOMBERS

Natl Defense

The Army in December awarded Modus Operandi, a Melbourne, Fla.-based software company, a $1 million contract to develop a system that can detect gestures that indicate criminal intent or activity. More>>

Florida Today - Nov. 11, 2012

NEWSMAKER SPOTLIGHT: LESSNER MEANS BUSINESS AT SOFTWARE COMPANY

Emergency Mgmt

Jeff Lessner is the new vice president of business development at Modus Operandi in Melbourne. Lessner will be responsible for all aspects of the company's business development efforts, including full oversight of sales and marketing efforts, market analysis, and management of business proposal production. More>>

Florida Today - July 4, 2012

LOCAL FIRM ASSISTS IN SIFTING OF SPY DATA

Emergency Mgmt

Computers search vast amounts of information, and do so very quickly, but a Melbourne defense contractor has created software that helps a computer narrow what it's looking for, adding to their power and usefulness.

Just ask the military. More>>

National Defense - May 2012

TOO MUCH INFORMATION, NOT ENOUGH INTELLIGENCE

Emergency Mgmt

The Defense Department over the last decade has built up an inventory of billions of dollars worth of spy aircraft and battlefield sensors. Those systems create avalanches of data that clog military information networks and overwhelm analysts. More>>

Emergency Management - Jan. 26, 2012

CYBER SECURITY ISSUES AND SOLUTIONS

Emergency Mgmt

I had the opportunity to chat with Peter Mozloom, VP, Cyber Solutions, Modus Operandi about cyber security here in the United States. This discussion included the threats and the status of our preparedness efforts and what we can do better. More>>

Popular Science - Nov. 2, 2011

CAN TECHNOLOGY SAVE THE MILITARY FROM A DATA DELUGE?

Pop Sci

Modus Operandi and companies like it are scrambling to create natural language processing and textual analytics that allow machines and people to share a common language. More>>

Geospatial Intelligence Forum - Oct. 2011

INTELLIGENCE FAMILY

GIF Online

Some of the nuts and bolts that will enable DCGS to exploit legacy capabilities are still under development, with a company called Modus Operandi working on several. MO is working on a system that would automatically reconfigure information from legacy systems into an intermediate format, from which it would be pulled by intelligence users and retranslated... More>>

Intelligent Utility - July 13, 2011

CYBER EXPERTISE LACKING?

GSN Online

Modus Operandi, Inc. works on connecting disparate, human-generated intelligence and reports by using "natural language" science so the massive volume of verbal and written commentary generated by myriad intelligence sources yields actionable responses. The underlying theme between that pursuit and utility security is "information assurance," according to Peter Mozloom... More>>

Government Security News - Dec. 17, 2010

ANALYTIC TOOLS DESIGNED TO CATCH TERRORISTS MIGHT ALSO BE USEFUL IN CYBER-SPACE

GSN Online

For years, a company called Modus Operandi, Inc. has been developing software tools that help intelligence analysts extract bits-and-pieces of valuable information from a wide variety of printed materials by finding patterns and relationships among those pieces of data that can help the analyst identify terrorists and the plots they are hatching. More>>

Search SOA - Nov. 2, 2010

SOA, SEMANTICS AND SERVICES COMBINE IN DOD INTELLIGENCE SHARING EFFORT

SearchSOA

The concept of sharing does not always come naturally to human beings or legacy systems. But a service-oriented architecture effort within the U.S. Department of Defense's intelligence community is helping DoD in the effort to better share information as well as modernize systems. More>>

SYS-CON Media - Oct. 5, 2010

SERVICE-ORIENTED ARCHITECTURE AND CYBER SECURITY

SYS-CON

The framework this firm has developed, can allow an analyst to produce essential and immediate field ready intelligence within a timeline which enables effective counteraction e.g. data which normally takes several days to parse and interpret can now be parsed and interpreted within several hours or minutes. More>>

Defense Systems - July 2010

A GIANT LEAP FORWARD IN INTELLIGENCE SHARING

Defense Systems

One of the biggest problems that Defense Department intelligence analysts face isn't a lack of information — rather, it's finding the right information buried in the sea of data that exists on DOD's intelligence, surveillance and reconnaissance networks. More>>

Signal Magazine - June 2010

GOOGLIZING INTELLIGENCE

SIGNAL

Modus Operandi Incorporated, Melbourne, Florida, has spent the last several years designing semantic software that tags text in a way that enables intelligence analysts to extract key information about designated entities. More>>

Military Embedded Systems - Jan/Feb 2010

TOP TECHNOLOGIES FOR THE WARFIGHTER

MES

Companies like Modus Operandi have won DARPA contracts to take open source data and use it for tactical exploitation. More>>

Government Executive - Feb. 1, 2010

FEATURES UP IN THE AIR

Government Exec

"All types of sensors are getting cheaper and easier to deploy. The challenge we face is how to help analysts make sense of this overwhelming volume of data. To compound matters it's not just one type of data - human intelligence, imagery, signals intelligence, all of which come in different formats."  More>>

C4ISR Journal - Jan. 2010

THE GET-WELL PLAN

C4ISR Journal

"We're not trying to replace them (analysts)... We're trying to help them do their jobs better." His company has a tool that uses key words to analyze print matter  More>>

Military Embedded Systems - Nov.-Dec. 2009

C4ISR AND THE BIG PICTURE

Military Embedded Systems

By overlaying sensor data with human text documents, such as "… at 20:00 hours there was single vehicle activity at the Northern border crossing …", commanders will get a different view of the battlefield to aid in decision making. More>>

Company

About Modus Operandi

Source: http://www.modusoperandi.com/CompanyProfile.html

Operations

 

Speeding the discovery, integration and fusion of information

FOR CUSTOMERS IN THE DEFENSE AND INTELLIGENCE COMMUNITY.

Accolades

  • 2010 Defense Threat Reduction Agency (DTRA) Award for Exceptional Support at EC10
  • 2009 Letter of appreciation for outstanding support to the PEO IEW&S Operations Cell / ISR Taskforce for Afghanistan
  • 2008 Intelligence and Security Command (INSCOM) Certificate Outstanding Support to the Guardrail Ground Baseline (GGB)
  • 2005 PM Aerial Common Sensor (ACS) Certificate of Appreciation for Net Centric Collaborative Targeting Exercise
  • 2009 Software Innovation Award (Lockheed Martin)
  • 2007 & 2006 Small Business Administration Award for Excellence
  • 2006 USAF 45th Space Wing Team Excellence Award (Gen. Mark Owen)
  • 2006 & 2005 Economic Development Commission Industry Appreciation Award
  • 2005 Economic Development Commission Information Technology Sector Job Creation
  • 2004 U.S. Army Communications-Electronics Command (CECOM) Quality Team of the Year Award (L3 Communications small business team member)

Modus Operandi

Modus Operandi is a high-tech software company serving the U.S. defense and intelligence community. Our purpose is to help defend and protect our country, our allies, and our planet by providing software technology to speed information discovery, integration and fusion.

We are focused on the development and marketing of technology, services and solutions to deliver information-based intelligence capabilities. Our solutions combine innovative semantic technology with defense sector software systems development experience in the command, control, communications, computers, intelligence, surveillance and reconnaissance (C4ISR) domain.

Through a combination of customer funding and private investment, Modus Operandi has developed leading edge technologies for multi-intelligence data enrichment and fusion. Our Wave® technology, implemented in a service-oriented architecture (SOA), enables analysts to rapidly discover relevant information from structured and unstructured data, reason over the data to increase situational awareness, and ultimately, speed the delivery of actionable intelligence to warfighters.

Our defense customers span the U.S. Air Force, Army, Navy, Marine Corps, Missile Defense Agency, Defense Threat Reduction Agency, and the Defense Advanced Research Projects Agency. The typical customer mission environments we support are characterized by large volumes of data from multiple sources, in multiple formats, and representing multiple types of intelligence.

Our Values

  • People – We bring an entrepreneurial spirit and teamwork to everything we do.
  • Customer Service – We partner for quality results that meet or exceed shared expectations.
  • Innovation – Unique solutions that encompass process, product, and technology.
  • Integrity – Displayed through a culture of trust, respect, and honesty.
  • Quality – In our people, processes, and products.

Leadership

Management is doing things right; leadership is doing the right things. Peter F. Drucker

Leadership Team & Board of Directors

Leadership Team

Peter Dyson, Chairman and CEO

Peter Dyson has more than 25 years of experience in leading the development and application of advanced computing technologies to solve the complex problems of commercial and government clients.

Under his leadership, the company has grown substantially over the past eight years as well as been recognized with both community and customer awards. Mr. Dyson has served as president and CEO of Modus Operandi since its founding as a result of a merger. During his tenure, he has actively participated in all aspects of the business, spanning strategy planning and execution, sales and marketing, executive recruitment and development, customer development and relationship management, program leadership, and financial management.

Richard McNeight, President

As president, Mr. McNeight draws upon his entrepreneurial and leadership experience of high-tech companies to lead Modus Operandi to the forefront of the government and military software technology market. He represents and promotes the company to current and potential customers, partners and stakeholders.

Mr. McNeight has been serving as a Modus Operandi board member since 2007. Most recently, he has been president of Federal Systems LLC. As the founder of Paravant Computer Systems in 1982, he led the company through its successful initial public offering in 1996 and its acquisition by DRS Technologies in 2002. He subsequently served as president of DRS Tactical Systems, significantly growing the business and establishing DRS as the worldwide leader in design and production of rugged portable computers for military systems. Mr. McNeight holds a bachelor's degree in applied science and engineering from the University of Wisconsin – Milwaukee and a master's degree in computer information and control engineering from the University of Michigan. He has served on numerous business, education and charitable boards, including the Florida Institute of Technology Board of Trustees, Technological Research and Development Authority (TRDA) Board, the Astronaut Memorial Foundation (AMF) Board, the Brevard County Workforce Development Board and the Space Coast Early Intervention Center Board.

Jeffrey Lessner, VP, Business Development

Jeff Lessner is responsible for all aspects of the company's business development efforts, including full oversight of sales and marketing efforts, market analysis, and management of business proposal production. He is also instrumental in working with government customers to transition advanced technology into programs of record.

Mr. Lessner has over 25 years of experience representing various-sized companies to customers within the Department of Defense, Homeland Security and Intelligence Community. In these roles, Mr. Lessner was focused on the development, deployment and life cycle management of compute-intensive C4ISR, C2 systems, GEOINT, persistent surveillance, SOA, cloud computing, critical infrastructure protection and storage intensive products and programs. Mr. Lessner joined Modus Operandi from General Dynamics Advance Information Systems, where he was a senior manager for business development for the Cyber Systems Division, Enterprise Mission Services. Prior to that position, Mr. Lessner was director of business development for ProLogic, Inc., (now Ultra ProLogic) where he was instrumental in its dramatic growth. Mr. Lessner holds a bachelor's degree in electrical engineering from the University of Maryland.

Dr. Eric Little, VP and Chief Scientist

Dr. Eric Little oversees Modus Operandi's research and development efforts and the continued development and enhancement of the company's semantic software.

Dr. Little has a broad range of experience in the fields of ontology, knowledge management, cognitive science, and information fusion, from both the academic and business sectors. Most recently, Dr. Little was director of information management at Orbis Technologies, managing offices in both Orlando and Jacksonville, FL. Prior to that position, Dr. Little was chief knowledge engineer and executive consultant at the Computer Task Group in Buffalo, NY.

Dr. Little holds a Ph.D. in philosophy and cognitive science from The University at Buffalo, State University of New York (SUNY). Before embarking upon his commercial business career, Dr. Little spent several years working in academia, as assistant professor of doctoral studies and director of the Center for Ontology & Interdisciplinary Studies at D'Youville College in Buffalo, NY. Prior to that position, he was a post-doctoral research fellow in ontology development at the Center for Multisource Information Fusion, Department of Industrial Engineering, at SUNY Buffalo working on semantic technologies for use in information fusion applications.

Dr. Little has published in the areas of philosophy, cognitive science, ontology, information fusion, and human factors engineering. Dr. Little is currently active in several collaborative research groups including: co-chair of the Central Florida Semantic Web Meet-Up Group, the National Center for Ontology Research, the National Center for Multisource Information Fusion, the federal government's e-Governance group, and numerous other on-line semantic communities.

Charles Keuthan, VP, Finance and Administration

Charles "Chuck" Keuthan is Modus Operandi's vice president of finance and administration. He returned to Modus Operandi in 2004 after spending three years at a $30M distribution company of nutritional supplement products as the controller.

He performed the same role with Modus Operandi the prior thirteen years and has spent fifteen years in academics, instructing at both the graduate and undergraduate levels. He is a two-time graduate from the Florida Institute of Technology, attaining a B.S. in management science and an M.B.A. As vice president of finance and administration, Mr. Keuthan must balance the needs of a high-growth company and ensure continued efficiency of operations without sacrificing the company's reputation of outstanding technical ability and quality customer service.

Board of Directors

Peter Dyson, Chairman and CEO

Peter Dyson has more than 25 years of experience in leading the development and application of advanced computing technologies to solve the complex problems of commercial and government clients.

Under his leadership, the company has grown substantially over the past eight years as well as been recognized with both community and customer awards. Mr. Dyson has served as president and CEO of Modus Operandi since its founding as a result of a merger. During his tenure, he has actively participated in all aspects of the business, spanning strategy planning and execution, sales and marketing, executive recruitment and development, customer development and relationship management, program leadership, and financial management.

Dr. Anthony Catanese, Member

Dr. Anthony Catanese is the president of Florida Institute of Technology and former president of Florida Atlantic University.

A graduate of Rutgers University, Dr. Catanese earned a degree in city and regional planning. He holds a master of urban planning degree from New York University, a Ph.D. from the University of Wisconsin in urban and regional planning, and was a Senior Fulbright Professor in Bogota, Colombia. Dr. Catanese is the founding president of the Florida State University Presidents Association and has served as president or chairman of the Florida Association of Colleges and Universities; Florida Campus Compact; and Atlantic Sun Athletics Conference. He has served on several boards, including National Collegiate Athletics Association, Orange Bowl Committee, AvMed (South Florida) and Wachovia Bank (Florida). He is the recipient of numerous awards, including the Chief Executive Leadership Award of the Council for Advancement and Support of Education (2001), and is author of 13 books and over 65 journal articles.

Richard McNeight, President and Member

As president, Mr. McNeight draws upon his entrepreneurial and leadership experience of high-tech companies to lead Modus Operandi to the forefront of the government and military software technology market. He represents and promotes the company to current and potential customers, partners and stakeholders.

Mr. McNeight has been serving as a Modus Operandi board member since 2007. Most recently, he has been president of Federal Systems LLC. As the founder of Paravant Computer Systems in 1982, he led the company through its successful initial public offering in 1996 and its acquisition by DRS Technologies in 2002. He subsequently served as president of DRS Tactical Systems, significantly growing the business and establishing DRS as the worldwide leader in design and production of rugged portable computers for military systems. Mr. McNeight holds a bachelor's degree in applied science and engineering from the University of Wisconsin – Milwaukee and a master's degree in computer information and control engineering from the University of Michigan. He has served on numerous business, education and charitable boards, including the Florida Institute of Technology board of trustees, Technological Research and Development Authority (TRDA) Board, the Astronaut Memorial Foundation (AMF) Board, the Brevard County Workforce Development Board and the Space Coast Early Intervention Center Board.

James W. Thomas, Member

James W. Thomas is EVP of Rivian Motors.

He is also the founder of Coastal Dominion Capital, LLC, an early stage investment firm. Previously, he was the chief operating officer and chief financial officer of MapQuest.com, where he helped lead the transition from a traditional service company to the market-leading Internet mapping company. He managed MapQuest's initial public offering and subsequent sale to AOL. Prior to MapQuest, he was division president for Sierra Online's largest division in charge of the company's major branded products. He has a B.S. in mathematics from Florida Institute of Technology and an M.B.A. from the University of Virginia. He is a member of the board of trustees of Florida Institute of Technology.

Modus Operandi partners with prime contractors to help them to bring increased value to their customers. We participate on major IDIQ contracts to streamline contracting processes for government agencies.

Active IDIQ Contracts

Our IDIQ contracts - customers - primes - contract numbers:

  • Agile Cyber Technology - Air Force - AIS - FA875012D0002
  • Battle Command of Products - U.S. Army - Lockheed Martin - W15P7T-09-D-N007
  • Battlespace Awareness - Navy SPAWAR - Vickers Nolan Enterprises - N65236-13-D-5839
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  • SeaPort-e - Navy/USMC - eSTS, ISS - N00178-07-D-5051
  • SETAC10 - U.S. Army SMDC - Radiance - W9113M-13-D-0006
  • S3 - U.S. Army CECOM - Booz Allen Hamilton - W15P7T-06-D-E401; Lockheed Martin - W15P7T-06-D-E405; and MSTI - W15P7T-06-D-E403
  • SSES NexGen - U.S. Army CECOM - Various - W15P7T-12-D-E0XX

Partners

Modus Operandi is a small business bringing real technology solutions to mission critical software systems development and information integration needs.

Mutually beneficial technology alliances and partnerships

ENABLE MODUS OPERANDI TO SERVE OUR CUSTOMERS MORE EFFECTIVELY BY BRINGING LEADING EDGE SOLUTIONS TO MISSION CRITICAL SOFTWARE SYSTEMS DEVELOPMENT AND INFORMATION INTEGRATION NEEDS.

Our Partners

  • ACIN
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Past Performance

Source: http://www.modusoperandi.com/PastPerformance.html

Technology Services

Case Studies

FOCUSED ON DEVELOPING NEW APPLICATIONS AND ENHANCING EXISTING ONES

U.S. Army DCGS SIGINT

U.S. Army Guardrail Common Sensor

U.S. Marine Corps DCGS-MC Semantic Wiki

Our Government Customers

  • U.S. Air Force - AFRL, ESC, 45th Space Wing, ISR Agency
  • U.S. Army - CECOM, RDECOM
  • U.S. Marine Corps
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  • Intelligence Agencies

Publications

 
Modus Operandi participates in many trade shows and conferences throughout the year.
 
Technical Publications, Articles and Presentations

Presentations

What Makes A Wiki Semantic?

By Mark Wallace
SemTech Biz Conference 
June 2013
San Francisco, CA

Tactical Semantics: Extracting Situational Knowledge from Voice Transcripts using Ontology-Driven Text Analysis

By Dr. Kent Bimson and Dr. Teresa Nieten
SemTech Biz Conference 
June 2012
San Francisco, CA

Introduction to RDF, RDFS, & OWL

By Mark Wallace
SemTech Biz Conference 
June 2012
San Francisco, CA

Semantic Fusion of Multi-INT Data

By Dr. Richard Hull and Dr. Mark Gerken
IDGA ISR Summit

Nov. 2011
Wash., DC

Rapid Prototyping with Jena Command Line Utilities

By Mark Wallace:
SemTech Biz Conference
June 2011
San Francisco, CA

Careers

Technology Services
Accelerating customer mission success by modernizing software systems and liberating information.

WITH A 29-YEAR TRACK RECORD OF SOFTWARE TECHNOLOGY INNOVATION, PRODUCT DEVELOPMENT, AND TECHNICAL SERVICES, MODUS OPERANDI IS A SMALL BUSINESS USING AGILE METHODS AND TEAMWORK TO CREATE VALUE FOR OUR CUSTOMERS.

Current Openings

  • To apply for an open position at Modus Operandi, send a cover letter and resume to the e-mail address indicated in the position description.
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  • We are usually interested in talking to people with the following experience:
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  • Semantic Technologies Research

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We offer a very comprehensive benefits package which includes:

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OUR CORPORATE OFFICE IS LOCATED ON THE SPACE COAST OF FLORIDA AND OUR FIELD OFFICE IS IN MARYLAND.

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Semantic Wiki

Fact Sheet

PDF

 
Case Study: DCGS-MC Semantic Wiki
SemanticWikiPhoto.png SemanticWikiLogo.png

Background

The Distributed Common Ground System – Marine Corps (DCGS-MC) integrates intelligence, surveillance, and reconnaissance (ISR) processing and exploitation capabilities into a single, net-centric environment. DCGS-MC supports intelligence analysts across the Marine Force by making ISR data more visible, accessible, and understandable. The DCGS Integration Backbone (DIB) serves as the basis for interoperability between the various services’ DCGS programs.

Problems

  • Analysts are overwhelmed by data
  • Multitudes of unstructured documents that are not readily searchable
  • Hard to expose DCGS-MC data to other DIBs
  • Duplication of analysis and reports instead of collaboration

Solution

Modus Operandi (MO) developed a system that allows Marine Corps intelligence analysts to rapidly search large amounts of unstructured data, find critical patterns, and other essential elements of information and share their work with other analysts. The MO developed system is based on a semantic wiki that makes data more visible, accessible, and understandable. The wiki supports a collaborative environment and avoids much duplication of effort yet aiding individual efforts. Adding semantic capabilities to the basic wiki provides finer grained, more accurate searches, as well as providing the capability to automatically generate pages and some page content. MO coupled the semantic wiki capabilities with text analytics to address the problem of integrating relevant information from unstructured documents. MO’s text analytics: 1) identifies events of interest to analysts such as IED events, observation events (sighting of a high value individual or HVI), and travel events (movement of an HVI from one location to another), 2) transforms them into Resource Description Framework (RDF) “triples”, and 3) persists the RDF in a triple store that supports advanced queries. Analysts can automatically create pages for these events by importing them into the semantic wiki from the triple store. Collectively these analytic tools combined in the semantic wiki expose DCGS-MC data to DIB searches without the need to manually create metadata cards (a means to identify data) and insert them into a DIB metadata catalog (MDC).

Results

  • Reduction of analysts’ workload through automatic extraction of key events
  • Semantic searches provide more accurate and finer-grained results
  • Closer collaboration between analysts
  • Improved situational awareness
  • Less training required for users familiar with wikis
  • Reduction of duplicate analyses and intelligence products 

SemanticWikiFooter.png

 

Slides

PDF

What Makes a Wiki Semantic?

MarkWallace062013Slide1.png

What's a Wiki?

MarkWallace062013Slide2.png

Example: Wikipedia

MarkWallace062013Slide3.png

Wiki Issue

MarkWallace062013Slide4.png

What's a Semantic Wiki?

MarkWallace062013Slide5.png

How are Features Achieved?

MarkWallace062013Slide6.png

Demonstration

MarkWallace062013Slide7.png

Take-aways

MarkWallace062013Slide8.png

Semantic Enrichment and Fusion Of Multi-Intelligence Data

By Dr. Richard D. Hull, Don Jenkins, Alan McCutchen Modus Operandi, Inc. 709 S. Harbor City Blvd., Suite 400 Melbourne, FL 32901 http://www.modusoperandi.com
Source:  (PDF)

Copyright Copyright Information Wave-EF White Paper Copyright © 2006-2009 Modus Operandi, Inc. Disclaimer The sample data included in this guide is fictitious. Trademark or Service Marks Wave is a registered trademark of Modus Operandi, Incl. All trademarks are the property of their respective companies.

SemanticEnrichmentWave.png

1 Abstract

The challenges of predictive battlespace awareness and transformation of TCPED to TPPU processes in a net-centric environment are numerous and complex. One of these challenges is how to post the information with the right metadata so that it can be effectively discovered and used in an ad hoc manner. We have been working on the development of a semantic enrichment capability that provides concept and relationship extraction and automatic metadata tagging of multi-INT sensor data. Specifically, this process maps COMINT, IMINT and HUMINT data to concepts and relationships specified within a semantic model (ontology). We are using semantic enrichment for development of data fusion services to support multiple Department of Defense programs. This paper presents an example of using the semantic enrichment architecture for concept and relationship extraction from USMTF HUMINT reports and COMINT data. The process of semantic enrichment adds semantic metadata tags to the original data enabling advanced correlation and fusion. A geospatial user interface leverages the semantically-enriched data to provide powerful search, correlation, and fusion capabilities.

2 Introduction

Net-centricity transforms the traditional intelligence analysis process of Task, Collect, Process, Exploit and Disseminate (TCPED) to a Task, Post, Process and Use (TPPU) process. The fundamental difference between the two processes is that TPPU posts information before it is processed, making it available for other ad hoc purposes. To realize the value of this strategy, ad hoc users must be able to find relevant information from across the Department of Defense (DoD) Enterprise. All Net-centric systems have some mechanism for finding relevant information, typically content and metadata discovery services that require accurate and complete metadata tags to describe the content of information items they maintain.

We are developing a framework for semantically enriching sensor data and intelligence reports that can improve a net-centric system‟s discovery services by generating semantic tags for the information automatically, thereby helping users and applications find meaningful, situation-specific information more easily. The semantic tags, defined within one or more ontologies, represent the important concepts and relationships users need to fulfill their missions. This framework, called the Wave Exploitation Framework or Wave-EF, is built upon a publish and subscribe messaging service, an open source unstructured information management system, and components for transforming the raw information into XML and generating semantic tags.

Wave-EF is designed to function within a service-oriented architecture (SOA), with rapid integration into Enterprise Service Bus (ESB), Metadata Registries and other components of a SOA ecosystem. It is both a consumer and producer of enterprise and web services. The use of a publish/subscribe mechanism means that the Wave-EF services do not need be collocated, an important element of the net-centric and TPPU philosophies.

Wave-EF was used recently to develop a pipeline for semantically enriching United States Message Text Format (USMTF)[1] reports with metadata tags relating to vehicle thefts. The use case we were addressing was: “Intelligence analysis has identified a number of potential indicators of a future detonation of a vehicle borne improvised explosive device (VBIED), one of which is the theft of an appropriate vehicle for use in the VBIED attack. How can we identify vehicle theft events described within intelligence reports with high precision?” This manuscript describes our methodology and results towards recognizing descriptions of vehicle theft events within USMTF reports and creating the appropriate semantic tags, i.e., tags representing the concepts and relationships of each „true‟ event. This capability, once fully generalized, will provide automated tagging of military information, thereby accomplishing two critical objectives simultaneously: (1) accelerating the pace with which information can be made accessible by net-centric discovery services; and (2) enhancing the meaning of these messages for more accurate filtering, fusion and situation-specific analysis.

Discovery services

Discovery services in a net-centric environment provide visibility, accessibility and understandability of data and service assets across the DoD Enterprise. According to the DDMS Specification [2], “The Department of Defense Discovery Metadata Specification (DDMS) defines discovery metadata elements for resources posted to community and organizational shared spaces. „Discovery‟ is the ability to locate data assets through a consistent and flexible search. The DDMS specifies a set of information fields that are to be used to describe any data or service asset that is made known to the Enterprise, and it serves as a reference for developers, architects, and engineers by laying a foundation for Discovery Services. The DDMS will be employed consistently across the Department‟s disciplines, domains and data formats.”

The DoD Net-Centric Data Strategy (NCDS) and DoD Directive Number 8320.2 require data sharing across the DoD, including the creation of new information resources to describe the available assets: [POLICY] 4.2. Data assets shall be made visible by creating and associating metadata (“tagging”), including discovery metadata, for each asset. Discovery metadata shall conform to the Department of Defense Discovery Metadata Specification (DDMS)[3].

The recommended best practice for describing an asset is to associate it with one or more DDMS Subject Content Category metadata entries from a controlled vocabulary. Communities of Interest (COIs) are responsible for defining the controlled vocabularies, taxonomies or classification schemes for their respective domains and ultimately for overseeing the use of those schemes during metadata tagging. Unfortunately, the manual tagging of data and service assets is tedious, error prone and resource-intensive. Our work is aimed at automating these tasks.

Semantics

Decades of research in knowledge representation and machine reasoning has led to recent efforts in the creation of a number of robust technologies for implementing the „Semantic Web‟.[4] In particular, the DARPA-sponsored efforts including the DARPA Agent Markup Language (DAML)[5] and the Ontology Inference Layer (OIL)[6], collectively known as DAML+OIL, were used along with the Resource Description Framework (RDF)[7] to create the World Wide Web Consortium‟s (W3C) Web Ontology Language or OWL[8]. OWL provides a standards-based language for defining ontologies of concepts and relationships, definition of properties of concepts and relationships, set theoretics (e.g., union, intersection) and equivalence constructs between concepts and individuals (concept or class instances).

OWL as a representation language for COI taxonomies and controlled vocabularies has been used in the DoD‟s Metadata Registry [9]. Therefore automatic production of OWL metadata tags would support the COI efforts across the DoD. One other large effort underway to produce OWL metadata tags is the Automated Metadata Population Service (AMPS).[10] Our work differs from AMPS in that we are focusing on the generation of OWL-based subject content tags instead of keywords.

Semantic enrichment

Semantic enrichment is the process of adding or associating semantic tags – usually concepts, relationships, events and properties described in an ontology – to augment unstructured data items. The semantic tags are persisted and used for the search and retrieval of the original data items as well as for machine reasoning over the tags themselves. We are currently developing semantic enrichment capabilities to support multi-intelligence (multi-INT) data fusion for DoD C4ISR applications by tagging raw and exploited HUMINT, IMINT and COMINT with Joint Command Control and Consultation Information Exchange Data Model (JC3IEDM)[11] and Universal Core (UCore)[12] concepts and relationships.

Figure 1. Example of HUMINT/COMINT data streams (USMTF messages) are first transformed into XML (Step 1)
Vehicle theft events are recognized and XML tags representing them are added (Step 2). These elements are then transformed into OWL ontology constructs (Step 3).
SemanticEnrichmentFigure1.png
 
An example of the process of semantic enrichment (shown in Figure 1) begins with the transformation of raw source data into an intermediary eXtensible Markup Language (XML)[13] format. This step identifies the major structural features of the source data, including header, body and footer features. We have developed tools that allow us to quickly develop a data extraction language (DEL) for the incoming source. A source can be textual or binary and structured, semi-structured or unstructured.
 
During the second step of the transformation process, each structural feature is scanned so that domain features can be identified and annotated. If the source information is a USMTF message, a handler specific to each structural field of the message is invoked. The „location‟ field of a USMTF message can be converted from Military Grid Reference System (MGRS)[14] coordinates to geodetic latitude and longitude coordinates during transformation into XML. The „remarks‟ field is forwarded to an entity extraction system to identify entities (concepts) of interest. This flexible approach is also quite powerful and has allowed us to leverage both proprietary and third-party (open source, COTS, GOTS) components for efficient transformation of source data.
 
The second step produces XML annotations for salient features identified in the source data; it produces no annotations if the message does not describe a theft event or a related concept or relationship. The third step is to map the extracted features to the concepts and relationships defined in the target ontology or ontologies. For example, the application we will describe in Section 0 involves the identification of vehicle theft events in USMTF messages. The XML representation for a vehicle theft event is a <VEHICLE-THEFT> element. If the target ontology is JC3IEDM-based, then there are two potential concepts we could map to: a subclass of ACTION-EVENT called „Looting‟ or a subclass of ACTION-EVENT called „Robbery‟ 1. We have chosen to map to the „Robbery‟ subclass when it‟s clear that the theft was directly from a person rather than an organization, group or location, e.g., because the verb „carjack‟ or „hijack‟ was used or if the car was stolen directly „from‟ a person. All other instances map to the „Looting‟ concept.
 
1 JC3IEDM is not an ontology but a data model. There have been efforts to construct an OWL ontology from JC3IEDM using XSLT [20]. However, the Looting and Robbery ACTION-EVENTs in this JC3IEDM ontology do not capture the semantic cases necessary for representing these events. We have added cases including perpetrator, theme, location and time to our own OWL representation of JC3IEDM.
 
We have studied the problem of automating the mapping of schemas and ontologies and while it is difficult, there are alternative approaches that can provide useful solutions. Rahm and Bernstein surveyed the state of the art in automatic schema matching and found that there are a number of different approach types, some more suited than others for semantic query processing [15]. While this survey is directed more towards relational databases, many of the ideas have merit in the realm of ontology concept matching, in particular, the linguistic approaches. Another recent work is C-OWL, which uses a set of bridge rules to create a mapping between two ontologies [BOU2003]. The bridge rules "define semantic relations between concepts in different ontologies." This work builds upon research published by Borgida and Serafini regarding distributed description logics, which extends the reasoning available on ordinary schemas to the case of multiple schemas connected by arbitrary binary correspondences between individuals (i.e., bridge rules) [BOR2002].
 
For the purposes of this discussion, we will assume that a mapping between the XML elements and the target ontology has been created.

Wave-EF

The Wave Exploitation Framework was used as the semantic enrichment platform for the experiment described here. As shown in Figure 2, Wave-EF uses a publish and subscribe messaging system (currently the Java Message Service[18], but it can be easily swapped with other publish/subscribe systems such as the Data Distribution Service[19]) to route incoming multi-source intelligence data through a semantic enrichment pipeline to a number of applications and services.

Figure 2. Wave-EF semantically enriches multi-source intelligence data

through direct feeds or from net-centric services for fusion, mediation, metadata tagging, alerting and dissemination within applications and services

SemanticEnrichmentFigure2.png

[20]?

Processing is typically from left to right through the pipeline, but there is no requirement that all steps be executed and other orderings are possible. Formatting and parsing of the incoming data, i.e., translation into XML, is handled by two components, Wave Formatter and Wave Parser. Wave Formatter allows a knowledge engineer to define a DEL mapping to XML and later Wave Parser uses that specification to translate items as they are published. The concept and relationship extraction steps are performed within the Apache Unstructured Information Management Architecture (UIMA)[21], a Java-based, open source architecture for processing unstructured information. We are using UIMA to organize a number of concept, relation and event extractors, or as they are called in UIMA, annotators. These annotators can detect and extract concepts (or entities) including simple concept instances such as specific locations, dates, times, email addresses, as well as more complex items such as JC3IEDM concepts or vehicle theft events. We have built our own powerful annotators using a combination of regular expression patterns and semantic variables. These extensions to UIMA are described in more detail in Section 0.

The results of the extraction process are then translated into OWL instances or RDF triples and persisted for further correlation and fusion, and published to the Enterprise for use by interested (subscribing) applications and services.

Identifying vehicle theft events

We have used Wave-EF to address a representative problem faced by analysts looking for very specific events and concepts within a large corpus of potentially similar yet unrelated data. The problem involves detection of indicator or precursor events to VBIED attacks within HUMINT reports. The goal of this effort was to accurately identify and extract descriptions of the thefts of vehicles in and around an Area Of Responsibility (AOR) that may precede a car or truck bomb attack on US military and civilian targets. Currently, this is being done manually by analysts reading all incoming reports of criminal activity or by using traditional search engine technology to search for reports that mention keywords like “steal”, “stole”, “stolen”, “car”, “truck”, “vehicle”, etc.

Even using search tools, the analysts had to review many irrelevant stories because their tools could not differentiate among semantically relevant and irrelevant stories. Moreover, important reports were missed because of incomplete keywords. Therefore, we identified this gap as an opportunity to use Wave-EF to semantically tag incoming reports with JC3IEDM representations of vehicle thefts in the reports that describe them. These automatically-generated tags are persisted in the Wave-EF knowledge base, but they could just as easily be used to populate metadata entries in a net-centric metadata catalog. While this paper describes the methodology and results for identifying vehicle theft events, our approach can be generalized to identify other JC3IEDM event types. In fact, we are currently defining extraction patterns for other event types and plan to have patterns for all 347 JC3IEDM enemy event types in the future.

3 Methodology

The semantic enrichment process used in this project involved the following steps: 1) identification and parsing of the various fields of the USMTF messages; 2) extraction of concepts and relationships found in those fields and the generation of the appropriate XML tags for them; and 3) translation of the XML tags into JC3IEDM OWL representations. Steps 1 and 3 are straightforward as they both involve manipulation of structured formats. Step 2 is more difficult because it requires analysis of unstructured text to extract concepts, relationships and, ultimately, events. Therefore we will focus the following discussion on the methodology for implementing Step 2.

Extraction patterns

Extraction of concepts, relationships and events requires a systematic description of the information to be extracted, i.e., a set of extraction patterns. To build these patterns, a knowledge engineer created a training set of 32 USMTF messages. These messages contained fictitious, unclassified HUMINT reports of vehicle thefts and similar criminal events. Manual analysis of the training set messages and similar news reports by the knowledge engineer resulted in a number of logical patterns describing vehicle theft events (similar to a phrase structure grammar). Examples of patterns for active and passive voice sentences using the past tense of the verb „steal‟ include:

Active voice: <person | organization> [adverb] stole <vehicle> [from <location>] [adverb] (1)

Passive voice: <vehicle> was stolen [from <location>] [by <person | organization> ] [adverb] (2)

Items in angle brackets (<>) are classes or kinds in our ontology: they represent any word or phrase denoting an instance of the class, e.g., a person (an individual‟s name or nouns such as „man‟ and „thief‟), an organization, a location, etc. Items in square brackets ([ ]) are optional. Parts of speech, such as „adverb‟, are used to match instances of that part of speech, e.g., „allegedly‟, „reportedly‟ or „yesterday‟. A vertical (|) represents a logical disjunction, e.g., < person | organization > matches instances of a person class or an organization class. Active voice pattern (1) matches sentences that describe a person or organization stealing a vehicle with an optional adverb and an optional prepositional phrase indicating from where the vehicle was stolen.

The two patterns above identify sentences describing vehicle theft events: “John Smith stole a truck,” “A terrorist group allegedly stole the fuel truck from the Oil Ministry” and “A 2003 Chevrolet Suburban was stolen yesterday.” Patterns for matching vehicle theft events using other tenses of the verb steal and other verbs were also constructed. All of these patterns form two über 2 patterns as the verbs of „stealing‟ tend to the same verbal subcategorizations 3. Therefore we define these two über patterns to be:

Active voice: <person | organization> [adverb] <steal> <vehicle> [from <location>] [adverb] (3)

Passive voice: <vehicle> was <steal> [from <location>] [by <person | organization>] [adverb] (4)

2 The term über here is used to express a super pattern which subsumes all others patterns for a particular voice.

3 Verbal subcategorizations describe the number and type of syntactic arguments that a verb co-occurs with. Two major subcategories of verbs are the transitive (verbs which take a direct object as in ‘Peter ate an apple’) and the intransitive (verbs which do not take a direct object as in ‘Peter sleeps’) verbs.

The new item <steal> represents the different verbs depicting the theft of an object, e.g., take, hijack, carjack, make off with, pilfer, etc. Other prepositional phrases not shown here but also part of the über patterns include prepositions for spatial relationships („near‟, „by‟, „within‟) and prepositions for temporal relationships („during‟, „on‟, „before‟, „after‟). The use of externally manageable dictionaries further enhances the power of the über patterns. For example, references to specific makes and models of vehicles (e.g. “Chevrolet Suburban”) can be matched using a <vehicle> dictionary. As new makes and models emerge, only the external dictionary is changed, while the über patterns remain the same.

From each of these über patterns, a regular expression was created to recognize instances of the pattern within intelligence reports. According to Wikipedia, “In computing, regular expressions provide a concise and flexible means for identifying strings of text of interest, such as particular characters, words, or patterns of characters.”[22]. The regular expressions are used to match the appropriate substrings (portions of the HUMINT sentences) and to extract the semantic cases (i.e., thematic roles) of the vehicle theft event. For example, considering the sentence “John Smith stole a truck”, the semantic case representing the actor or perpetrator of the theft event is “John Smith” and the semantic case representing the theme or object stolen is “the truck”. Identification of these semantic cases is crucial for subsequent analysis of the data.

Using these über patterns, the header and body sections of the original 32 training messages and 168 new test messages, describing vehicle theft and other events, were analyzed to detect and capture vehicle theft events. We calculated recall and precision metrics for accurate detection and extraction of vehicle thefts and compared them against the results from using a search engine with a complex Boolean query. The query we used was:

(stole OR stolen) AND (vehicle OR car OR truck OR suv OR van OR bus)

This is actually a more complex query than most analysts would create. Training new analysts in the complexities of Boolean queries takes additional time and effort. Moreover, this illustrates a weakness of keyword queries: one must know and be prepared to type all of the possible verbs and vehicle types that could be used in a message. Our approach, however, can leverage all of the subtypes (e.g., „car‟ and „truck‟) and instances of <vehicle> stored within the ontology or separate concept dictionaries.

The semantic enrichment results were displayed within a geospatial visualization tool. The tool also provides filtering by geographic region and date/time, used to further reduce the output. For example, the analyst can request only those reports that indicate a vehicle theft in a specific region of the AOR during the last fifteen days. Locations and times of the thefts are extracted from the USMTF header information.

4 Data

Our experiment involved the use of 200 USMTF messages describing real events from in and around an AOR. An example of a USMTF message portraying a vehicle theft event is shown in Figure 3.

Figure 3. An example of a USMTF message describing a vehicle theft

CC CC

PRIORITY

Q 231452Z SEP 08

FM CDR1STMIBN

TO WHOMEVER

U N C L A S S I F I E D

QQQQ

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THE POLICE DEPARTMENT IS LOOKING FOR A MAN SUSPECTED OF A CARJACK LATE THURSDAY NIGHT. AT ABOUT 11:45 P.M., A MAN FORCIBLY STOLE A 2003 CHEVROLET SUBURBAN WHEN HE CAME TO A STOP SIGN AT THE INTERSECTION. AS THE VICTIM STOPPED, A MAN APPROACHED HIM, PULLED HIM FROM HIS VEHICLE AND STOLE THE VEHICLE. THE VICTIM SUFFERED MINOR INJURIES BUT REFUSED TREATMENT.//

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We purposefully added messages containing „red herring‟ events to the test set. Red herring events are those that mention keywords associated with vehicle theft events but do not describe an actual event. For example, a message containing the sentence, “A car wash was found using water stolen from broken city pipes,” contains the keywords „car‟ and „stolen‟ but does not describe the theft of a car. Red herring messages are erroneously retrieved, however, by traditional search engines and can cause intelligence analysts to waste their time reviewing them. Our approach avoids this pitfall because the über patterns define contexts for the keywords that must be present to convey a true vehicle theft relationship.

5 Results

Of the USMTF 200 messages, 35 describe vehicle theft events. Precision, recall and F-measure statistics are shown in Table 1. Precision is the ratio of the number of relevant messages selected to the total number of messages selected. Recall is the ratio of number of relevant messages select to the total number of relevant messages. F-measure is the weighted harmonic mean of precision and recall and is used to combine both precision and recall into a single score. In all cases, larger numbers are better.

Table 1. Comparison of Wave-EF, search engine and random precision, recall and F-measure statistics

  Msgs Selected Relevant Msgs Selected Precision Recall F-measure
Wave-EF 23 23 1.00 0.657 0.793
Keyword Query 67 29 0.433 0.829 0.569
Random 97 17 0.175 0.489 0.223
Select All Msgs 200 35 0.175 1.00 0.241
 
Wave-EF was the most precise method by a wide margin. The F-measure score for Wave-EF was significantly better than the other approaches. However, Wave-EF’s recall number was below that of the keyword query and the selection of all messages. The decline in recall was due to 12 messages containing vehicle theft events which were not identified by Wave-EF. The sentences or clauses that should have led to a correct identification, but were missed by Wave-EF, are shown in Table 2 with the reason for the missed identification. Most of these missed events can be fixed with updates to the concept dictionaries or minor changes to the über patterns.

Table 2.

ID Description Reason
vt8 ... when a vehicle and trailer were stolen ... conjoined subject
vt21 The car was originally reported stolen from a nearby neighborhood. complex adverbial
vt25 The car used in the bombing was stolen from a church there. embedded relative clause
vt42 Last week, a gang stole a truck with four cows in it. missed subject
vt47 ... 32 suspects were captured after stealing two cars ... long distance dependency
vt56 ... noting that they stole the vehicle and detained him. anaphora
vt58 The insurgent fighters had commandeered the vehicle from two soldiers missed verb
vt72 A state-owned pick-up, which had been stolen from a state department, . missed subject
vt79 The appeals court also fined each of them for stealing a car ... missed subject
vt82 Several police and army vehicles had been stolen conjoined subject
vt100 The car was hijacked from its owner a day earlier. missed verb
vt105 They are stealing everything: fire trucks, water tankers, ... missed object
 
These results are displayed within a geospatial visualization tool using latitude/longitude pairs extracted from the message. The simple demonstration tool shown in Figure 4 illustrates the results of Wave-EF filtering. The map is dramatically less cluttered than it would be if the 200 original or even the 67 keywords results were displayed. The analyst can select any of the small blue vehicle icons to see the original message and the extracted vehicle theft event.

Figure 4. These screenshots show the original 200 events (left)

and the results after filtering the semantically enriched events for vehicle thefts (right). The resulting events are projected as icons onto a map of the AOR for analysts to examine. Selecting an event icon causes the message details and the semantic metadata to be displayed.

SemanticEnrichmentFigure4.png

These results are very promising and show that Wave-EF can identify vehicle theft events with high precision. In addition, key attributes that greatly enhance discovery of relevant data are also extracted. These attributes include the time, location, perpetrator and stolen object attributes of these events. The extracted attributes can be used as semantic metadata tags associated with the messages and stored in a net-centric metadata catalog or to populate a RDF triple store for reasoning. For example, the JC3IEDM metadata tags generated for USMTF message described in Figure 3 are shown in Figure 5.

Figure 5. Semantic metadata tags generated for a vehicle theft event

<owl:Class rdf:ID="Looting">

<rdfs:label rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Looting</rdfs:label>

<rdfs:comment>Act to take private property from an enemy or stolen by thieves.</rdfs:comment>

<rdfs:subClassOf rdf:resource="#ACTION-EVENT" />

</owl:Class>

<owl:Class rdf:ID="PERSON">

<rdfs:comment>An OBJECT-ITEM that is a human being to whom military or civilian significance is attached.</rdfs:comment>

<rdfs:subClassOf rdf:resource="#OBJECT-ITEM" />

</owl:Class>

<owl:Class rdf:ID="VEHICLE-TYPE"> <rdfs:comment>An EQUIPMENT-TYPE that is designed to operate on land routes (other than rail) with a primary role of transporting personnel, equipment or supplies.</rdfs:comment>

<rdfs:subClassOf rdf:resource="#EQUIPMENT-TYPE" />

</owl:Class>

<PERSON rfd:ID=“Man101”>

</PERSON> <VEHICLE rfd:ID=“Vehicle202”></VEHICLE>

<Looting rdf:ID="Event1">

<actor rdf:resource=“#Man101”>

<objectStolen rdf:resource=“#Vehicle202”>

<REPORTING-DATA rdf:resource=“#ReportingData19763”>

</Looting>

The event extracted is an instance of a Looting event with the identifier „Event1‟. The actor (i.e., the agent performing the looting or the perpetrator) of the Looting event is „Man101‟. Man101 is an instance of PERSON class. The identifier „Man101‟ is automatically generated by concatenating the class name with an increasing integer. We use this scheme for readability, but any unique identifier could be used (e.g., GUID). The object stolen of the Looting event is „Vehicle202‟. Vehicle202 is an instance of the VEHICLE class. Time and location information is stored in the REPORTING-DATA class instance with the identifier „ReportingData19763‟. The REPORTING-DATA class contains information about the date, time, reliability, source, etc. Event locations are not shown here, but are captured as well.

6 Conclusions

We have shown how HUMINT reports can be semantically enriched with metadata tags from a military ontology (JC3IEDM) for identifying vehicle theft events. These events represent potential precursors to VBIED attacks on US and Allied forces in and around an AOR, and intelligence analysts need a way to retrieve them from the much larger sets of reports found in DoD repositories (e.g., the DCGS-A Brain). While our experiment‟s combined training and test set was only 200 messages, the actual number of local police and US military messages is much larger. We are currently developing new methods for extracting events that can handle very large numbers of input messages, on the order of 1000‟s to 10,000‟s per day. With that volume of incoming information, the ability to identify and extract events with high precision is critical. Furthermore, because we intend to apply this approach to automatic metadata tagging of net-centric information, the tags we generate must be as correct as possible. They must also be transformed into a standard representation that allows them to be combined with additional information about the same events, through an iterative semantic enrichment process.

7 Acknowledgements

The authors would like to acknowledge the contributions of the Wave-EF development team who have helped both in the development of Wave-EF and in the accuracy of this manuscript. They are Dan Nieten, Rob Asfar, Mike Gill and Javad Maharramzade. Teresa Nieten developed the software for projecting vehicle theft events onto a geospatial display and in defining geospatial rules for event filtering.

8 References

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