Table of contents
  1. Story
  2. Slides
    1. 1. Information Visualization for Knowledge Discovery
    2. 2. Turning Messy BigData into Actionable SmallData
    3. 3. Interdisciplinary research community
    4. 4. Design Issues
    5. 5. HCI Pride: Serving 5B Users
    6. 6. Obama Unveils “Big Data” Initiative (3/2012)
    7. 7. Information Visualization & Visual Analytics
    8. 8. Information Visualization & Visual Analytics
    9. 9. Information Visualization & Visual Analytics
    10. 10. Business takes action
    11. 11. Spotfire: Retinol’s role in embryos & vision
    12. 12. Spotfire: DC natality data
    13. 13. http://registration.spotfire.com/eval/default_edu.asp
    14. 14. 10M - 100M pixels: Large displays
    15. 15. 100M-pixels & more
    16. 16. 1M-pixels & less Small mobile devices
    17. 17. Information Visualization: Mantra
    18. 18. Information Visualization: Data Types
    19. 19. Anscombe’s Quartet
    20. 20. Anscombe’s Quartet
    21. 21. Anscombe’s Quartet
    22. 22. Temporal Data: TimeSearcher 1.3
    23. 23. Temporal Data: TimeSearcher 2.0
    24. 24. LifeLines: Patient Histories
    25. 25. LifeLines2: Align-Rank-Filter & Summarize
    26. 26. LifeFlow: Aggregation Strategy
    27. 27. LifeFlow: Interface with User Controls
    28. 33. EventFlow: Original Dataset
    29. 34. LABA_ICSs Merged
    30. 35. SABAs Merged
    31. 36. Align by First LABA_ICS
    32. 37. Reduce Window Size
    33. 38. EventFlow Team: Oracle support
    34. 39. Treemap: Gene Ontology
    35. 40. Treemap: Smartmoney MarketMap
    36. 41. Market falls steeply Feb 27, 2007, with one exception
    37. 42. Market falls steeply Sept 22, 2011, some exceptions
    38. 43. Market mixed, February 8, 2008
    39. 44. Market rises, September 1, 2010, Gold contrarians
    40. 45. Market rises, March 21, 2011, Sprint declines
    41. 46. Treemap: Newsmap (Marcos Weskamp)
    42. 47. Treemap: WHC Emergency Room
    43. 48. Treemap: WHC Emergency Room
    44. 49. Treemap: Supply Chain
    45. 50. Treemap: Nutritional Analysis
    46. 51. Treemap: Spotfire Bond Portfolio Analysis
    47. 52. Treemap: NY Times – Car&Truck Sales
    48. 53. Treemap (Voronoi): NY Times - Inflation
    49. 55. VisualComplexity.com : Manuel Lima
    50. 56. SocialAction
    51. 57. Network from Database Tables
    52. 58. NodeXL
    53. 59. NodeXL
    54. 60. NodeXL: Import Dialogs
    55. 61. Tweets at #WIN09 Conference: 2 groups
    56. 62. Flickr networks
    57. 63. Twitter discussion of #GOP
    58. 64. Analogy: Clusters Are Occluded
    59. 65. Separate Clusters Are More Comprehensible
    60. 66. Twitter networks: #SOTU
    61. 67. Group-In-A-Box: Twitter Network for #CI2012
    62. 69. Pennsylvania Innovation Network
    63. 71. Innovation Patterns: 11,000 vertices, 26,000 edges
    64. 72. No Location Philadelphia Innovation Clusters: People, Locations, Companies
    65. 74. Interactive Methods to Reveal Patterns
    66. 75. Senate Co-Voting
    67. 76. Group-In-A-Box by Region
    68. 77. Interactive Methods to Reveal Patterns
    69. 78. Motif Simplification
    70. 79. Motif Simplification
    71. 80. Motif Simplification
    72. 81. Clique Motifs & Glyphs: 4, 5 & 6
    73. 82. Senate Co-Voting: 65% Agreement
    74. 83. Senate Co-Voting: 70% Agreement
    75. 84. Senate Co-Voting: 80% Agreement
    76. 85. Senate Co-Voting: 90% Agreement
    77. 86. Senate Co-Voting: 95% Agreement
    78. 87. Analyzing Social Media Networks with NodeXL
    79. 88. Social Media Research Foundation
    80. 89. Sense-Making Loop
    81. 90. Sense-Making Loop: Expanded
    82. 91. Discovery Process: Systematic Yet Flexible
    83. 92. Discovery Process: Systematic Yet Flexible
    84. 93. Discovery Process: Systematic Yet Flexible
    85. 94. UN Millennium Development Goals To be achieved by 2015
    86. 95. 30th Anniversary Symposium May 22-23, 2013
    87. 96. For More Information
    88. 97. For More Information
  3. Spotfire Dashboard
  4. Research Notes
  5. Big Data Visualization

Ben Shneiderman

Last modified
Table of contents
  1. Story
  2. Slides
    1. 1. Information Visualization for Knowledge Discovery
    2. 2. Turning Messy BigData into Actionable SmallData
    3. 3. Interdisciplinary research community
    4. 4. Design Issues
    5. 5. HCI Pride: Serving 5B Users
    6. 6. Obama Unveils “Big Data” Initiative (3/2012)
    7. 7. Information Visualization & Visual Analytics
    8. 8. Information Visualization & Visual Analytics
    9. 9. Information Visualization & Visual Analytics
    10. 10. Business takes action
    11. 11. Spotfire: Retinol’s role in embryos & vision
    12. 12. Spotfire: DC natality data
    13. 13. http://registration.spotfire.com/eval/default_edu.asp
    14. 14. 10M - 100M pixels: Large displays
    15. 15. 100M-pixels & more
    16. 16. 1M-pixels & less Small mobile devices
    17. 17. Information Visualization: Mantra
    18. 18. Information Visualization: Data Types
    19. 19. Anscombe’s Quartet
    20. 20. Anscombe’s Quartet
    21. 21. Anscombe’s Quartet
    22. 22. Temporal Data: TimeSearcher 1.3
    23. 23. Temporal Data: TimeSearcher 2.0
    24. 24. LifeLines: Patient Histories
    25. 25. LifeLines2: Align-Rank-Filter & Summarize
    26. 26. LifeFlow: Aggregation Strategy
    27. 27. LifeFlow: Interface with User Controls
    28. 33. EventFlow: Original Dataset
    29. 34. LABA_ICSs Merged
    30. 35. SABAs Merged
    31. 36. Align by First LABA_ICS
    32. 37. Reduce Window Size
    33. 38. EventFlow Team: Oracle support
    34. 39. Treemap: Gene Ontology
    35. 40. Treemap: Smartmoney MarketMap
    36. 41. Market falls steeply Feb 27, 2007, with one exception
    37. 42. Market falls steeply Sept 22, 2011, some exceptions
    38. 43. Market mixed, February 8, 2008
    39. 44. Market rises, September 1, 2010, Gold contrarians
    40. 45. Market rises, March 21, 2011, Sprint declines
    41. 46. Treemap: Newsmap (Marcos Weskamp)
    42. 47. Treemap: WHC Emergency Room
    43. 48. Treemap: WHC Emergency Room
    44. 49. Treemap: Supply Chain
    45. 50. Treemap: Nutritional Analysis
    46. 51. Treemap: Spotfire Bond Portfolio Analysis
    47. 52. Treemap: NY Times – Car&Truck Sales
    48. 53. Treemap (Voronoi): NY Times - Inflation
    49. 55. VisualComplexity.com : Manuel Lima
    50. 56. SocialAction
    51. 57. Network from Database Tables
    52. 58. NodeXL
    53. 59. NodeXL
    54. 60. NodeXL: Import Dialogs
    55. 61. Tweets at #WIN09 Conference: 2 groups
    56. 62. Flickr networks
    57. 63. Twitter discussion of #GOP
    58. 64. Analogy: Clusters Are Occluded
    59. 65. Separate Clusters Are More Comprehensible
    60. 66. Twitter networks: #SOTU
    61. 67. Group-In-A-Box: Twitter Network for #CI2012
    62. 69. Pennsylvania Innovation Network
    63. 71. Innovation Patterns: 11,000 vertices, 26,000 edges
    64. 72. No Location Philadelphia Innovation Clusters: People, Locations, Companies
    65. 74. Interactive Methods to Reveal Patterns
    66. 75. Senate Co-Voting
    67. 76. Group-In-A-Box by Region
    68. 77. Interactive Methods to Reveal Patterns
    69. 78. Motif Simplification
    70. 79. Motif Simplification
    71. 80. Motif Simplification
    72. 81. Clique Motifs & Glyphs: 4, 5 & 6
    73. 82. Senate Co-Voting: 65% Agreement
    74. 83. Senate Co-Voting: 70% Agreement
    75. 84. Senate Co-Voting: 80% Agreement
    76. 85. Senate Co-Voting: 90% Agreement
    77. 86. Senate Co-Voting: 95% Agreement
    78. 87. Analyzing Social Media Networks with NodeXL
    79. 88. Social Media Research Foundation
    80. 89. Sense-Making Loop
    81. 90. Sense-Making Loop: Expanded
    82. 91. Discovery Process: Systematic Yet Flexible
    83. 92. Discovery Process: Systematic Yet Flexible
    84. 93. Discovery Process: Systematic Yet Flexible
    85. 94. UN Millennium Development Goals To be achieved by 2015
    86. 95. 30th Anniversary Symposium May 22-23, 2013
    87. 96. For More Information
    88. 97. For More Information
  3. Spotfire Dashboard
  4. Research Notes
  5. Big Data Visualization

  1. Story
  2. Slides
    1. 1. Information Visualization for Knowledge Discovery
    2. 2. Turning Messy BigData into Actionable SmallData
    3. 3. Interdisciplinary research community
    4. 4. Design Issues
    5. 5. HCI Pride: Serving 5B Users
    6. 6. Obama Unveils “Big Data” Initiative (3/2012)
    7. 7. Information Visualization & Visual Analytics
    8. 8. Information Visualization & Visual Analytics
    9. 9. Information Visualization & Visual Analytics
    10. 10. Business takes action
    11. 11. Spotfire: Retinol’s role in embryos & vision
    12. 12. Spotfire: DC natality data
    13. 13. http://registration.spotfire.com/eval/default_edu.asp
    14. 14. 10M - 100M pixels: Large displays
    15. 15. 100M-pixels & more
    16. 16. 1M-pixels & less Small mobile devices
    17. 17. Information Visualization: Mantra
    18. 18. Information Visualization: Data Types
    19. 19. Anscombe’s Quartet
    20. 20. Anscombe’s Quartet
    21. 21. Anscombe’s Quartet
    22. 22. Temporal Data: TimeSearcher 1.3
    23. 23. Temporal Data: TimeSearcher 2.0
    24. 24. LifeLines: Patient Histories
    25. 25. LifeLines2: Align-Rank-Filter & Summarize
    26. 26. LifeFlow: Aggregation Strategy
    27. 27. LifeFlow: Interface with User Controls
    28. 33. EventFlow: Original Dataset
    29. 34. LABA_ICSs Merged
    30. 35. SABAs Merged
    31. 36. Align by First LABA_ICS
    32. 37. Reduce Window Size
    33. 38. EventFlow Team: Oracle support
    34. 39. Treemap: Gene Ontology
    35. 40. Treemap: Smartmoney MarketMap
    36. 41. Market falls steeply Feb 27, 2007, with one exception
    37. 42. Market falls steeply Sept 22, 2011, some exceptions
    38. 43. Market mixed, February 8, 2008
    39. 44. Market rises, September 1, 2010, Gold contrarians
    40. 45. Market rises, March 21, 2011, Sprint declines
    41. 46. Treemap: Newsmap (Marcos Weskamp)
    42. 47. Treemap: WHC Emergency Room
    43. 48. Treemap: WHC Emergency Room
    44. 49. Treemap: Supply Chain
    45. 50. Treemap: Nutritional Analysis
    46. 51. Treemap: Spotfire Bond Portfolio Analysis
    47. 52. Treemap: NY Times – Car&Truck Sales
    48. 53. Treemap (Voronoi): NY Times - Inflation
    49. 55. VisualComplexity.com : Manuel Lima
    50. 56. SocialAction
    51. 57. Network from Database Tables
    52. 58. NodeXL
    53. 59. NodeXL
    54. 60. NodeXL: Import Dialogs
    55. 61. Tweets at #WIN09 Conference: 2 groups
    56. 62. Flickr networks
    57. 63. Twitter discussion of #GOP
    58. 64. Analogy: Clusters Are Occluded
    59. 65. Separate Clusters Are More Comprehensible
    60. 66. Twitter networks: #SOTU
    61. 67. Group-In-A-Box: Twitter Network for #CI2012
    62. 69. Pennsylvania Innovation Network
    63. 71. Innovation Patterns: 11,000 vertices, 26,000 edges
    64. 72. No Location Philadelphia Innovation Clusters: People, Locations, Companies
    65. 74. Interactive Methods to Reveal Patterns
    66. 75. Senate Co-Voting
    67. 76. Group-In-A-Box by Region
    68. 77. Interactive Methods to Reveal Patterns
    69. 78. Motif Simplification
    70. 79. Motif Simplification
    71. 80. Motif Simplification
    72. 81. Clique Motifs & Glyphs: 4, 5 & 6
    73. 82. Senate Co-Voting: 65% Agreement
    74. 83. Senate Co-Voting: 70% Agreement
    75. 84. Senate Co-Voting: 80% Agreement
    76. 85. Senate Co-Voting: 90% Agreement
    77. 86. Senate Co-Voting: 95% Agreement
    78. 87. Analyzing Social Media Networks with NodeXL
    79. 88. Social Media Research Foundation
    80. 89. Sense-Making Loop
    81. 90. Sense-Making Loop: Expanded
    82. 91. Discovery Process: Systematic Yet Flexible
    83. 92. Discovery Process: Systematic Yet Flexible
    84. 93. Discovery Process: Systematic Yet Flexible
    85. 94. UN Millennium Development Goals To be achieved by 2015
    86. 95. 30th Anniversary Symposium May 22-23, 2013
    87. 96. For More Information
    88. 97. For More Information
  3. Spotfire Dashboard
  4. Research Notes
  5. Big Data Visualization

Story

Ben Shneiderman's 8 Golden Rules of Data Science

BYfl0XSCQAARbR-.jpg

Source: "8 Golden Rules of Data Science"

Preparation

  • Choose actionable problems & appropriate theories
  • Consult domain experts & generalists

Exploration

  • Examine data in isolation & contexually
  • Keep cleaning & add related data
  • Apply visualizations& statistical patterns, clusters, gaps, outliers, missing & uncertain data

Decision

  • Evaluate your efficacy, refine your theory
  • Take responsibility, own your failures
  • World is complex, proceed with humility

This would be fun to do his: Turning Messy BigData into Actionable SmallData, and work with his datasets.

Use of Spotfire:

10. Business takes action

11. Spotfire: Retinol’s role in embryos & vision

12. Spotfire: DC natality data

13. http://registration.spotfire.com/eval/default_edu.asp

17. Information Visualization: Mantra

Implemented in Spotifre: Overview, zoom & filter, details-on-demand

18. Information Visualization: Data Types

51. Treemap: Spotfire Bond Portfolio Analysis

97. For More Information

Ben's interactive visualization tools have led to commercial success stories such as SpotfireSmart Money's Market Map and the Hive Group, as well as research tools such as TimeSearcher, developed at UMD for time series data analysis.

He pioneered the highlighted textual link in 1983, and it became part of Hyperties, a precursor to the web. His move into information visualization spawned Spotfire, known for pharmaceutical drug discovery and genomic data analysis.

MORE TO FOLLOW

Slides

http://www.slideshare.net/BenShneiderman/info-vis-4222013dcvismeetupshneiderman

1. Information Visualization for Knowledge Discovery

Ben Shneiderman ben@cs.umd.edu @benbendc

Founding Director (1983-2000), Human-Computer Interaction Lab

Professor, Department of Computer Science

Member, Institute for Advanced Computer Studies

University of Maryland College Park, MD 20742

2. Turning Messy BigData into Actionable SmallData

@benbendc

University of Maryland College Park, MD 20742

3. Interdisciplinary research community

- Computer Science & Info Studies

- Psych, Socio, Poli Sci & MITH

(http://www.cs.umd.edu/hcil)

4. Design Issues

• Input devices & strategies

• Keyboards, pointing devices, voice

• Direct manipulation

• Menus, forms, commands

• Output devices & formats

• Screens, windows, color, sound

• Text, tables, graphics

• Instructions, messages, help

• Collaboration & Social Media

• Help, tutorials, training

• Search • Visualization

http://www.awl.com/DTUI 

Fifth Edition: 2010

5. HCI Pride: Serving 5B Users

Mobile, desktop, web, cloud

 Diverse users: novice/expert, young/old, literate/illiterate, abled/disabled, cultural, ethnic & linguistic diversity, gender, personality, skills, motivation, ...

 Diverse applications: E-commerce, law, health/wellness, education, creative arts, community relationships, politics, IT4ID, policy negotiation, mediation, peace studies, ...

 Diverse interfaces: Ubiquitous, pervasive, embedded, tangible, invisible, multimodal, immersive/augmented/virtual, ambient, social, affective, empathic, persuasive, ...

6. Obama Unveils “Big Data” Initiative (3/2012)

Big Data challenges:

• Developing scalable algorithms for processing imperfect data in distributed data stores

• Creating effective human- computer interaction tools for facilitating rapidly customizable visual reasoning for diverse missions.

http://www.whitehouse.gov/sites/defa...se_final_2.pdf `

7. Information Visualization & Visual Analytics

• Visual bands • Human percle • Trend, clus.. • Color, size,.. • Three challe • Meaningful vi • Interaction: w • Process mo 1999

8. Information Visualization & Visual Analytics

• Visual bandwidth is enormous • Human perceptual skills are remarkable • Trend, cluster, gap, outlier... • Color, size, shape, proximity... • Three challenges • Meaningful visual displays of massive da • Interaction: widgets & window coordinati • Process models for discovery 1999 2004

9. Information Visualization & Visual Analytics

• Visual bandwidth is enormous • Human perceptual skills are remarkable • Trend, cluster, gap, outlier... • Color, size, shape, proximity... • Three challenges • Meaningful visual displays of massive data • Interaction: widgets & window coordination • Process models for discovery 1999 2004 2010

10. Business takes action

• General Dynamics buys MayaViz

• Agilent buys GeneSpring

• Google buys Gapminder

• Oracle buys Hyperion

• Microsoft buys Proclarity

• InfoBuilders buys Advizor Solutions

• SAP buys (Business Objects buys Xcelsius & Inxight & Crystal Reports )

• IBM buys (Cognos buys Celequest) & ILOG

• TIBCO buys Spotfire

11. Spotfire: Retinol’s role in embryos & vision

12. Spotfire: DC natality data

13. http://registration.spotfire.com/eval/default_edu.asp

My Note: This URL does not work

14. 10M - 100M pixels: Large displays

15. 100M-pixels & more

16. 1M-pixels & less Small mobile devices

17. Information Visualization: Mantra

• Overview, zoom & filter, details-on-demand

• Overview, zoom & filter, details-on-demand

• Overview, zoom & filter, details-on-demand

• Overview, zoom & filter, details-on-demand

• Overview, zoom & filter, details-on-demand

• Overview, zoom & filter, details-on-demand

• Overview, zoom & filter, details-on-demand

• Overview, zoom & filter, details-on-demand

• Overview, zoom & filter, details-on-demand

• Overview, zoom & filter, details-on-demand

18. Information Visualization: Data Types

My Note: Two vertical texts that I cannot make out alongside the two groups below

• 1-D Linear Document Lens, SeeSoft, Info Mural

• 2-D Map GIS, ArcView, PageMaker, Medical imagery

• 3-D World CAD, Medical, Molecules, Architecture 

 

• Multi-Var Spotfire, Tableau, Qliktech, Visual Insight

• Temporal LifeLines, TimeSearcher, Palantir, DataMontage

• Tree Cone/Cam/Hyperbolic, SpaceTree, Treemap

• Network Pajek, UCINet, NodeXL, Gephi, Tom Sawyer

http://infosthetics.com

http://visualcomplexity.com

http://eagereyes.org

http://flowingdata.com

http://perceptualedge.com

http://datakind.org

http://visual.ly

http://Visualizing.org

http://infovis.org

19. Anscombe’s Quartet

1 2 3 4x y x y x y x y10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.7613.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.8411.0 8.33 11.0 9.26 11.0 7.81 8.0 8.4714.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.5012.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89

20. Anscombe’s Quartet

Property Value

Mean of x 9.0

Variance of x 11.0

Mean of y 7.5

Variance of y 4.12

Correlation 0.816

Linear regression y = 3 + 0.5x

21. Anscombe’s Quartet

22. Temporal Data: TimeSearcher 1.3

• Time series

• Stocks

• Weather

• Genes

• User-specified patterns

• Rapid search

23. Temporal Data: TimeSearcher 2.0

• Long Time series (>10,000 time points)

• Multiple variables

• Controlled precision in match (Linear, offset, noise, amplitude)

24. LifeLines: Patient Histories

http://www.cs.umd.edu/hcil/lifelines

25. LifeLines2: Align-Rank-Filter & Summarize

26. LifeFlow: Aggregation Strategy

Temporal Categorical Data (4 records)

LifeLines2 format

Tree of Event Sequences

LifeFlow Aggregation

http://www.cs.umd.edu/hcil/lifeflow

27. LifeFlow: Interface with User Controls

My Note: Slides 28-32 with no titles

33. EventFlow: Original Dataset

34. LABA_ICSs Merged

35. SABAs Merged

36. Align by First LABA_ICS

37. Reduce Window Size

39. Treemap: Gene Ontology

+ Space filling

+ Space limited

+ Color coding

+ Size coding- Requires learning (Shneiderman, ACM Trans. on Graphics, 1992 & 2003)

http://www.cs.umd.edu/hcil/treemap/

40. Treemap: Smartmoney MarketMap

http://www.smartmoney.com/marketmap

41. Market falls steeply Feb 27, 2007, with one exception

42. Market falls steeply Sept 22, 2011, some exceptions

43. Market mixed, February 8, 2008

Energy & Technology up, Financial & Health Care down

44. Market rises, September 1, 2010, Gold contrarians

45. Market rises, March 21, 2011, Sprint declines

46. Treemap: Newsmap (Marcos Weskamp)

http://newsmap.jp

47. Treemap: WHC Emergency Room

(6304 patients in Jan2006)

Group by Admissions/MF, size by service time, color by age

48. Treemap: WHC Emergency Room

(6304 patients in Jan2006) (only those service time >12 hours)

Group by Admissions/MF, size by service time, color by age

49. Treemap: Supply Chain

http://www.hivegroup.com

50. Treemap: Nutritional Analysis

http://www.hivegroup.com

51. Treemap: Spotfire Bond Portfolio Analysis

http://www.spotfire.com

52. Treemap: NY Times – Car&Truck Sales

http://www.cs.umd.edu/hcil/treemap/

53. Treemap (Voronoi): NY Times - Inflation

http://www.nytimes.com/interactive/2...G_GRAPHIC.html

My Note: Slide 54 with no title

55. VisualComplexity.com : Manuel Lima

56. SocialAction

• Integrates statistics & visualization

• 4 case studies, 4-8 weeks (journalist, bibliometrician, terrorist analyst, organizational analyst)

• Identified desired features, gave strong positive feedback about benefits of integration

http://www.cs.umd.edu/hcil/socialaction

Perer & Shneiderman, CHI2008, IEEE CG&A 2009

57. Network from Database Tables

http://www.centrifugesystems.com

58. NodeXL

Network Overview for Discovery & Exploration in Excel

http://www.codeplex.com/nodexl

59. NodeXL

Network Overview for Discovery & Exploration in Excel

http://www.codeplex.com/nodexl

60. NodeXL: Import Dialogs

http://www.codeplex.com/nodexl

61. Tweets at #WIN09 Conference: 2 groups

62. Flickr networks

63. Twitter discussion of #GOP

Red: Republicans, anti-Obama, mention Fox

Blue: Democrats, pro-Obama, mention CNN

Green: non-affiliated

Node size is number of followers Politico is major bridging group

64. Analogy: Clusters Are Occluded

Hard to count nodes, clusters

65. Separate Clusters Are More Comprehensible

66. Twitter networks: #SOTU

67. Group-In-A-Box: Twitter Network for #CI2012

68. Twitter Network for “TTW”

69. Pennsylvania Innovation Network

64. No Location Philadelphia Patent Tech Navy SBIR (federal) PA DCED (state) Related patent 2: Federal agencyPharmaceutical/Medical 3: EnterprisePittsburgh Metro 5: Inventors 9: Universities 10: PA DCED 11/12: Phil/Pitt metro cnty 13-15: Semi-rural/rural cnty 17: Foreign countries 19: Other statesWestinghouse Electric

My Note: Slide 70 with no title

71. Innovation Patterns: 11,000 vertices, 26,000 edges

72. No Location Philadelphia Innovation Clusters: People, Locations, Companies

Patent Tech Navy SBIR (federal) PA DCED (state) Related patent 2: Federal agency

Pharmaceutical/Medical 3: Enterprise

Pittsburgh Metro 5: Inventors 9: Universities 10: PA DCED 11/12: Phil/Pitt metro cnty 13-15: Semi-rural/rural cnty 17: Foreign countries 19: Other statesWestinghouse Electric

My Note: Slide 73 with no title

74. Interactive Methods to Reveal Patterns

Filtering Node & link attribute values or statistics

Clustering Cluster algorithmically by link connectivity

Grouping Group based on node attributes

Motif Simplification Common, meaningful structures replaced with simplified glyphs

75. Senate Co-Voting

76. Group-In-A-Box by Region

77. Interactive Methods to Reveal Patterns

Filtering Node & link attribute values or statistics

Clustering Cluster algorithmically by link connectivity

Grouping Group based on node attributes

Motif Simplification Common, meaningful structures replaced with simplified glyphs

78. Motif Simplification

(a) Fan motifs & glyphs

(b) Connector motifs & glyphs

79. Motif Simplification

80. Motif Simplification

81. Clique Motifs & Glyphs: 4, 5 & 6

82. Senate Co-Voting: 65% Agreement

83. Senate Co-Voting: 70% Agreement

84. Senate Co-Voting: 80% Agreement

85. Senate Co-Voting: 90% Agreement

86. Senate Co-Voting: 95% Agreement

87. Analyzing Social Media Networks with NodeXL

I Getting Started with Analyzing Social Media Networks

1. Introduction to Social Media and Social Networks

2. Social media: New Technologies of Collaboration

3. Social Network Analysis.

II NodeXL Tutorial: Learning by Doing

4. Layout, Visual Design & Labeling

5. Calculating & Visualizing Network Metrics 

6. Preparing Data & Filtering

7. Clustering & Grouping

III Social Media Network Analysis Case Studies

8. Email

9. Threaded Networks

10. Twitter

11. Facebook  

12. WWW

13. Flickr

14. YouTube 

15. Wiki Networks

http://www.elsevier.com/wps/find/boo...54/description

88. Social Media Research Foundation

Researchers who want to

- create open tools

- generate & host open data

- support open scholarship

Map, measure & understand social media

Support tool projects to collection, analyze & visualize social media data.

http://smrfoundation.org

89. Sense-Making Loop

Thomas & Cook: Illuminating the Path (2004)

90. Sense-Making Loop: Expanded

Thomas & Cook: Illuminating the Path (2004)

91. Discovery Process: Systematic Yet Flexible

Preparation

• Own the problem & define the schedule

• Data cleaning & conditioning

• Handle missing & uncertain data

• Extract subsets & link to related information

92. Discovery Process: Systematic Yet Flexible

Preparation

• Own the problem & define the schedule

• Data cleaning & conditioning

• Handle missing & uncertain data

• Extract subsets & link to related information

Purposeful exploration – Hypothesis testing

• Range & distribution

• Relationships & correlations

• Clusters & gaps

• Outliers & anomalies

• Aggregation & summary

• Split & trellis

• Temporal comparisons & multiple views

• Statistics & forecasts

93. Discovery Process: Systematic Yet Flexible

Preparation

• Own the problem & define the schedule

• Data cleaning & conditioning

• Handle missing & uncertain data

• Extract subsets & link to related information

Purposeful exploration – Hypothesis testing

• Range & distribution

• Relationships & correlations

• Clusters & gaps

• Outliers & anomalies

• Aggregation & summary

• Split & trellis

• Temporal comparisons & multiple views

• Statistics & forecasts

Situated decision making - Social context • Annotation & marking

• Collaboration & coordination

• Decisions & presentations

94. UN Millennium Development Goals To be achieved by 2015

• Eradicate extreme poverty and hunger

• Achieve universal primary education

• Promote gender equality and empower women

• Reduce child mortality

• Improve maternal health

• Combat HIV/AIDS, malaria and other diseases

• Ensure environmental sustainability

• Develop a global partnership for development

95. 30th Anniversary Symposium May 22-23, 2013

http://www.cs.umd.edu/hcil

96. For More Information

• Visit the HCIL website for 700+ papers & info on videos

http://www.cs.umd.edu/hcil

• See Chapter 14 on Info Visualization

Shneiderman, B. and Plaisant, C., Designing the User Interface: Strategies for Effective Human-Computer Interaction: Fifth Edition (2010)

http://www.awl.com/DTUI

• Edited Collections:

Card, S., Mackinlay, J., and Shneiderman, B. (1999) Readings in Information Visualization: Using Vision to Think

Bederson, B. and Shneiderman, B. (2003) The Craft of Information Visualization: Readings and Reflections

97. For More Information

• Treemaps

• HiveGroup: http://www.hivegroup.com

• Smartmoney: http://www.smartmoney.com/marketmap

• HCIL Treemap 4.0: http://www.cs.umd.edu/hcil/treemap

• Spotfire: http://www.spotfire.com

• TimeSearcher: http://www.cs.umd.edu/hcil/timesearcher

• NodeXL: http://nodexl.codeplex.com

• Hierarchical Clustering Explorer: http://www.cs.umd.edu/hcil/hce

• LifeLines2: http://www.cs.umd.edu/hcil/lifelines2

• EventFlow: http://www.cs.umd.edu/hcil/eventflow

Spotfire Dashboard

April 22 · 6:30 PM

Big Data has become a ubiquitous phrase because its simple phrase speaks to everyone.  'Big' depends on your perspective, one person's standard is another's extreme, and in all cases we need to explore our data if it is to support our decisions.

Data Visualization DC is happy to coordinate with Big Data Week to present Professor Ben Shneiderman's "Information Visualization for Knowledge Discovery: Turning Messy BigData into Actionable SmallData".  Ben's interactive visualization tools have led to commercial success stories such as SpotfireSmart Money's Market Map and the Hive Group, as well as research tools such as TimeSearcher, developed at UMD for time series data analysis. The integration of statistics with visualizations has been successfully applied to temporal event sequences such as electronic health records (Lifelines2 and Lifeflow) and social network data (SocialAction and NodeXL).

Bio

Ben Shneiderman is a Professor in the Department of Computer Science, Founding Director (1983-2000) of the Human-Computer Interaction Laboratory, and a member of the Institute for Advanced Computer Studies at the University of Maryland, College Park. He was elected as a Fellow of the Association for Computing (ACM) in 1997 and a Fellow of the American Association for the Advancement of Science (AAAS) in 2001. He received the ACM SIGCHI Lifetime Achievement Award in 2001. He is a member of the National Academy of Engineering.

He pioneered the highlighted textual link in 1983, and it became part of Hyperties, a precursor to the web. His move into information visualization spawned Spotfire, known for pharmaceutical drug discovery and genomic data analysis. He is a technical advisor for the treemap visualization producer, The Hive Group.

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