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Info vis 4-22-2013-dc-vis-meetup-shneiderman

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Slide show on Information visualization for the Data Visualization Meetup in Washington, DC during www.bigdataweek.com April 22, 2013

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Info vis 4-22-2013-dc-vis-meetup-shneiderman

  1. 1. Information Visualization for Knowledge Discovery Ben Shneiderman ben@cs.umd.edu @benbendcFounding 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. 2. Turning Messy BigDatainto Actionable SmallData @benbendc University of Maryland College Park, MD 20742
  3. 3. Interdisciplinary research community - Computer Science & Info Studies - Psych, Socio, Poli Sci & MITH (www.cs.umd.edu/hcil)
  4. 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 www.awl.com/DTUI Fifth Edition: 2010• Help, tutorials, training• Search • Visualization
  5. 5. HCI Pride: Serving 5B UsersMobile, 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. 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/default/files/microsites/ostp/big_data_press_release_final_2.pdf `
  7. 7. Information Visualization & Visual Analytics • Visual bands • Human percle • Trend, clus.. • Color, size,.. • Three challe • Meaningful vi • Interaction: w • Process mo 1999
  8. 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. 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. 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. 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• 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. 18. . Information Visualization: Data Types • 1-D Linear Document Lens, SeeSoft, Info Mural • 2-D Map GIS, ArcView, PageMaker, Medical imagery • 3-D World CAD, Medical, Molecules, Architecturezi Vc S i • Multi-Var Spotfire, Tableau, Qliktech, Visual Insight • Temporal LifeLines, TimeSearcher, Palantir, DataMontage • Tree Cone/Cam/Hyperbolic, SpaceTree, Treemap • Networkzi V f nI Pajek, UCINet, NodeXL, Gephi, Tom Sawyer o infosthetics.com visualcomplexity.com eagereyes.org flowingdata.com perceptualedge.com datakind.org visual.ly Visualizing.org infovis.org
  19. 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. 20. Anscombe’s Quartet 1 2 3 4x y x y x y x y Property Value10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 Mean of x 9.0 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 Variance of x 11.013.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 Mean of y 7.5 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 Variance of y 4.1211.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 Correlation 0.81614.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 Linear regression y = 3 + 0.5x 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
  21. 21. Anscombe’s Quartet
  22. 22. Temporal Data: TimeSearcher 1.3• Time series • Stocks • Weather • Genes• User-specified patterns• Rapid search
  23. 23. Temporal Data: TimeSearcher 2.0• Long Time series (>10,000 time points)• Multiple variables• Controlled precision in match (Linear, offset, noise, amplitude)
  24. 24. LifeLines: Patient Histories www.cs.umd.edu/hcil/lifelines
  25. 25. LifeLines2: Align-Rank-Filter & Summarize
  26. 26. LifeFlow: Aggregation Strategy Temporal Categorical Data (4 records) LifeLines2 format Tree of Event Sequences LifeFlow Aggregation www.cs.umd.edu/hcil/lifeflow
  27. 27. LifeFlow: Interface with User Controls
  28. 28. EventFlow: Original Dataset
  29. 29. LABA_ICSs Merged
  30. 30. SABAs Merged
  31. 31. Align by First LABA_ICS
  32. 32. Reduce Window Size
  33. 33. EventFlow Team: Oracle support www.cs.umd.edu/hcil/eventflowwww.umdrightnow.umd.edu/news/umd-research-team-developing-powerful-data-visualization-tool-support-oracle
  34. 34. Treemap: Gene Ontology+ Space filling+ Space limited+ Color coding+ Size coding- Requires learning (Shneiderman, ACM Trans. on Graphics, 1992 & 2003) www.cs.umd.edu/hcil/treemap/
  35. 35. Treemap: Smartmoney MarketMap www.smartmoney.com/marketmap
  36. 36. Market falls steeply Feb 27, 2007, with one exception
  37. 37. Market falls steeply Sept 22, 2011, some exceptions
  38. 38. Market mixed, February 8, 2008Energy & Technology up, Financial & Health Care down
  39. 39. Market rises, September 1, 2010, Gold contrarians
  40. 40. Market rises, March 21, 2011, Sprint declines
  41. 41. Treemap: Newsmap (Marcos Weskamp) newsmap.jp
  42. 42. Treemap: WHC Emergency Room (6304 patients in Jan2006)Group by Admissions/MF, size by service time, color by age
  43. 43. 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
  44. 44. Treemap: Supply Chain www.hivegroup.com
  45. 45. Treemap: Nutritional Analysis www.hivegroup.com
  46. 46. Treemap: Spotfire Bond Portfolio Analysis www.spotfire.com
  47. 47. Treemap: NY Times – Car&Truck Sales www.cs.umd.edu/hcil/treemap/
  48. 48. Treemap (Voronoi): NY Times - Inflationwww.nytimes.com/interactive/2008/05/03/business/20080403_SPENDING_GRAPHIC.html
  49. 49. VisualComplexity.com : Manuel Lima
  50. 50. 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 www.cs.umd.edu/hcil/socialaction Perer & Shneiderman, CHI2008, IEEE CG&A 2009
  51. 51. Network from Database Tables www.centrifugesystems.com
  52. 52. NodeXL: Network Overview for Discovery & Exploration in Excelwww.codeplex.com/nodexl
  53. 53. NodeXL:Network Overview for Discovery & Exploration in Excel www.codeplex.com/nodexl
  54. 54. NodeXL: Import Dialogs www.codeplex.com/nodexl
  55. 55. Tweets at #WIN09 Conference: 2 groups
  56. 56. Flickr networks
  57. 57. 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
  58. 58. Analogy: Clusters Are OccludedHard to count nodes, clusters
  59. 59. Separate Clusters Are More Comprehensible
  60. 60. Twitter networks: #SOTU
  61. 61. Group-In-A-Box: Twitter Network for #CI2012
  62. 62. Twitter Network for “TTW”
  63. 63. Pennsylvania Innovation Network
  64. 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
  65. 65. Innovation Patterns: 11,000 vertices, 26,000 edges
  66. 66. No Location Philadelphia Innovation Clusters: People, Locations, Companies 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
  67. 67. Interactive Methods to Reveal PatternsFiltering Node & link attribute values or statisticsClustering Cluster algorithmically by link connectivityGrouping Group based on node attributesMotif Common, meaningful structures Simplification replaced with simplified glyphs
  68. 68. Senate Co-Voting
  69. 69. Group-In-A-Box by Region
  70. 70. Interactive Methods to Reveal PatternsFiltering Node & link attribute values or statisticsClustering Cluster algorithmically by link connectivityGrouping Group based on node attributesMotif Common, meaningful structures Simplification replaced with simplified glyphs
  71. 71. Motif Simplification(a) Fan motifs & glyphs (b) Connector motifs & glyphs
  72. 72. Motif Simplification
  73. 73. Motif Simplification
  74. 74. Clique Motifs & Glyphs: 4, 5 & 6
  75. 75. Senate Co-Voting: 65% Agreement
  76. 76. Senate Co-Voting: 70% Agreement
  77. 77. Senate Co-Voting: 80% Agreement
  78. 78. Senate Co-Voting: 90% Agreement
  79. 79. Senate Co-Voting: 95% Agreement
  80. 80. Analyzing Social Media Networks with NodeXLI. 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 AnalysisII. NodeXL Tutorial: Learning by Doing 4. Layout, Visual Design & Labeling 5. Calculating & Visualizing Network Metrics  6. Preparing Data & Filtering 7. Clustering &GroupingIII Social Media Network Analysis Case Studies 8. Email 9. Threaded Networks 10. Twitter 11. Facebook   12. WWW 13. Flickr 14. YouTube  15. Wiki Networks  www.elsevier.com/wps/find/bookdescription.cws_home/723354/description
  81. 81. Social Media Research FoundationResearchers who want to - create open tools - generate & host open data - support open scholarshipMap, measure & understand social media  Support tool projects to collection, analyze & visualize social media data.   smrfoundation.org
  82. 82. Sense-Making Loop Thomas & Cook: Illuminating the Path (2004)
  83. 83. Sense-Making Loop: Expanded Thomas & Cook: Illuminating the Path (2004)
  84. 84. 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
  85. 85. 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
  86. 86. 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
  87. 87. 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
  88. 88. 30th Anniversary Symposium May 22-23, 2013 www.cs.umd.edu/hcil
  89. 89. For More Information• Visit the HCIL website for 700+ papers & info on videos 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) 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
  90. 90. For More Information• Treemaps • HiveGroup: www.hivegroup.com • Smartmoney: www.smartmoney.com/marketmap • HCIL Treemap 4.0: www.cs.umd.edu/hcil/treemap• Spotfire: www.spotfire.com• TimeSearcher: www.cs.umd.edu/hcil/timesearcher• NodeXL: nodexl.codeplex.com• Hierarchical Clustering Explorer: www.cs.umd.edu/hcil/hce• LifeLines2: www.cs.umd.edu/hcil/lifelines2• EventFlow: www.cs.umd.edu/hcil/eventflow

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