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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  • "The IN Cell Analyzer automated microscope was used to identify proteins influencing the division of human cells. After the images were analyzed, quantitative results were transferred to Spotfire DecisionSite. This screen revealed the previously unknown involvement of the retinol binding protein RBP1 in cell cycle control.(Stubbs S, & Thomas N. 2006 Methods in Enzymology; 414:1-21.) Retinol a form of Vitamin A plays a crucial role in vision and during embryonic development"  
  • Using LifeFlow, 7,041 patients are aggregated into this visualization and LifeFlow immediately reveal the most common pattern, which you could not do easily in SQL. You could easily notice this huge pattern “Arrival -> ER -> Exit”, meaning patients who visited with minor injuries or simple conditions and left the hospital immediately after receiving their treatment. When hovering the mouse over, LifeFlow displays a tooltip that gives more information, such as number of patients and other statistics, and also shows the distribution of the patients. As the horizontal gap represents time, you can see from the distribution that some patients left the hospital very quickly after visiting the emergency room while some of them stayed longer. *optional The second most common pattern is “Arrival (Blue) -> ER (Pink) -> Floor (Green) -> Exit (Cyan)”, meaning patients who were admitted to observe the conditions and then everything went well so they left the hospital. You can also use the horizontal gap to compare these patients with the patients who exit from the emergency room. Comparing the gap from pink to cyan and pink to green, you can see that the gap from pink to green is smaller than pink to cyan, so the patients were transferred to Floor faster than exit the hospital in average. You have seen the two most common cases, now I will remove the common patterns so we can analyze the less frequent patterns.
  • After removing all the common cases, we have 344 patients left. These are mostly the patients who were admitted. There are many information that I can explain from this visualization here, but I will go straight into the case that our physician partners are mostly interested in. The mouse is pointing at this sequence, which represents the “bounce backs” patients, meaning patients who were transferred from ICU to Floor because they seemed to get better, however, they were transferred back to the ICU. So the physician are interested in finding these patients to analyze what made them made the wrong decisions. *optional Another case is the step ups, which means the patients whose level of care were escalated to higher level, you can see from the visualization that there were patients who were transferred from ER to Floor (green) to ICU (red) and IMC (orange). The number of these patients and the average transferred time could be compare to the hospital standards to measure the quality of care.
  • Ben: This slide is optional. You can use it to show that when you click on the bounce backs patients, you can get the details of each patient in LifeLines2 view.
  • Another interesting feature is you can align by a particular event. For example, if you want to know what happened before and after the patients went to the ICU, you can align by ICU. The dash line separate between what happened before and what happened after. You can see that the ICU patients mostly came from the ER (pink), and most of them were transferred to Floor (green) after that. Unfortunately, some of them died after they were transferred to the ICU (black). From this visualization, you may notice a small pattern in the bottom. Let me zoom in.
  • So this patient was dead before transferred to the ICU, which is impossible. Of course, this must be problem with data entry. But we may never notice it if the data are hidden in the database. Therefore, you can see that LifeFlow support this kind of analysis by giving overview, showing common trends, providing summary of every sequences, you can do SQL and calculate average for every transfer if you like, but in LifeFlow, it is right there, you just need to move your mouse over. showing every possible transfer pattern and may led you to a discovery of surprising pattern.
  • Live Demonstration
  • Aligning sales and marketing is essential for success. The graph on the left shows sales people linked to opportunities, including industry. The thicker the line, the higher the probability of closing the deal. The larger the dollar sign, the bigger the deal. Sullivan, Vazquez and Distefano are performing the best. The upper right shows the number of deals by stage in the sales cycle. The blue bubble chart shows potential revenue by marketing program and stage in the sales cycle. Search engine optimization and inbound links from Web sites have the biggest impact. Armed with this information, marketing managers can advertise to the financial services and manufacturing sectors through specific tactics, and sales managers can see the performance of the reps and the industries where they are successful.
  • Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#WIN09” used by attendees of a conference on Network Science at NYU in September 2009. Each user’s node is sized proportional to the number of tweets they have ever made to that date.
  • Chapter 3, Figure 1 (page 6). A NodeXL social media network diagram of relationships among Twitter users mentioning the hashtag “#WIN09” used by attendees of a conference on Network Science at NYU in September 2009. Each user’s node is sized proportional to the number of tweets they have ever made to that date.
  • Figure 13.24. NodeXL network of Flickr users who comment on Marc_Smith’s photos (network depth 1.5; edge weight≥4).
  • 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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