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Ben Shneiderman: Thrill of Discovery

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On Tuesday 18 September 2007, Ben Shneiderman gave a talk at the Centre for HCI Design, City University London, on the topic of information visualisation for high-dimensional spaces. Over 100 people …

On Tuesday 18 September 2007, Ben Shneiderman gave a talk at the Centre for HCI Design, City University London, on the topic of information visualisation for high-dimensional spaces. Over 100 people from industry and academia attended the talk.

http://hcid.soi.cty.ac.uk/

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    • 1. The Thrill of Discovery: Information Visualization for High Dimensional Spaces Ben Shneiderman [email_address] 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. Interdisciplinary research community - Computer Science & Info Studies - Psych, Socio, Poli Sci & MITH (www.cs.umd.edu/hcil)
    • 3. Scientific Approach (beyond user friendly)
      • Specify users and tasks
      • Predict and measure
        • time to learn
        • speed of performance
        • rate of human errors
        • human retention over time
      • Assess subjective satisfaction (Questionnaire for User Interface Satisfaction)
      • Accommodate individual differences
      • Consider social, organizational & cultural context
    • 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 & communities
      • Manuals, tutorials, training
      www.awl.com/DTUI
    • 5. U.S. Library of Congress
      • Scholars, Journalists, Citizens
      • Teachers, Students
    • 6. Visible Human Explorer (NLM)
      • Doctors
      • Surgeons
      • Researchers
      • Students
    • 7. NASA Environmental Data
      • Scientists
      • Farmers
      • Land planners
      • Students
    • 8. Bureau of the Census
      • Economists, Policy makers, Journalists
      • Teachers, Students
    • 9. NSF Digital Government Initiative
      • Find what you need
      • Understand what you Find
      • Census,
      • NCHS,
      • BLS, EIA,
      • NASS, SSA
      www.ils.unc.edu/govstat/
    • 10. International Children’s Digital Library www.childrenslibrary.org
    • 11. Piccolo: Toolkit for 2D zoomable objects
      • Structured canvas of graphical objects in a hierarchical scenegraph
        • Zooming animation
        • Cameras, layers
      • Open, Extensible & Efficient
      • Java, C#, PocketPC versions
      • www.cs.umd.edu/hcil/piccolo
      AppLens & Launch Tile UMD, Microsoft Research DateLens Windsor Interfaces, Inc. Cytoscape Institute for Systems Biology Memorial Sloan-Kettering Institut Pasteur UCSD TreePlus UMD
    • 12. Information Visualization
          • The eye…
          • the window of the soul,
          • is the principal means
          • by which the central sense
          • can most completely and
          • abundantly appreciate
          • the infinite works of nature.
          • Leonardo da Vinci
          • (1452 - 1519)
    • 13. Using Vision to Think
      • Visual bandwidth is enormous
        • Human perceptual skills are remarkable
          • Trend, cluster, gap, outlier...
          • Color, size, shape, proximity...
        • Human image storage is fast and vast
      • Opportunities
        • Spatial layouts & coordination
        • Information visualization
        • Scientific visualization & simulation
        • Telepresence & augmented reality
        • Virtual environments
    • 14. Spotfire: Retinol’s role in embryos & vision
    • 15. Spotfire: DC natality data
    • 16.  
    • 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
      • 1-D Linear Document Lens, SeeSoft, Info Mural, Value Bars
      • 2-D Map GIS, ArcView, PageMaker, Medical imagery
      • 3-D World CAD, Medical, Molecules, Architecture
      • Multi-Var Parallel Coordinates, Spotfire, XGobi, Visage, Influence Explorer, TableLens, DEVise
      • Temporal Perspective Wall, LifeLines, Lifestreams, Project Managers, DataSpiral
      • Tree Cone/Cam/Hyperbolic, TreeBrowser, Treemap
      • Network Netmap, netViz, SeeNet, Butterfly, Multi-trees
      (Online Library of Information Visualization Environments) otal.umd.edu/Olive InfoViz SciViz .
    • 19. ManyEyes: A web sharing platform http://services.alphaworks.ibm.com/manyeyes/app
    • 20. Treemap: view large trees with node values
      • Space filling
      • Space limited
      • Color coding
      • Size coding
      • Requires learning
      (Shneiderman, ACM Trans. on Graphics , 1992 & 2003) TreeViz (Mac, Johnson, 1992) NBA-Tree(Sun, Turo, 1993) Winsurfer (Teittinen, 1996) Diskmapper (Windows, Micrologic) SequoiaView, Panopticon, HiveGroup, Solvern Treemap4 (UMd, 2004)
    • 21. Treemap: Stock market, clustered by industry
    • 22. www.hivegroup.com Treemap: Newsmap
    • 23. Treemap: Gene Ontology http://www.cs.umd.edu/hcil/treemap/
    • 24. www.hivegroup.com Treemap: Product catalogs
    • 25.  
    • 26.  
    • 27. LifeLines: Patient Histories
    • 28. LifeLines: Customer Histories
      • Temporal data visualization
      • Medical patient histories
      • Customer relationship management
      • Legal case histories
    • 29. Temporal Data: TimeSearcher 1.3
      • Time series
        • Stocks
        • Weather
        • Genes
      • User-specified patterns
      • Rapid search
    • 30. Temporal Data: TimeSearcher 2.0
      • Long Time series (>10,000 time points)
      • Multiple variables
      • Controlled precision in match (Linear, offset, noise, amplitude)
    • 31. Goal: Find Features in Multi-Var Data
      • Clear vision of what the data is
      • Clear goal of what you are looking for
      • Systematic strategy for examining all views
      • Ranking of views to guide discovery
      • Tools to record progress & annotate findings
    • 32. Multi-V: Hierarchical Clustering Explorer www.cs.umd.edu/hcil/hce/ “ HCE enabled us to find important clusters that we didn’t know about.” - a user
    • 33. Do you see anything interesting?
    • 34. What features stand out?
    • 35. Correlation…What else?
    • 36. … and Outliers He Rn
    • 37. Demonstration
      • US counties census data
        • 3138 counties
        • 14 dimensions : population density, poverty level, unemployment, etc.
      Demo
    • 38. Rank-by-Feature Framework: 1D Ranking Criterion Rank-by-Feature Prism Score List Manual Projection Browser
    • 39. Rank-by-Feature Framework: 2D Ranking Criterion Rank-by-Feature Prism Score List Manual Projection Browser
    • 40. Ranking Criterion: Pearson correlation (0.996, 0.31, 0.01, -0.69) Ranking Criterion: Uniformity (entropy) (6.7, 6.1, 4.5, 1.5) A Ranking Example 3138 U.S. counties with 17 attributes
    • 41. HCE Status
      • In collaboration and sponsored by Eric Hoffman: Children’s National Medical Center
      • Phd work of Jinwook Seo
      • 72K lines of C++ codes
      • 4,000+ downloads since April 2002
      • www.cs.umd.edu/hcil/hce
    • 42. Evaluation Methods
      • Ethnographic Observational Situated
      • Multi-Dimensional
      • In-depth
      • Long-term
      • Case studies
    • 43. Evaluation Methods
      • Ethnographic Observational Situated
      • Multi-Dimensional
      • In-depth
      • Long-term
      • Case studies
          • Domain Experts Doing Their Own Work for Weeks & Months
    • 44. Science 2.0 Evaluation Methods
      • Ethnographic Observational Situated
      • Multi-Dimensional
      • In-depth
      • Long-term
      • Case studies
      MILCs Shneiderman & Plaisant, BeLIV workshop , 2006
    • 45. MILC example
      • Evaluate Hierarchical Clustering Explorer
      • Focused on rank-by-feature framework
      • 3 case studies, 4-8 weeks (molecular biologist, statistician, meteorologist)
      • 57 email surveys
      • Identified problems early, gave strong positive feedback about benefits of rank-by-feature
      Seo & Shneiderman, IEEE TVCG 12,3, 2006
    • 46. MILC example
      • Evaluate SocialAction
      • Focused on integrating 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
      Perer & Shneiderman, 2007
    • 47. Case Study Methodology
      • 1) Interview (1 hr)
      • 2) Training (2 hr)
      • 3) Early Use (2-4 weeks)
      • 4) Mature Use (2-4 weeks)
      • 5) Outcome (1 hr)
    • 48. Network Data
      • Nodes & Links
        • Relationships & communication
        • Scientific/legal citations
      • Difficult to complete tasks
        • Occlusion
        • Complexity
    • 49. Network Data
      • Network
      • Visualization
      • with
      • Semantic
      • Substrates
      • Meaningful
      • layout of nodes
      • User controlled
      • visibility of links
    • 50. Network Data
    • 51. Take Away Message
      • Rank-by-Feature Framework
      • Decomposition of complex problems into multiple simpler problems wins
      • Ranking guides discovery
      • Systematic strategies
      • www.cs.umd.edu/hcil/hce
    • 52. 25 th Annual Symposium May 30, 2008 www.cs.umd.edu/hcil
    • 53. 6 th Creativity & Cognition Conference
      • Washington, DC June 13-15, 2007
      • Receptions at Nat’l Academy of Sciences & Corcoran Gallery of Art
      • Expand community of researchers
      • Bridge to software developers
      • Encourage art & science thinking
      www.cs.umd.edu/hcil/CC2007 http://www.cs.umd.edu/hcil/CC2007/
    • 54. Information Visualization: Tasks
      • Overview Gain an overview of the entire collection
      • Zoom Zoom in on items of interest
      • Filter Filter out uninteresting items
      • Details-on-demand Select an item or group and get details when needed
      • Relate View relationships among items
      • History Keep a history of actions to support undo, replay, and progressive refinement
      • Extract Allow extraction of sub-collections and of the query parameters
    • 55. Goal: Find Features in Multi-D Data
      • Finding correlations, clusters, outliers, gaps,  Cognitive difficulties in >3D
      • Therefore utilize low-dimensional projections
        • Perceptual efficiency in 1D and 2D
        • Use Rank-by-Feature Framework to guide discovery