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Information Visualization: Ten Years in Review

Information Visualization: Ten Years in Review. Xia Lin Drexel University. Before 1990. Static graphical representation Graphics are made, not generated Graphics do not support interactions Graphics illustrate the organization of information

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Information Visualization: Ten Years in Review

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  1. Information Visualization:Ten Years in Review Xia Lin Drexel University

  2. Before 1990 • Static graphical representation • Graphics are made, not generated • Graphics do not support interactions • Graphics illustrate the organization of information • Graphics are used to help the analysis of information structures • Examples: • Maps based on citation analysis • Semantic term relationships • Semantic Net representation

  3. Around 1990 • Scientific data visualization • Popularity of Macintosh and Windows • Availability of computational power

  4. Motivations • For data analysis • Visual inspection of data properties • Dimensional deduction • For graphical representation of large amount of data • Clustering and grouping • Discovery of hidden internal structures • For visual interaction with the data • interactive online searching • browse large amount of information

  5. Motivations • To utilize human perception for information seeking • Human can apprehend relationships on graphics fast and sometime intuitively • Human can understand graphical relationships that otherwise difficult to represent • To understand/reveal information structures • Understand information structures help online searching and retrieval • Reveal semantic structures through graphical representation

  6. Around 1995 • IV for IR starts to get popular before of some web applications • HotSause • SemioMap • WebCutter (Mapuciino) • AltaVista’s LiveTopic • Xerox PARC’s research prototypes

  7. Expectations • Most of these systems did not live up to their expectations • Limited success • No clear advantages over other approaches • Many are “for demonstration only” • not practical • No instant mapping and visualizing • Not easy to be understood by the user

  8. Lessons • Applications that “Look great” do not guarantee to have users. • Visualization tools should reduce, rather than adding cognitive loads to the searcher. • No one feels that he/she has to use these visualization tools yet.

  9. Problems • Precision and Clarity • If all details are shown, the result is confusion • If only selected details are shown, it may be lack of precision needed. • Graphics are often not conclusive • subject to interpretation • subject to the cognition of the viewer.

  10. Problems • Structures • Structures help people understand. • Structures also disorient people easily. • Usefulness • For what purposes is an application created? • For what purposes do people use the application? • How usefulness can be demonstrated? • No theories • No experimental results • No practical applications

  11. A Successful Story • Spotfire • Completed in 5 years from research prototypes to commercial products. • Focused on data presentation for data analysis • Deterministic, rather than fuzziness • Usefulness, not just pretty pictures. • Utilized simple functionalities • Not the most advanced features • Practical

  12. A Developing Story • Kohonen Mapping for Data Analysis • A banking report example • A drug treatment/development example • Marie Synnestvedt’s data

  13. Baking Cabling Messages • FINCEN (Financial Crimes Enforcement Network) receives thousands and thousands of messages each day from banks all over the world, which one deserves more attention? • Solution: • cluster messages • identify trends • Interact with the data • Samples: • Kohonen net input: 243 dimensions, 130 input vectors • Kohonen net output: 14 by 14 • Index parameters: words appear in at least four messages and no more than half of the total input.

  14. Map of the Suspicious Activity Reporting (SARS)

  15. Drug Treatment Data • WAR (Wyeth Ayerst Research) • Desired to have a visualization tool for data exploration on experimental dr • Complained about the limited exploration power of Spotfire. • Sent me a sample data for mapping • When the mapping was completed, the director was gone.

  16. Data: • 8624 cases (patients) • 120 independent variables (treatments) • Kohonen output: 20 by 20

  17. Marie’s Data • 488 cases • 12 Variables used for mapping: • SiteExtrem • SiteHead • SiteTrunk • SiteSubVol • Level2s • Level3 • Level4 • Level5 • ThickGroup1 • ThickGroup2 • ThickGroup3 • ThickGroup4

  18. SiteExtrem Level4 Thick G3 Level 5 Thick Group4 SiteSubVol Level3 SiteHead Thick Group1 SiteTrunk Thick G2

  19. Our Current Projects: AuthorLink/ConceptLink • Make it practical • Make it simple • Make it useful • The purpose of visualization is INSIGHT, not pretty pictures.

  20. Design Objective 1 • Develop visualization tools that work on real world data. • Working with data that have meaningful structures • Thesaurus • Citations • Document collections with good semantic structures • Real time mapping • Large databases, small visualization areas

  21. Design Objective 2 • Develop tools for associative mapping • Analyze co-occurrence data • Co-citation counts • Co-occurrence of terms in documents • View the invisible • Reveal "the meaning of associations" • Without visualization, “the meaning” could be hidden in the data.

  22. Design Objective 3 • Develop practical visualization interfaces for information access. • Simple and Practical • Everyone can use it without much learning • Useful • Connecting to good resources • Focus on contents • Not pretty graphics • No additional cognitive interpretations

  23. Design Objective 4 • Develop real time interaction for information visualization • “drag-and-drop” from visual mapping to search engines • “real-time” feed-back from search engines. • Mixed-initiative interaction • The search engine responses to what the user asks for. • The search engine may also conduct searches before the user asks it to to do.

  24. Design Objective 5 • Develop a flexible system architecture for system integration and future expansion. • Design in Java • Develop a middle solution that might be ported to other databases/search engines.

  25. System Architecture

  26. Concept Mapping

  27. Author Mapping

  28. Future Research • Beyond interaction • moving from interaction to cooperation and to collaboration. • Creating a culture and the environment for information visualization • user education • hardware and software improvement • Encouragement of graphical thinking

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