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Visualization Schemas for Flexible Information Visualization

Visualization Schemas for Flexible Information Visualization. Chris North, Nathan Conklin, Varun Saini Proceedings of IEEE Symposium on InforVis’02 Presented by Mei Huang, Chunyuan Liao Apr. 21,2005. Outline. Relational Data Schema Motivation Related Work Snap-Together Datacompass

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Visualization Schemas for Flexible Information Visualization

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  1. Visualization Schemas for Flexible Information Visualization Chris North, Nathan Conklin, Varun Saini Proceedings of IEEE Symposium on InforVis’02 Presented by Mei Huang, Chunyuan Liao Apr. 21,2005

  2. Outline • Relational Data Schema • Motivation • Related Work • Snap-Together • Datacompass • Summary & Remarks

  3. Relational Data Schema • Structural description of data sets • Primitives: attributes, tuples and relations

  4. Motivation • Relational data schema enables flexible database design • No corresponding flexible ways to construct effective UI and visualization -- unique data schema  unique visualization/coordination -- database keeps changing -- different views for same data

  5. Mismatch in design capabilities

  6. Related Work • Single relation visualization • APT • Sage/SageBrush • DEVise • Multiple relation visualization • Visage • DataSplash/Tioga-2 • Rivet/Polaris • Sieve

  7. DEVise http://www.cs.wisc.edu/~devise/devise/quick_intro/index.html

  8. Visage www-2.cs.cmu.edu/~sage/visage.html

  9. DataSplash/Tioga-2 http://datasplash.cs.berkeley.edu/tour_quick.html

  10. Polaris http://graphics.stanford.edu/projects/polaris/

  11. Snap-Together -- Overview A strong analogy between relational database concepts and Snap visualization concepts enables a matching level of design capability. Demo

  12. Snap-Together -- Theory(1) • Snap Visualization Model • Multiple views/Components • Schema primitives • Data-centric coordination and Joins

  13. Snap-Together -- Theory(2) • Self Join eg: TreeView [URLs]  Table view • Single Join eg: TreeView  [URLs]  [HitCounts] Scatterplot • Compound Join eg: TreeView [URLs] [Hits]  [Referrers] TableView • Multiple alternative join eg: TreeView [URLs] [Hits]  [Referrers] TableView TreeView [URLs] [Links]  [Referrers] TableView url_id url_id refer_id url_id refer_id refer_id url_id

  14. Snap-Together -- User Interface • Visualization Schemas -- represented as a graph and support direct manipulation • Nodes -- represent instantiated visualization components • Edges -- represent coordinations between visualizations

  15. Snap-Together -- System Architecture Coordination Manager Visualization Schema Coordination Graph Database Manager Database Schema Relational Database Relational Database

  16. Snap server • Event-based coordination • Send -> Translate -> Receive -> selection/navigate • Extendable architecture

  17. DataCompass • For novice users or very complex database schemas • One-step construction • Interchangeable with visualization schema • Bottom-up approach vs. Top down approach

  18. Summary -- Snap’s three perspectives • Theory: multi-view visualization, coordinating between data design and visualization design • UI: diagrammatic UI to enable rapid customization of visualization without programming • System Architecture: web-based component architecture to support run-time integration of diverse data sources and visualization tools, and dissemination of custom visualization as web pages.

  19. Remarks • Merits: • A cohesive and extensible architecture for coordinating visualization components • Flexible and easy user interface, no programming needed • Shortcoming: • No support for visual query • No integration between query and visualization schema • Limited support for coordinated data navigation ( pan, zoom … )

  20. Thanks!

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