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Visual Analytics Techniques that Enable Knowledge Discovery:

Visual Analytics Techniques that Enable Knowledge Discovery: Detect the Expected and Discover the Unexpected. ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery V A K D '09 Paris, France. Jim J. Thomas Director, National Visualization and Analytics Center

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Visual Analytics Techniques that Enable Knowledge Discovery:

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  1. Visual Analytics Techniques that Enable Knowledge Discovery: Detect the Expected and Discover the Unexpected ACM SIGKDD Workshop on VisualAnalytics and KnowledgeDiscovery VAKD '09 Paris, France Jim J. Thomas Director, National Visualization and Analytics Center AAAS Fellow, Pacific Northwest National Laboratory Fellow http://NVAC.pnl.gov Jim.thomas@pnl.gov

  2. Visual Analytics Techniques that Enable Knowledge Discovery Introduction: what is and is not visual analytics? Landscape of visualization science Discussion of selected existing systems and technologies Common characteristics enabling knowledge discovery Top ten challenges

  3. Introduction:History of Graphics and Visualization • 90s to 2000s • Information visualization • Web and Virtual environments • 80s to 90s • Scientific visualization • Realism, entertainment • 2000s to 2010s • Visual Analytics • Visual/audio analytic appliances • 70s to 80s • CAD/CAM Manufacturing, cars, planes, and chips • 3D, education, animation, medicine, etc.

  4. Visual Analytic Collaborations Detecting the Expected -- Discovering the UnexpectedTM

  5. “The beginning of knowledge is the discovery of something we do not understand.” ~Frank Herbert (1920 - 1986) Visual Analytics Definition Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. People use visual analytics tools and techniques to • Synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data • Detect the expected and discover the unexpected • Provide timely, defensible, and understandable assessments • Communicate assessment effectively for action.

  6. What is not visual analytics? Large graph structure with no labels Heat map with no labels Search and retrieval systems Chart with no interaction Image with no semantic interpretation Stand alone image that does not tell a story

  7. The Landscape of Visualization Science Publications from IEEE VisWeek, 2006, 2007, 2008 using IN-SPIRE Visual Analytics Tool Each dot is an published science article, full text

  8. Systems Considered: IN-SPIRE -http://in-spire.pnl.gov. JIGSAW - John Stasko, Carsten Görg, and Zhicheng Liu, “Jigsaw: Supporting Investigative Analysis through Interactive Visualization,” Information Visualization, vol. 7, no. 2, pp. 118-132, Palgrave Magellan, 2008. WIREVIZ - Remco Chang, Mohammad Ghoniem, Robert Korsara, William Ribarsky, Jing Yang, Evan Suma, Carolina Ziemkiewicz, Daniel Keim, Agus Sudjianto, IEEE Visual Analytics Science and Technology (VAST) 2007. GreenGrid - Pak Chung Wong, Kevin Schneider, Patrick Mackey, Harlan Foote, George Chin Jr., Ross Guttromson, Jim Thomas “A Novel Visualization Technique for Electric Power Grid Analytics,” IEEE Transactions on Visualization and Computer Graphics 15(3):410-423. Scalable Reasoning System - Pike WA, JR Bruce, RL Baddeley, DM Best, L Franklin, RA May, II, DM Rice, RM Riensche, and K Younkin. (2008) "The Scalable Reasoning System: Lightweight Visualization for Distributed Analytics."  In IEEE Symposium on Visual Analytics Science and Technology (VAST).

  9. Whole - Part Relationship Scale independent representations, whole and parts at same time at multiple levels of abstraction, often linked

  10. Whole - Part Relationship

  11. Relationship Discovery Explore high dimensional relationships, theme groupings, outlier detection, searching by proximity at multiple scales

  12. Relationship Discovery Boolean By Example

  13. Combined Exploratory and Confirmatory Analytics Develop and refine hypothesis Evidence collection, management, and matching to hypothesis Tailor views/displays for thematic/hypothesis focus of interest Often suggestive of predictions enabling proactive thinking

  14. Multiple Data Types Supports multiple data types: structured/unstructured text Imagery/video, cyber Systems of either data type or application specific

  15. Temporal Views and Interactions Most analytics situations involve time, pace, velocity Group segments of thoughts by time Compare time segments Often combined with geospatial

  16. Reasoning Workspace • Workspace to construct logic and illustrate reasoning • Flexible spatial view of reasoning: stories Stu Card, PARC

  17. Grouping and Outlier Detection Form groups of thought/data Labels and annotation Compare groupings Find small groups or outliers 17

  18. Labeling Critically important, Dynamic in scope, number labels, size, color Positioning Almost everything has labels Labels tell semantic meaning

  19. Multiple Linked Views Temporal, geospatial, theme, cluster, list views with association linkages between views

  20. Multiple Linked Views Search by Example (Find Similar Accounts) Heatmap View (Accounts to Keywords Relationship) Keyword Network (Keyword Relationships) Strings and Beads (Relationships over Time) Temporal, geospatial, theme, cluster, list views with association linkages between views

  21. WireViz Video

  22. Reporting Capture display segments in graph modes for putting in reports, PPT etc Capture reasoning segments of analytic results Capture animations

  23. Engaging Interaction GreenGrid video

  24. GreenGrid Video

  25. Tested With Known Data and Solutions

  26. Top Ten Challenges Within Visual Analytics • Human Information Discourse for Discovery—new interaction paradigm based around cognitive aspects of critical thinking • New visual paradigms that deal with scale, multi-type, dynamic streaming temporal data flows • Data, Information and Knowledge Representation • Collaborative Predictive/Proactive Visual Analytics • Visual Analytic Method Capture and Reuse

  27. Top Ten Challenges Within Visual Analytics • Dissemination and Communication • Visual Temporal Analytics • Validation/verification with test datasets openly available • Delivering short-term products while keeping the long view • Interoperability interfaces and standards: multiple VAC suites of tools 27

  28. Conclusions The best is yet to come… • Visual Analytics is an opportunity worth considering • Practice of Interdisciplinary Science is required • Broadly applies to many aspects of society • For each of you:

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