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Foundations of Visual Analytics Pat Hanrahan Director, RVAC Stanford University

Foundations of Visual Analytics Pat Hanrahan Director, RVAC Stanford University. Analytical Reasoning Facilitated by Interactive Visualization. Why is a Picture (Sometimes) Worth 10,000 Words. Let’s Solve a Problem: Number Scrabble Herb Simon. Number Scrabble.

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Foundations of Visual Analytics Pat Hanrahan Director, RVAC Stanford University

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  1. Foundations of Visual AnalyticsPat HanrahanDirector, RVACStanford University

  2. Analytical ReasoningFacilitated byInteractive Visualization

  3. Why is a Picture (Sometimes) Worth 10,000 Words

  4. Let’s Solve a Problem:Number ScrabbleHerb Simon

  5. Number Scrabble • Goal: Pick three numbers that sum to 15

  6. Number Scrabble • Goal: Pick three numbers that sum to 15 • A: • B:

  7. Number Scrabble • Goal: Pick three numbers that sum to 15 • A: • B:

  8. Number Scrabble • Goal: Pick three numbers that sum to 15 • A: • B:

  9. Number Scrabble • Goal: Pick three numbers that sum to 15 • A: • B:

  10. Number Scrabble • Goal: Pick three numbers that sum to 15 • A: • B:

  11. Number Scrabble • Goal: Pick three numbers that sum to 15 • A: • B: ?

  12. Tic-Tac-Toe

  13. Tic-Tac-Toe X

  14. Tic-Tac-Toe X O

  15. Tic-Tac-Toe X X O

  16. Tic-Tac-Toe X O X O

  17. Tic-Tac-Toe X O X X O

  18. Tic-Tac-Toe X O X X O O

  19. Problem Isomorph 4 3 8 9 5 1 2 7 6 Magic Square: All rows, columns, diagonals sum to 15

  20. Switching to a Visual Representation 4 3 8 9 5 1 2 7 6

  21. Switching to a Visual Representation 4 3 8 9 5 1 2 7 6

  22. Switching to a Visual Representation 4 3 8 9 5 1 2 7 6

  23. Switching to a Visual Representation 4 3 8 9 5 1 2 7 6

  24. Switching to a Visual Representation 4 3 8 9 5 1 2 7 6 ?

  25. Switching to a Visual Representation 4 3 8 9 5 1 2 7 6

  26. Why is a Picture Worth 10,000 Words? • Reduce search time • Pre-attentive (constant-time) search process • Spatially-indexed patterns store the “facts” • Reduce memory load • Working memory is limited • Store information in the diagram • Allow perceptual inference • Map inference to pattern finding • Larkin and Simon, Why is a diagram (sometimes) worth 10,000 • words, Cognitive Science, 1987

  27. The Value of Visualization • It is possible to improve human performance by 100:1 • Faster solution • Fewer errors • Better comprehension • The best representation depends on the problem

  28. Number RepresentationsNorman and Zhang

  29. Number Representations • Counting – Tallying • Adding – Roman numerals • Multiplication – Arabic number systems XXIII + XII = XXXIIIII = XXXV

  30. Zhang and Norman, The Representations of Numbers, Cognition, 57, 271-295, 1996

  31. Distributed Cognition External (E) vs. Internal (I) process • Separate power & base I E • Get base value E I • Multiply base values I I • Get power values I E • Add power values I E • Combine base & power I E • Add results I E Roman Arabic Arabic more efficient than Roman

  32. Long-Hand Multiplication 34 x 72 68 238 2448 From “Introduction to Information Visualization,” Card, Schneiderman, Mackinlay

  33. Power of Representations • The representational effect • Different representations have different cost-structures / ”running” times • Distributed cognition • Internal representations (mental models) • External representations (cognitive artifacts) • Representations 101 • Representations are not the real thing • Manipulate symbols to perform useful work

  34. Modeling and Simulation • Simulation for computer graphics is sophisticated • Diversity of phenomenon • Complexity of the environment • Robustness • Range of models: fast to accurate • Lots of breakthroughs: one small example is GPUs which may become the major platform for scientific computation

  35. Mathematics of Visual Analysis • MSRI, Berkeley, CA, Oct 16-17, 2006 • Organizers: P. Hanrahan, W. Cleveland, S. Harabagliu, P. Jones, L. Wilkinson • Participants: J. Arvo, A. Braverman, J. Byrnes, E. Candes, D. Carr, S. Chan, N. Chinchor, N. Coehlo, V. de Silva, L. Edlefsen, R. Gentleman, G. Lebanon, J. Lewis, J. Mackinlay, M. Mahoney, R. May, N. Meinshausen, F. Meyer, M. Muthukrishnan, D. Nolan, J-M. Pomarede, C. Posse, E. Purdom, D. Purdy, L. Rosenblum, N. Saito, M. Sips, D. W. Temple Lang, J. Thomas, D. Vainsencher, A. Vasilescu, S. Venkatasubramanian, Y. Wang, C. Wickham, R. Wong Kew

  36. Supporting Interaction • Panelists: William Cleveland, Robert Gentleman, Muthu Muthukrishnan, Suresh Venkatasubramanian, Emmanuel Candez • Fast algorithms: streaming and approximate algorithms, compressed sensing, randomized numerical linear algebra, … • Fast systems: map-reduce, column stores, beyond R, …

  37. Finding Patterns • Panelists: Peter Jones, Vin de Silva, Francois Meyer, Naoki Saito, Michael Mahoney • How to represent patterns? • Data/dimensional reduction vs. transformation to meaningful form? • Are humans required to build good models? How is domain knowledge added? • When are computers good pattern finders? When are people good pattern finders?

  38. Computation Steeringvs.Interactive Simulation

  39. Integrating Heterogenous Data • Panelists: Sanda Harabagliu, John Byrnes, Jean-Michel Pomeranz, Christian Posse, Guy Lebanon • Many important datatypes: text and language, audio, video, image, sensors, logs, transactions, nD relations, … • How to fuse into common semantic representation? • Beyond the desktop to new representations of information spaces: vispedia, jigsaw, …

  40. Smart Visual Analysis • Panelists: Leland Wilkinson, Jock Mackinlay, Jim Arvo, Amy Braverman, Dan Carr • Automatic graphical presentation and summarization; guided analysis • How do people reason about uncertainty?

  41. Summary • Visual analytics merges • Cognitive psychology • Mathematics and computation (algm, stat, nlp) • Interactive visualization techniques • Need to rethink how these capabilities are combined

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