Foundations of Visual Analytics Pat Hanrahan Director, RVAC Stanford University.
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Transcript of 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
Goal: Pick three numbers that sum to 15
Number Scrabble
Goal: Pick three numbers that sum to 15
A:
B:
Number Scrabble
Goal: Pick three numbers that sum to 15
A:
B:
Number Scrabble
Goal: Pick three numbers that sum to 15
A:
B:
Number Scrabble
Goal: Pick three numbers that sum to 15
A:
B:
Number Scrabble
Goal: Pick three numbers that sum to 15
A:
B:
Number Scrabble
Goal: Pick three numbers that sum to 15
A:
B: ?
Tic-Tac-Toe
Tic-Tac-Toe
X
Tic-Tac-Toe
X
O
Tic-Tac-Toe
X
O
X
Tic-Tac-Toe
X
O
X O
Tic-Tac-Toe
X
O
X O
X
Tic-Tac-Toe
X
O
X O
X
O
Problem Isomorph
34 8
59 1
72 6
Magic Square: All rows, columns, diagonals sum to 15
Switching to a Visual Representation
8
59 1
72 6
34
Switching to a Visual Representation
8
59 1
72 6
34
Switching to a Visual Representation
34 8
59 1
72 6
Switching to a Visual Representation
34 8
59 1
72 6
Switching to a Visual Representation
34 8
59 1
72 6
?
Switching to a Visual Representation
34 8
59 1
72 6
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
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
Number Representations
Norman and Zhang
Number Representations
Counting – Tallying
Adding – Roman numerals
Multiplication – Arabic number systems
XXIII + XII = XXXIIIII = XXXV
Zhang and Norman, The Representations of Numbers,
Cognition, 57, 271-295, 1996
Distributed Cognition
1. Separate power & base I E
2. Get base value E I
3. Multiply base values I I
4. Get power values I E
5. Add power values I E
6. Combine base & power I E
7. Add results I E
Roman Arabic
Arabic more efficient than Roman
External (E) vs. Internal (I) process
Long-Hand Multiplication
34x 72
68238
2448
From “Introduction to Information Visualization,”
Card, Schneiderman, Mackinlay
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
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
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
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, …
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?
Computation Steeringvs.
Interactive Simulation
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, …
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?
Summary
Visual analytics merges
Cognitive psychology
Mathematics and computation (algm, stat, nlp)
Interactive visualization techniques
Need to rethink how these capabilities are combined
