Information Interfaces Research Group
School of Interactive Computing
Georgia Institute of Technology
John Stasko
Visualization for
Information Analysis and Exploration
Sept. 17, 2008
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• Get out pencil & paper
Exercise
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– news, sports, financial, purchases, etc...
• Computers, internet and web give people
access to an incredible amount of data
– There simply is more “stuff”
• Society is more complex
Data Explosion
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– How do we avoid being overwhelmed?
– How do we harness this data in decisiondecisionmaking processes?
– How do we make sense of the data?
• Confound: How to make use of the data
Data Overload
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• Transform the data into information
(understanding, insight) thus making it
useful to people
The Challenge
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• Visualization of data helps people
understand it better
Premise of my Work
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– Much done preattentively, ie, without
thought
– Strong pattern recognition
– Parallel
– ~100 MB/s
• Highest bandwidth sense
Human Vision
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• From [Card, Mackinlay Shneiderman ‘98]
– “The use of computercomputer-supported, interactive
visual representations of data to amplify
cognition.”
• Definition
Visualization
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– Insight: discovery, decision making,
explanation, analysis, exploration, learning
• “The purpose of visualization is insight,
not pictures”
– Internalize an understanding
– Form a mental image of something
• Really is a cognitive process
• Often thought of as process of creating a
graphic or an image
Visualization
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Larkin & Simon ’87
Card, Mackinlay, Shneiderman ‘98
– Role of external world in thinking and reason
• External cognition aid
• Pattern matching
• Cognition → Perception
– Provide a frame of reference, a temporary
storage area
• Visuals help us think
Main Idea
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– Want to know what questions to ask
– Don’t have a priori questions
– Don’t know what you’re looking for
• Visualization most useful in exploratory
data analysis
– Data mining, DB queries, machine learning…
• Many other techniques for data analysis
When to Apply?
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• “A picture is worth a thousand words”
• “Seeing is believing”
• “I see what you’re saying”
Part of our Culture
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Some quick (static) examples…
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E. Tufte, Visual Display of Quant Info
NYC Weather
2220 numbers
London Subway
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www.thetube.com
True Geography
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www.kottke.org/plus/misc/images/tubegeo.gif
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Easy Walking Lines Added
rodcorp.typepad.com/photos/art_2003/tube_walklines_final_lmfaint.html
Atlanta Journal
April 30, 2000
Atlanta Flight Traffic
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InfoVis ‘07
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Reinforce my point with two examples
Questions:
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Which cereal has the most/least potassium?
Is there a relationship between potassium and fiber?
If so, are there any outliers?
Which manufacturer makes the healthiest cereals?
Fiber
Potassium
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• What if I read the data to you?
• What if you could only see one cereal’s
data at a time? (e.g. some websites)
Even Tougher?
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http://astro.swarthmore.edu/astro121/anscombe.html
• Coefficient of determination = 0.67
• Correlation coefficient = 0.82
• Residual sums of squared errors (about the regression line)
= 13.75
• Regression sums of squared errors (variance accounted for by x)
= 27.5
• Sums of squared errors (about the mean) = 110.0
• Equation of the leastleast-squared regression line is: y = 3 + 0.5x
• Mean of the y values = 7.5
• Mean of the x values = 9.0
Four Data Sets
The Data Sets
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1
10.0, 8.04
8.0, 6.95
13.0, 7.58
9.0, 8.81
11.0, 8.33
14.0, 9.96
6.0, 7.24
4.0, 4.26
12.0,10.84
7.0, 4.82
5.0, 5.68
The Values
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10.0,9.14
8.0,8.14
13.0,8.74
9.0,8.77
11.0,9.26
14.0,8.10
6.0,6.13
4.0,3.10
12.0,9.13
7.0,7.26
5.0,4.74
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3
10.0, 7.46
8.0, 6.77
13.0,12.74
9.0, 7.11
11.0, 7.81
14.0, 8.84
6.0, 6.08
4.0, 5.39
12.0, 8.15
7.0, 6.42
5.0, 5.73
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8.0, 6.58
8.0, 5.76
8.0, 7.71
8.0, 8.84
8.0, 8.47
8.0, 7.04
8.0, 5.25
19.0,12.50
8.0, 5.56
8.0, 7.91
8.0, 6.89
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• What did you put on paper?
Revisit Starting Exercise
• Visual Analytics
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• Information Visualization
Two Related Disciplines
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• Area emerged approximately 1990
– Statistics, databases, software, …
• Using interactive computer visualizations
to represent and communicate abstract
data
Information Visualization
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– Interaction is crucial
– Challenges of evaluation
– InfoVis for the Masses
• Recent research trends
Information Visualization
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• Area emerged approximately 2005
• InfoVis++
• Formal: The science of analytical
reasoning facilitated by interactive visual
interfaces
• Informal: Using visual representations to
help make decisions
Visual Analytics
• Positioning for an Enduring
Success
• Moving Research Into
Practice
• Production, Presentation, and
Dissemination
• Data Representations
and Transformations
• Science of Visual
Representations
and Interactions
• Science of Analytical
Reasoning
• Challenges
Overview of the R&D Agenda
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• Decision sciences
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• Comunications: Capture, Illustrate and present a message
• Cognitive and Perceptual Sciences
• Ontology, semantics, NLP, extraction, synthesis, …
• Knowledge representation, management and discovery
• Applied Mathematics
• Geospatial and Temporal Sciences
• Statistics, data representation and statistical graphics
Visual Analytics: Beyond InfoVis
Information
Visualization
~1990
Academic Context
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Visual
Analytics
~2005
IEEE VAST
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IEEE InfoVis
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“A motivated , continuous effort to understand
connections (which can be among people,
places, and events) in order to anticipate
their trajectories and act effectively.”
– Klein, Moon and Hoffman
Sensemaking
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• Visualization for Investigative Analysis
across Document Collections
Jigsaw
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Gennadiy Stepanov
Sarah Williams
Neel Parekh
Kanupriyah Singhal
Carsten Görg
Zhicheng Liu
Vasili Pantazopoulos
+ 4 new students
The Jigsaw Team
Pirolli & Card, ICIA ‘05
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• Analysts’ span of attention for evidence
and hypotheses
• Cost structure of scanning and selecting
items for further attention
Pain Points
Documents/
case reports
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Blogs
DBs
• Help investigative analysts discover
plans, plots and threats embedded across
the individual documents in large
document collections
Problem Addressed
Example Document
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• Thesis: A plot/threat within the
documents will involve a set of entities in
coordination
– Person, place, organization, phone number,
date, license plate, etc.
• Entities within the documents
Our Focus
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• Not our main research focus –
Collaborate with or use tools from others
– Crucial for our work
• Must identify and extract entities from
plain text documents
Entity Identification
Entities Identified
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– The more documents they appear in
together, the stronger the connection
– Two entities are connected if they appear in
a document together
• Connection definition:
• Entities relate/connect to each other to
make a larger “story”
Connections
“Putting the pieces together”
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• User actions generate events
that are transmitted to and
(possibly) reflected in other
views
• Views are highly interactive and
coordinated
• Multiple visualizations (views) of
documents, entities, & their connections
Jigsaw
System Views
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The Need for Pixels
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Demo
Console
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Document View
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List View
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Graph View
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Scatterplot View
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Calendar View
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Report Cluster View
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Timeline View
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Shoebox
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• Transitioning system to real clients
Trial Use
• Reliability/uncertainty
• Other types of data
• Themes/concepts
• Enhanced evidence
marshalling
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• Connectivity search
• Collaborative version
• Scalability issues
• Present/browse
investigation history
• Geospatial View
• Evaluation
• Deployment
• Display wall?
• Web search & situational
awareness
• Wikipedia & Intellipedia
• Entity Identification
Future Work
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• Including flexible, useful interaction is
one of the best ways to do this
– Not to just illustrate and reconfirm existing
knowledge
• Design your visualization systems and
tools to facilitate analysis and exploration
Take Away Point
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• http://www.gvu.gatech.edu/ii
To Learn More
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• Some slides in this presentation
borrowed from overviews of visual
analytics by Jim Thomas, NVAC Director
Acknowledgment
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• Supported by NSF IISIIS-0414667
• Work conducted as part of the
Southeastern Regional Visualization and
Analytics Center, supported by DHS and
NVAC
Acknowledgments
• Questions?
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• Thanks for your attention!
End