What is Visual Analytics?
Jim Thomas
AAAS Fellow, PNNL Fellow
Director National Visualization and Analytics Center
Jim Thomas 9/16/2008
What is Visual Analytics?
Third Wave: Knowledge based society
Visual analytics enables the creation of knowledge
Definitions
Motivation behind the need for science of visual analytics
What Visual Analytics is and is not: examples
Establishment of VAC partnerships from basic sciences to
deployed missions
Transition to DHS Perspectives: Video
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The Third Wave Wealth System
“Third Wave Wealth system is increasingly based on
serving, thinking, knowing and experiencing.”
transdisciplinary science
Revolutionary Wealth: Alvin and Heidi Toffler, Alfred Knopf
publisher 2006, authors of Future Shock and The Third Wave
Knowledge based economy:
"The new production of knowledge: The dynamics of science and
research in contemporary societies" By Michael Gibbons, Camille
Limoges, Helga Nowotny, Simon Schwartzman, Peter Scott, and
Martin Trow
"Re-Thinking Science: Knowledge and the Public in a Age of
Uncertainty" By Helga Nowotny, Peter Scott, and Michael
Gibbons
Data – Information – Knowledge - Wisdom
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Visual Analytics Definition
Congress: Visual analytics provides the last
12 inches between the masses of information
and the human mind to make decisions
Science: Visual analytics is the science of
analytical reasoning facilitated by interactive
visual interfaces
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History of Graphics and Visualization
• 70s to 80s
•
90s to 2000s
– CAD/CAM Manufacturing, cars, planes, and chips
– 3D, education, animation, medicine, etc.
•
80s to 90s
– Scientific visualization
– Realism, entertainment
– Information visualization
– Web and Virtual environments
•
2000s to 2010s
– Visual Analytics
– Visual/audio appliances
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Selected Societal Drivers and Observations
Scale of Things to Come:
Information:
In 2002, recorded media and electronic information flows generated
about 22 exabytes (1018) of information
In 2006, the amount of digital information created, captured, and
replicated was 161 EB
In 2010, the amount of information added annually to the digital
universe will be about 988 EB (almost 1 ZB)
A Forecast of Worldwide Information Growth Through 2010: IDC
National Open Source Enterprise - Intelligence Community Directive No.
301, July 11, 2006
UC Berkeley School of Information Management and Systems: Now
much Information
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Why Must Change
Scale of Things to Come:
Information:
Drivers of Digital Universe:
70% of the Universe is being produced by individuals
Organizations (businesses, agencies, governments, universities)
produce 30%
Wal-Mart has a database of 0.5 PB; it captures 30,000,000
transactions/day
The growth is uneven
Today the United States accounts for 41% of the Universe; by
2010, the Asia Pacific region will be growing 40% faster than any
of the other regions
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Why Must Change
Scale of Things to Come:
Information:
Drivers of Digital Universe:
Kinds of Data:
About 2 GB of digital information is being produced per person per
year
95% of the Digital Universe’s information is unstructured
25% of the digital information produced by 2010 will be images
By 2010, the number of e-mailboxes will reach 2 billion
The users will send 28 trillion e-mails/year, totaling about 6 EB of
data
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Why Must Change
Scale of Things to Come:
Information:
Drivers of Digital Universe:
Kinds of Data:
Interaction:
Today's interaction designed for point and click on individual items,
groups(folders), and lists
Today's interaction assumes user knows subject, concepts within
information spaces, and can articulate what they want
Today's interaction assumes data and interconnecting relationships
are static in meaning over time
Today's interaction is one way initiated
Today’s interaction (WIMP) designed over 30 years ago
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Observations on Complexity and Uncertainty
Disorganized Complexity almost always comes with unstructured data, 95% of
data
Organized Complexity1: one could conceivably model or simulate, such as city neighborhood as a
living mechanism
Disorganized Complexity1: seemingly random collections, unknown relationships, unknown forces
With Unstructured data comes a significant, amount of uncertainty
Uncertainty2: The lack of certainty, A state of having limited knowledge where it is impossible to
exactly describe existing state or future outcome, more than one possible outcome.
Vagueness or ambiguity are sometimes described as "second order uncertainty", where there is
uncertainty even about the definitions of uncertain states or outcomes.
Must enable and rely on human judgment
1.
2.
Weaver, Warren (1948), “ Science and Complexity”. American Scientist 36:536
Tannert C, Elvers HD, Jandrig B (2007). "The ethics of uncertainty. In the light of possible
dangers, research becomes a moral duty." EMBO Rep. 8 (10): 892-6.
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Critical Thinking*
“…the quality of our life and that of what we produce,
make, or build depends precisely on the quality of our
thoughts.”
Elements of thought:
Points of View
Implications &
Consequences
Purpose of
the Thinking
Question at Issue
Assumptions
Concepts
Information
Interpretation
And Inference
* Foundations of Critical Thinking www.criticalthinking.org
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Example Heuer’s Central Ideas
“Tools and techniques
that gear the analyst’s
mind to apply higher
levels of critical
thinking can
substantially improve
analysis… structuring
information,
challenging
assumptions, and
exploring alternative
interpretations.”
Examples Demonstrating Need
Towards Predictive Analytics - discovery of
the unexpected through Hypothesis/Scenariobased Analytics (hypothesis testing – IN-SPIRE)
Human Information Discourse
Japan
Trade
Protection
Protection Measures
Measures
Japan
Trade
Protection
Measures
Trade
Protection
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Examples Demonstrating Need
Changing Nature of Information Structure: Temporal, dynamically changing relationships,
determination of intent (DC Sniper & ThemeRiver)
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Examples Demonstrating Need
Information synthesis while preserving security and privacy
Data signatures that are semantic and scale
Video
Images
Firm 5Firm 6
Firm 4
Firm 7
Firm 3
Firm 8
Firm 2
Country A Firm 9
Firm 1
Firm 10
A Bank
Financial
Audio
Discover what is there AND discover what isn’t there
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Visual analytics requires rapid data ingest
into analytical process
All source, all types, little standards, gathered with
unknown quality
What’s
in
here?
analyst
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Visual analytics requires mathematical and
semantic representations and transformations
of data
Transations
Cyber
Power grid
Financial
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Into scalable
analytical reasoning
framework
Visual analytics is the discovery of
relationships in data; plus finding the dots
High dimensional fuzzy, likely incomplete relationships
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Visual analytics is the discovery of
relationships at different scales within
changing temporal conditions
High dimensional fuzzy, likely incomplete relationships
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Visual analytics often requires the syntheses
of data sources, types, etc.
Biological Activity Space
Chemical Structure Space
Relating Chemical Attributes
with Biological Activity
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Chemical Structure Viewer
Visual Analytics is about reasoning, hypothesis
creation and validation, evidence marshalling,
uncertainty refinement
STAB
RESIN
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Visual Analytics is the bridge between
theory, experiment, and the human mind for
discovery in science (predictive science)
Environment
Energy
Health
Economics
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Visual Analytics is about mapping the abstract
and the physical together; e.g. geospatial
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Visual Analytics is about assessible analytic
tools from mobile, desktop, command center
back to cell phone: walkup usable
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Context-sensitive
Interactive
Dynamic
Auditable
Composition support
Visual Analytics is about visual
communication, the message, the story, etc
Visual Analytics is about Analytic Methods
and Verification
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