What is Visual Analytics?

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What is Visual Analytics?
Jim Thomas AAAS Fellow, PNNL Fellow Director
National Visualization and Analytics Center
Jim Thomas 9/16/2008
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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

• 90s to 2000s
• Information visualization
• Web and Virtual environments

• 70s to 80s
• CAD/CAM Manufacturing, cars, planes, and chips
• 3D, education, animation, medicine, etc.

• 80s to 90s
• Scientific visualization
• Realism, entertainment

• 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 Universes 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
• Todays 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. Weaver, Warren (1948), Science and Complexity.
   American Scientist 36536
2. 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.

Purpose of the Thinking
Elements of thought
Points of View
Implications Consequences
Question at Issue
Assumptions
Information
Interpretation And Inference
Concepts
Foundations of Critical Thinking
www.criticalthinking.org
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Example Heuers Central Ideas

• Tools and techniques that gear the analysts
  mind to apply higher levels of critical thinking
  can substantially improve analysis structuring
  information, challenging assumptions, and
  exploring alternative interpretations.

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Examples Demonstrating Need

• Towards Predictive Analytics - discovery of the
  unexpected through Hypothesis/Scenario-based
  Analytics (hypothesis testing IN-SPIRE)
• Human Information Discourse

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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
Financial
Audio
Discover what is there AND discover what isnt
there
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Visual analytics requires rapid data ingest into
analytical process

• All source, all types, little standards, gathered
  with unknown quality

Whats in here?
analyst
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Visual analytics requires mathematical and
semantic representations and transformations of
data
Into scalable analytical reasoning framework
Transations Cyber Power grid
Financial
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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.
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Visual Analytics is about reasoning, hypothesis
creation and validation, evidence marshalling,
uncertainty refinement
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Visual Analytics is the bridge between theory,
experiment, and the human mind for discovery in
science (predictive science)
Energy
Environment
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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Visual Analytics is about visual communication,
the message, the story, etc