What is Visual Analytics?

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

2. 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 2

3. 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 3

4. 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

5. History of Graphics and Visualization • 70s to 80s • CAD/CAM Manufacturing, cars, planes, and chips • 3D, education, animation, medicine, etc. • 90s to 2000s • Information visualization • Web and Virtual environments • 80s to 90s • Scientific visualization • Realism, entertainment • 2000s to 2010s • Visual Analytics • Visual/audio appliances

6. 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

7. 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

8. 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

9. 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

10. 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 • 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.

11. 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

12. 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.”

13. JapanProtectionMeasures TradeProtectionMeasures Japan Trade Protection Measures Trade Protection Examples Demonstrating Need • Towards Predictive Analytics - discovery of the unexpected through Hypothesis/Scenario-based Analytics (hypothesis testing – IN-SPIRE) • Human Information Discourse

14. Examples Demonstrating Need • Changing Nature of Information Structure: Temporal, dynamically changing relationships, determination of intent (DC Sniper & ThemeRiver)

15. Firm 5 Firm 6 Firm 4 Firm 7 Firm 3 Country A Firm 8 Firm 2 Firm 9 Firm 1 Firm 10 A Bank Examples Demonstrating Need Video • Information synthesis while preserving security and privacy • Data signatures that are semantic and scale Images Financial Audio Discover what is there AND discover what isn’t there

16. Visual analytics requires rapid data ingest into analytical process • All source, all types, little standards, gathered with unknown quality What’s in here? analyst 16 16

17. Visual analytics requires mathematical and semantic representations and transformations of data Into scalable analytical reasoning framework Transations Cyber Power grid Financial 17

18. Visual analytics is the discovery of relationships in data; plus finding the dots • High dimensional fuzzy, likely incomplete relationships 18

19. Visual analytics is the discovery of relationships at different scales within changing temporal conditions • High dimensional fuzzy, likely incomplete relationships 19

20. Biological Activity Space Chemical Structure Space Relating Chemical Attributes with Biological Activity Chemical Structure Viewer Visual analytics often requires the syntheses of data sources, types, etc. 20

21. Visual Analytics is about reasoning, hypothesis creation and validation, evidence marshalling, uncertainty refinement STAB RESIN 21

22. Visual Analytics is the bridge between theory, experiment, and the human mind for discovery in science (predictive science) Energy Environment Health Economics 22

23. Visual Analytics is about mapping the abstract and the physical together; e.g. geospatial 23

24. Visual Analytics is about assessible analytic tools from mobile, desktop, command center back to cell phone: walkup usable 24 24

25. Context-sensitive Interactive Composition support Dynamic Auditable Visual Analytics is about visual communication, the message, the story, etc Visual Analytics is about Analytic Methods and Verification 25