Jim ThomasFounding Director, Science Advisor | National
Visualization and Analytics CenterAAAS, PNNL Fellow
Pacific Northwest National [email protected] | http://nvac.pnl.gov
Welcome
FODAVA Teams
Visual Analytics Update
December 3, 2009
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Changing Landscape for Knowledge Workers and Analytics
• Starting Visual Analytics Definition• IVS Journal Suite• Success Stories are Critical• Characteristics of Deployed VA Technologies• International Collaboration• Foundational Support: architecture and test
data sets• My Challenge for You
Visual Analytics Definition
Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces.
People use visual analytics tools and techniques to Synthesize information and derive insight from massive, dynamic,
ambiguous, and often conflicting data Detect the expected and discover the unexpected Provide timely, defensible, and understandable assessments Communicate assessment effectively for action.
“The beginning of knowledge is the discovery of something we do not understand.” ~Frank Herbert (1920 - 1986)
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History of Graphics and Visualization
• 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 analytic appliances
• 90s to 2000s– Information visualization– Web and Virtual environments
The Landscape of Visualization Science
Publications from IEEE VisWeek, 2006, 2007, 2008
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Special Issue: Journal Information Visualization
Foundations and Frontiers of Visual Analytics
Large-screen collaborative touch screen for “walk-up” analysis of streaming data for national/regional situation assessment.• Builds on IN-SPIRE document analysis framework
• Supports collaborative exploration
• Examples Deployments:– DHS S&T
– DHS ICE
– NASIC
– Intelligence Community
Example SUCCESS STORYAssessment Wall
Law Enforcement Information Framework (LEIF)• “Lightweight analytics” brings power of visual discovery to investigators and
emergency responders.
• Deployments– ARJIS: Enabling analysis of incident and suspicious activity reports for 75 member
agencies.
– Seattle PD / ARJIS: Providing situational awareness and real time information sharing for mobile users.
– NY/NJ Port Authority: Next generation statistical and modus operandi analysis for police commanders.
Example SUCCESS STORYTechnology Transfer to Law Enforcement
Commercial license
Law enforcement partners
Research partners
Multiple Linked Views
Temporal, geospatial, theme, cluster, list views with association linkages between views
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Visual environments for disease surveillance and early detection of public health outbreaks.• Supports public health personnel in simulating pandemic outbreaks and
planning response.
• PanViz tool allows officials to track the spread of influenza across the state of Indiana and implement various decision measures at any time during the pandemic.
• Deployments:– Indiana Department of Health
– Georgia DPH
Example SUCCESS STORYPublic and Animal Health
Visual environment for critical infrastructure protection and risk assessment. • Power grid health monitoring, discovery of weaknesses in grid.
• Supports interactive exploration of large graphs through multiple linked views.
• Deployments:– PNNL Energy Infrastructure Operations Center
– Bonneville Power Administration
– PJM Interconnection
– DHS
– Intelligence Community
Example SUCCESS STORYGraph Analytics for US Power Grid
Alberta
North California
Southern
Northern
Systems Considered: IN-SPIRE - http://in-spire.pnl.gov.
JIGSAW - John Stasko, Carsten Görg, and Zhicheng Liu, “Jigsaw: Supporting Investigative Analysis through Interactive Visualization,” Information Visualization, vol. 7, no. 2, pp. 118-132, Palgrave Magellan,
2008.
WIREVIZ - Remco Chang, Mohammad Ghoniem, Robert Korsara, William Ribarsky, Jing Yang, Evan Suma, Carolina Ziemkiewicz, Daniel Keim, Agus Sudjianto, IEEE Visual Analytics Science and Technology
(VAST) 2007. GreenGrid - Pak Chung Wong, Kevin Schneider, Patrick Mackey, Harlan Foote, George Chin Jr., Ross
Guttromson, Jim Thomas “A Novel Visualization Technique for Electric Power Grid Analytics,” IEEE Transactions on Visualization and Computer Graphics 15(3):410-423.
Scalable Reasoning System - Pike WA, JR Bruce, RL Baddeley, DM Best, L Franklin, RA May, II, DM Rice, RM Riensche, and K Younkin. (2008) "The Scalable Reasoning System: Lightweight Visualization for Distributed Analytics." In IEEE Symposium on Visual Analytics Science and Technology (VAST).
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Example Visual Analytics Characteristics
Whole-part relationship: multiple levels of information extraction
Relationship discovery: high dimensional analytics to detect the expected and discover the unexpected
Combined exploratory and confirmatory analytics
Selection, search (bool. and similarity) and groupings
Temporal and geospatial analytics
Extensive labeling: everything active on screen
Multiple linked views
Analytic interactions are foundational to critical thinking
Analytic reasoning framework
Capture analytic snippets for reporting
Both general and application specific applications
Visual Analytic Collaborations
Detecting the Expected -- Discovering the UnexpectedTM
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Virginia Tech
Penn State
Michigan State
Purdue
Stanford
Carnegie Mellon
U of Maryland
U of Calif Santa Cruz
Princeton Univ
Application Server
SOA Development
Data Interface Layer
Modeling Layer
Data Enhancement Layer
Presentation Layer
Component
Component
Component
External Data Store
Component
Component
Component
Component
Web-BasedThin-Client
Thick-ClientApplication
StandaloneApplication
Web S
ervicesW
indows S
ervices
Internal Database
Component
Component
SecurityLayer
Mobile Client
Goal: Develop new methods for assessing the utility of analytic technology.
Impact: Novel synthetic data sets provide “apples to apples” testing platform for visual analytics tools and spur development of new technology.
Applications: VAST Challenges, internal & external testing.
Users: Hundreds.
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Test and Evaluation
Current efforts:
• Threat Stream Generator
• Evaluation methods and metrics
• Requirements handbooks for user communities
• Law enforcement
• First responders
Current efforts:
• Threat Stream Generator
• Evaluation methods and metrics
• Requirements handbooks for user communities
• Law enforcement
• First responders
Test and Evaluation
In 2008: 73 Entries25 Organizations13 Countries
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Enduring Talent Base
Students/interns/Faculty
Visiting scholars
Visual Analytics Taxonomy
Visual analytics curriculumand digital library
Analyst internships
IEEE VAST conferenceand graduate colloquium
Watch andWarn Training
Class
2006 Interns
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IEEE VAST 2010• IEEE Symposium on Visual Analytics Science and Technology (VAST) 2010
• http://conferences.computer.org/vast/vast2010/• Salt Lake City• Oct, 2010
My Challenge for you
New science needs to support analytic interaction and reasoning
Consider: How will your new science aid the human mind to reason better within complex information spaces?
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Conclusions
Visual Analytics is an opportunity worth considering
Practice of Interdisciplinary Science is required
Broadly applies to many aspects of society
For each of you:
The best is yet to come…
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Top Ten Challenges Within Visual Analytics
Human Information Discourse for Discovery—new interaction paradigm based around cognitive aspects of critical thinking
New visual paradigms that deal with scale, multi-type, dynamic streaming temporal data flows
Data, Information and Knowledge Representation and synthesis
Synthesis and turning information into knowledge
Collaborative Predictive/Proactive Visual Analytics
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Top Ten Challenges Within Visual Analytics
Visual Analytic Method Capture and Reuse
Dissemination and Communication
Visual Temporal Analytics
Delivering short-term products while keeping the long view
Interoperability interfaces and standards: multiple VAC suites of tools
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