1 / 23

Jim Thomas Founding Director, Science Advisor | National Visualization and Analytics Center AAAS, PNNL Fellow Pacific N

Welcome FODAVA Teams Visual Analytics Update December 3, 2009. Jim Thomas Founding Director, Science Advisor | National Visualization and Analytics Center AAAS, PNNL Fellow Pacific Northwest National Laboratory Jim.Thomas@pnl.gov | http://nvac.pnl.gov.

thad
Télécharger la présentation

Jim Thomas Founding Director, Science Advisor | National Visualization and Analytics Center AAAS, PNNL Fellow Pacific N

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Welcome FODAVA Teams Visual Analytics Update December 3, 2009 Jim Thomas Founding Director, Science Advisor | National Visualization and Analytics Center AAAS, PNNL Fellow Pacific Northwest National Laboratory Jim.Thomas@pnl.gov | http://nvac.pnl.gov

  2. 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 2

  3. “The beginning of knowledge is the discovery of something we do not understand.” ~Frank Herbert (1920 - 1986) 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.

  4. 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 analytic appliances

  5. The Landscape of Visualization Science Publications from IEEE VisWeek, 2006, 2007, 2008

  6. Special Issue: Journal Information Visualization Foundations and Frontiers of Visual Analytics

  7. Example SUCCESS STORYAssessment Wall • 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

  8. Example SUCCESS STORYTechnology Transfer to Law Enforcement Law enforcement partners Research partners Commercial license • 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.

  9. Multiple Linked Views • Temporal, geospatial, theme, cluster, list views with association linkages between views

  10. Example SUCCESS STORYPublic and Animal Health • 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

  11. Example SUCCESS STORYGraph Analytics for US Power Grid • 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

  12. 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).

  13. 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

  14. Visual Analytic Collaborations Detecting the Expected -- Discovering the UnexpectedTM Carnegie Mellon Virginia Tech Penn State Michigan State Purdue Stanford U of Maryland U of Calif Santa Cruz Princeton Univ

  15. SOA Development Presentation Layer Security Layer Modeling Layer Data Enhancement Layer Data Interface Layer Application Server Web-Based Thin-Client Web Services Component Component Component Component Mobile Client Windows Services Thick-Client Application Component Component Component Component External Data Store Component Standalone Application Internal Database

  16. Test and Evaluation • 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. • Current efforts: • Threat Stream Generator • Evaluation methods and metrics • Requirements handbooks for user communities • Law enforcement • First responders

  17. Test and Evaluation • In 2008: • 73 Entries • 25 Organizations • 13 Countries

  18. Enduring Talent Base Watch andWarn TrainingClass 2006 Interns Students/interns/Faculty Visiting scholars Visual Analytics Taxonomy Visual analytics curriculumand digital library Analyst internships IEEE VAST conferenceand graduate colloquium

  19. IEEE VAST 2010 • IEEE Symposium on Visual Analytics Science and Technology (VAST) 2010 • http://conferences.computer.org/vast/vast2010/ • Salt Lake City • Oct, 2010

  20. 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?

  21. 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…

  22. 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

  23. 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 23

More Related