Jim Thomas Founding Director, Science Advisor | National Visualization and Analytics Center AAAS, PNNL Fellow Pacific Northwest National Laboratory | Welcome FODAVA Teams Visual Analytics Update December 3, 2009

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

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 ( )

4 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

6 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 STORY Assessment 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 STORY Technology 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 9

 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 STORY Public 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 STORY Graph Analytics for US Power Grid

Systems Considered:  IN-SPIRE  JIGSAW - John Stasko, Carsten Görg, and Zhicheng Liu, “Jigsaw: Supporting Investigative Analysis through Interactive Visualization,” Information Visualization, vol. 7, no. 2, pp , Palgrave Magellan,  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)  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):  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). 12

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

Visual Analytic Collaborations Detecting the Expected -- Discovering the Unexpected TM 14 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 External Data Store Component Web-Based Thin-Client Thick-Client Application Standalone Application Web Services Windows Services Internal Database Component Security Layer 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. 16 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 Entries 25 Organizations 13 Countries 17

18 Enduring Talent Base  Students/interns/Faculty  Visiting scholars  Visual Analytics Taxonomy  Visual analytics curriculum and digital library  Analyst internships  IEEE VAST conference and graduate colloquium Watch and Warn Training Class 2006 Interns

19 IEEE VAST 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST) 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?

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