Jim ThomasFounding Director, Science Advisor | National

Visualization and Analytics CenterAAAS, PNNL Fellow

Pacific Northwest National LaboratoryJim.Thomas@pnl.gov | 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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