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
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
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)
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
• 90s to 2000s
– Information visualization
– Web and Virtual environments
• 2000s to 2010s
– Visual Analytics
– Visual/audio analytic appliances
4
The Landscape of Visualization Science
Publications from IEEE VisWeek, 2006, 2007,
2008
Special Issue:
Journal Information Visualization
Foundations and Frontiers
of Visual Analytics
6
Example SUCCESS STORY
Assessment 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
Example SUCCESS STORY
Technology Transfer to Law Enforcement
 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.
Law enforcement partners
Research partners
Commercial license
Multiple Linked Views
 Temporal, geospatial, theme, cluster, list views with
association linkages between views
9
Example SUCCESS STORY
Public 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
Example SUCCESS STORY
Graph 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
Alberta
Northern
North
California
Southern
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).
12
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
13
Visual Analytic Collaborations
Detecting the Expected -Discovering the UnexpectedTM
Stanford
U of Calif Santa Cruz
Purdue
Michigan State
Carnegie Mellon
U of Maryland
Penn State
Princeton Univ
Virginia Tech
14
SOA Development
Presentation Layer
Web Services
Web-Based
Thin-Client
Security
Layer
Mobile Client
Windows Services
Thick-Client
Application
Modeling
Layer
Data Enhancement Data Interface
Layer
Layer
Application Server
Component
Component
Component
Component
Component
Component
Component
Component
External
Data Store
Component
Standalone
Application
Internal Database
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.
16
Test and Evaluation
In 2008:
73 Entries
25 Organizations
13 Countries
17
Enduring Talent Base
 Students/interns/Faculty
 Visiting scholars
Watch and
Warn Training
Class
 Visual Analytics Taxonomy
 Visual analytics curriculum
and digital library
 Analyst internships
 IEEE VAST conference
and graduate colloquium
2006 Interns
18
IEEE VAST 2010
•
IEEE Symposium on Visual Analytics Science and Technology (VAST) 2010
• http://conferences.computer.org/vast/vast2010/
• Salt Lake City
• Oct, 2010
19
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?
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…
21
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
22
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
23