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Visual Analytics: A Global Collaboration

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Title: Visual Analytics: A Global Collaboration


1
Visual AnalyticsA Global Collaboration
James (Jim) J. Thomas Director DHS National
Visualization and Analytics Center AAAS Fellow,
PNNL Fellow http//nvac.pnl.gov, 509-375-2210
Joseph Kielman Director Science Futures,
Department of Homeland Security Director
Visualization Programs Joseph.Kielman_at_DHS.GOV,
202-254-5787
2
Why We Should Care About Visual Analytics -
Three Trends
  • Digitization
  • Mining and Analysis
  • Simulation
  • Super Crunchers Ian Ayres
  • The World Without Us Alan Weisman

3
Selected Societal Drivers and Observations
  • Scale of Things to Come
  • Information
  • In 2002, recorded media and electronic
    information flows generated about 22 exabytes
    (1018) of information
  • In 2006, the amount of digital information
    created, captured, and replicated was 161 EB
  • In 2010, the amount of information added annually
    to the digital universe will be about 988 EB
    (almost 1 ZB)
  • A Forecast of Worldwide Information Growth
    Through 2010 IDC
  • National Open Source Enterprise - Intelligence
    Community Directive No. 301, July 11, 2006
  • UC Berkeley School of Information Management and
    Systems Now much Information

4
Selected Societal Drivers and Observations
  • Scale of Things to Come
  • Information
  • Drivers of Digital Universe
  • 70 of the Universe is being produced by
    individuals
  • Organizations (businesses, agencies, governments,
    universities) produce 30
  • Wal-Mart has a database of 0.5 PB it captures
    30,000,000 transactions/day
  • The growth is uneven
  • Today the United States accounts for 41 of the
    Universe by 2010, the Asia Pacific region will
    be growing 40 faster than any of the other
    regions

5
Selected Societal Drivers and Observations
  • Scale of Things to Come
  • Information
  • Drivers of Digital Universe
  • Kinds of Data
  • About 2 GB of digital information is being
    produced per person per year
  • 95 of the Digital Universes information is
    unstructured
  • 25 of the digital information produced by 2010
    will be images
  • By 2010, the number of e-mailboxes will reach 2
    billion
  • The users will send 28 trillion e-mails/year,
    totaling about 6 EB of data

6
Selected Societal Drivers and Observations
  • Scale of Things to Come
  • Information
  • Drivers of Digital Universe
  • Kinds of Data
  • Interaction (ref 2007 Dagstuhl - key selected
    topic for workshops)
  • Today's interaction designed for point and click
    on individual items, groups(folders), and lists
  • Today's interaction assumes user knows subject,
    concepts within information spaces, and can
    articulate what they want
  • Today's interaction assumes data and
    interconnecting relationships are static in
    meaning over time
  • Today's interaction is one way initiated
  • Todays interaction (WIMP) designed over 30 years
    ago

7
Multiple Techniques Contribute to Threat
Assessment
Prediction
Synthesis
Visual Analytics
Cognition
Graph Matching
Analysis
Pattern Analysis
Content Management
Evidence Extraction
Organization
Link Discovery
Integration
Connect the Dots
Extraction
Aggregation
Data Information
Knowledge
8
Questions
  • What is the true measure of security?
  • Risk vs. Resilience
  • How do we innovate?
  • Requirements-driven vs. Use-inspired
  • What determines the value of information?
  • What we know vs. What we think we know
  • How de we judge performance?
  • Scale vs. Relevance
  • Whats the market?
  • Analyst vs. First Responder

9
Whats Needed
  • Focus on the internal rather than the external
  • Consideration of the meaning of our facts
  • Appreciation of resilience factors
  • Irrelevance of scale
  • Application and implementation
  • Expansion of the Visualization and Analytics
    Complex

10
Science Futures Research
  • Visual Analytics and Physics-based Simulation
    Program
  • Visually based mathematical methods and
    computational algorithms for discovering,
    comprehending, and manipulating diverse data and
    applying the resulting knowledge to anticipate
    terrorist incidents or catastrophic events and
    guide response and recovery activities
  • Data-intensive Computing, Privacy, and Forensics
    Program
  • Simpler, more efficient software algorithms and
    hardware architectures for extracting and
    managing data, assessing threats and
    consequences, ensuring information privacy,
    securing the cyber infrastructure, and ensuring
    telecommunications interoperability
  • HIGHLIGHTS
  • Canada-USA Collaboration - Collaborative
    Activity Agreement (CAA) under existing Canada
    USA treaty between DHS ST and Defense Research
    and Development Canada
  • Visualization and Analytics Complex The
    National Visualization and Analytics Center
    (NVAC), 5 university-based RVACs, 2 GVACs, and 20
    industry partners
  • National Research and Development Agenda

11
Visualization and Analytics Centers
Detecting the Expected -- Discovering the
UnexpectedTM
12
Fall 2007 VAC Consortium
13
VAC Vision (edited by Consortium Members)
  • VAC Values
  • Thought leadership in the sciences, technologies,
    and processes involved in visual analytics
  • Impact on client missions
  • Demonstrated understanding of client and end user
    needs
  • Effectiveness to innovate, incubate, develop,
    disseminate, and promote science and technology
  • Productive collaborations and respectful
    competition
  • Building an enduring visual analytics community
  • Effective VAC communications to the public
  • Welcome, meaningful, and fun experience for all.

14
Visual Analytics Capabilities
15

Example Technology ProductsIN-SPIRE Advanced
Text Analysis Platform
  • Cluster and Thematic views
  • Evidence Evaluation
  • Triage Networks
  • Visual analysis for multiple languages
  • Multi-Viewpoint Support
  • Affect and Emotion Measures
  • Correlation Analysis
  • Streaming Data
  • Collaborative Team Analytics

http//in-spire.pnl.gov
16
The Assessment Wall
Developed an interactive information
visualization system that provides an up-to-date
overview and helps users intuitively find
documents of interest on a large touch display.
  • A walk-up usable interface that provide anyone
    instant analytical capability.
  • Designed for team collaboration and discussion
    of analytical tasks.
  • Simple interface design to provide rapid
    analytical results is ideal for command room
    style utility.

17
Integrating Structured/Unstructured Text, Form,
and Data Base Visual Data Analysis
18
Scalable Reasoning System
A free-form visual environment and knowledge base
scaling across mobile devices and desktop
interfaces that integrates methods for organizing
data, reasoning with information, and
disseminating knowledge.
  • Uses light-weight interaction and visualization
    techniques to support use by any analyst.
  • Designed for real-time collaborative tasks and
    sharing of knowledge across distributed teams.
  • Assists in vetting of knowledge products through
    dissemination of the evidence and analysis that
    contributed to a product.

19
Law Enforcement and Counter-Terrorism
Desktop to Handheld Enabling cross-jurisdictional
situational awareness for rapid decision making
and resource deployment
SRS/DCAF
Intuidex
20
Interactive Graph Analytics
An integrated problem-solving environment
providing novel interactive visualization of
graphs with up to 1 million nodes, feature
extraction techniques, and topological and
semantic analysis.
Going from huge connected graphs to proximity
clusters
  • Real-time scalable algorithms provide
    visualization support to most any application
    with graph data.
  • Feature extraction and clustering can be used to
    provide different perspectives for semantic
    graphs in domains such as power grid analysis to
    environmental sensor analysis.

Visualizing a collection of transmission system
lines
21
Interactive Graph Analytics
22
Case Studythe 8/10/96 Disturbance
23
Threat Stream Generator
Creating the science for developing realistic,
synthetic data sets, based on scenarios, with
known ground truth for testing and evaluation of
analytical tools and techniques
  • The data sets and evaluation methods can be
    applied to a wide range of analytical tasks to
    determine the value of tools and techniques.
  • Development of better testing methodologies will
    result in more rapid and superior tool
    development.

Of all of the data generators that I have
seen/heard about in my time at the ICAHST, PNNLs
seems to be the one that simulates the real world
the most effectively. - ICAHST Testing
Manager
24
Selected Other Programs
  • Over 80 projects
  • User guided rapid analytics first look
  • Active products dynamic tailored multimodal
    assessment product
  • Information Synthesis mathematic foundations for
    semantic synthesis
  • Science of interaction starting with John Stasko
    at PNNL
  • RVACs each have 5-8 projects to be discussed by
    RVAC leads
  • Several Collaborative projects NVAC-Industry,
    RVAC-RVAC, RVAC-NVAC-Industry, RVAC-industry,
  • Look forward to adding to many
    of these

CANVAC
25
Top Ten Challenges within Visual Analytics
  • Human Information Discourse for Discoverynew
    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
  • Predictive/Proactive Visual Analytics
  • Visual Analytic Method Capture and Reuse

26
Top Ten Challenges within Visual Analytics
  • Dissemination and Communication
  • Visual Temporal Analytics
  • Validation/verification with test datasets openly
    available
  • Delivering short-term products while keeping the
    long view
  • Interoperability interfaces and standards
    multiple VAC suites of tools

27
Education
  • RVAC interns
  • Interns and scholars
  • Visual analytics curriculumand digital library
  • Analyst internships
  • IEEE VAST conferenceand graduate colloquium

Watch andWarn TrainingClass
2006 Interns
28
(No Transcript)
29
Spring Consortium and IEEE VAST 2008
  • Spring VAC Consortium May 21-22, 2008 in DC area
  • IEEE Symposium on Visual Analytics Science and
    Technology (VAST) 2008
  • http//conferences.computer.org/vast/vast2008/
  • Columbus, Ohio
  • Oct 14-19, 2008

30
Visual AnalyticsA Global Collaboration
James (Jim) J. Thomas Director DHS National
Visualization and Analytics Center AAAS Fellow,
PNNL Fellow http//nvac.pnl.gov, 509-375-2210
Joseph Kielman Director Science Futures,
Department of Homeland Security Director
Visualization Programs Joseph.Kielman_at_DHS.GOV,
202-254-5787
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