Visualization, Level 2 Fusion, and Homeland Defense - PowerPoint PPT Presentation

Loading...

PPT – Visualization, Level 2 Fusion, and Homeland Defense PowerPoint presentation | free to download - id: 3b94b6-YzBiO



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Visualization, Level 2 Fusion, and Homeland Defense

Description:

Visualization, Level 2 Fusion, and Homeland Defense Dr. James Llinas Research Professor, Director Center for Multisource Information Fusion University at Buffalo – PowerPoint PPT presentation

Number of Views:36
Avg rating:3.0/5.0
Slides: 40
Provided by: visnxNet
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Visualization, Level 2 Fusion, and Homeland Defense


1
Visualization, Level 2 Fusion, and Homeland
Defense
  • Dr. James Llinas
  • Research Professor, Director
  • Center for Multisource Information Fusion
  • University at Buffalo
  • llinas_at_eng.buffalo.edu

2
Outline
  • Overview of a DARPA-sponsored Workshop on
  • Ontology Definition and Development, and the
    Perceptual/Comprehension Interface for Military
    Concepts
  • Remarks on Visualization Challenges of Homeland
    Defense

3
The Workshop --Ontology Action
Plan --Perspectives on Visualization (Kesavadas)
4
Workshop Assertion
  • The Data Fusion community is progressing toward
    meaningful achievements in Level 2 and 3 fusion
    processing capabilitybut there is no community
    ontology for the L2/L3 products--a process must
    be started to assess the need for, nature of, and
    means to achieve a supporting, consensus L2/L3
    Ontology (or Ontologies) that yields the
    important benefits associated with
    ontologically-grounded systems, such as
    Interoperability, Semantic Consistency,
    Completeness, Correctness, Adaptability, etc

To include Threat States, Intent, etc.
5
Data Fusion Functional Model (Jt. Directors of
Laboratories (JDL), 1993)
INFORMATION FUSION PROTOTYPE
JEM
JEM JWARN3 GCCS
Level 0 Processing Sub-object Data Association
Estimation
Level 1 Processing Single-Object Estimation
Level 2 Processing Situation Assessment
Level 3 Processing Threat/Impact Assessment
Methods --Combinatorial Optimization --Linear/NL
Estimation --Statistical --Knowledge-based --Contr
ol Theoretic
JWARN3
Level 4 Processing Adaptive Process Refinement
Data Base Management System
GCCS
Support Database
Fusion Database
6
Ontology-Based Fusion Visualization
Visualization Challenges --the Ontology itself
(presuming it is large and complex) --the L2
fusion results (complex, high-dimensional,
abstract concepts, not spatially referenced)
Raw Data (Truly raw and also L1 estimates)
The Results of Which Provide the Raw Material
For Visualization
Associated to Ontologically- Based L2 Fusion
Process
(Which we dont have)
Ontology-based Information Visualization, F.
vonHarmelen, et al, Proceedings of the workshop
on Visualization of the Semantic Web (VSW'01)",
2001
7
An Ontology Action Plan for the Information
Fusion Community Results of a DARPA/CMIF
Workshop, Nov. 2002
  • Dr. James Llinas
  • Dr. Eric Little
  • Center for Multisource Information Fusion
  • University at Buffalo

8
Background
  • Analysis and Decision-Support Needs for New and
    Diverse defense and national-security problems
    are demanding major improvements in Level 2 and 3
    Information Fusion (IF) capabilities.
  • U.S. and International efforts are underway to
    address many of the foundational issues
    associated with achieving such IF capability,
    especially system architecture and algorithmic
    processing.
  • However, the topic of Ontological Requirements as
    a foundation for these L2, L3 initiatives has not
    been explicitly addressed, although it is agreed
    that many Ontologically-related activities are
    underway to include Ontological prototyping but
    largely addressed from a Computational Ontology
    point of view.
  • In addition, the abstract nature of many L2, L3
    information products also places a demand on the
    approach to and means for Visualization of such
    fusion products.
  • In November 2002, a Workshop sponsored by DARPA
    and the CMIF was held to address these latter two
    issues.
  • This briefing summarizes thoughts from the
    Workshop regarding the Ontology topic only.

9
Ontology Track
10
A Tentative Conclusion
  • This Workshop opened with the following
    assertion
  • This assertion, and the higher-level, implied
    assertion that Good Ontologies Yield Good Fusion
    Systems, was conditionally accepted by the
    Workshop attendees.
  • The conditional aspects revolved about the need
    for some type of experimental proofthere was a
    consensus on the need for
  • A Proof-of-Concept Demonstration / Experiment
  • Definition and Employment of Appropriate Metrics
    and Evaluation Procedures that Quantify
  • Ontology Quality Per Se
  • Good Ontologys Contribution to Superior Fusion
    System Performance
  • These activities would comprise just a part of a
    larger Action Plan.
  • The Data Fusion community is progressing toward
    meaningful achievements in Level 2 and 3 fusion
    processing capabilitya process must be started
    to assess the need for, nature of, and means to
    achieve a supporting, consensus Ontology (or
    Ontologies) that yields the important benefits
    associated with ontologically-grounded systems,
    such as Interoperability, Semantic Consistency,
    Completeness, Correctness, Adaptability, etc

11
Ontology-Related Track Key Issues for an Action
Plan
  • An Action Plan for OntologyWhat have we learned?
  • Do we agree there is a need for a consensus
    ontology?
  • Gauging the nature and size of the underlying
    Taxonomy
  • The issue of Admission to the Taxonomy
  • The issue of the Extent of the Taxonomy
  • Formal Ontological Methods
  • Degree of formalism required
  • Accommodating a Hybrid approach
  • Research issues
  • Consensus-forming
  • Approach
  • Configuration Control, once a baseline is
    established
  • Construction Methods
  • General approach
  • Automated Tools

12
Nature and Size of the L2, L3 Taxonomy
  • Nature Admission to the Taxonomy
  • Coarse Filter In the main, L2 is about
    Situational Assessment, and L3 is about Threat
    and Impact Assessment, and we can easily populate
    that portion of the taxonomy
  • Fine Filter To be determined
  • Candidate Approach Build on the OSD/Decision
    Support Centers study of Essential Elements of
    Information (EEIs)
  • Cost-Efficient
  • EEIs well received by operational community
  • Conduct initial analysis before next workshop
  • Incorporate pre-workshop taxonomy
  • Size estimated as a subset of 3700-long EEI
    list, TBD

13
Formality in Ontology-Development
  • Methods for formal ontology development
    existbut--
  • Degree of formality fundamentally depends on
    Ontology Requirements
  • Develop from a Systems-Approach
  • Need to build both application-requirements and
    technical requirements
  • Application Requires defining Role for Ontology
    in IF applications
  • Human understanding
  • Computational benefits
  • Performance/Effectiveness benefits
  • Technical Requires quantifying technical
    criteria of goodness
  • Consistency
  • Completeness
  • Accuracy
  • etc

14
Selecting the Level of Formality
Integrated Data Fusion Dictionary for the
designers, users
Computational Ontology suitable for automated
reasoning
Ontology suitable for structured data management
from Deborah McGuiness, Ontologies Come of
Age
15
Consensus-Forming
  • Approach Options Nominated
  • NATO STANAG-development process
  • Via Intl Society for Information Fusion (ISIF)
  • U.S. DoD lead but International in scope
  • Link to Computer Science community via
  • Open Source Consortium
  • IEEE, ACM
  • Link to Intl Community Required eg, Canadian
    and Australian IF communities are addressing
    Ontological matters TTCP and NATO both active
  • Broad communication, coordination required
  • Website(s)
  • VTCs
  • Use of CSCW technology
  • Specialized Conference sessions

16
Ontology Construction
  • Once Requirements have been specified, those
    reqmts either directly or indirectly influence
    the overall approach to Ontology construction,
    eg
  • Formalism
  • Language
  • Automated Tools
  • Tools for Visualizing the Ontology
  • Strategies for Ontology evaluation
  • In the following we borrow directly from the
    paper by Anne-Clair Boury-Bisset and M. Gauvin
    OntoCINC Server A Web-based Environment for
    Collaborative Construction of Ontologies, 19 Sept
    2002
  • Anne-Claire was a workshop attendee and briefed
    the attendees on the cited topic

17
Ontology Construction Approach
1.ID Data Fusion Ontology Task ID Military
Utility
  • 1. Identification of the task for which the
    ontology is being developed
  • 2. Definition of the requirements for the
    ontology purpose and scope
  • 3. Informal specification Build informal
    specification of concepts
  • 3. Encoding Formally represent the concepts and
    axioms in a language
  • 5. Evaluation of the ontology.

2.Data Fusion Ontology Purpose, Scope, Formality
3.Build Taxonomy then specify concepts
4.Collaborative Development
Select Tool
5. Evaluate DF Ontology
Real World
Verify Utility
Validate
18
Ontology Construction
  • From Boury-Bisset, Gauvin

19
Ontology Construction
  • From Boury-Bisset, Gauvin

20
Ontology Construction
  • From Boury-Bisset, Gauvin

21
Viewing Ontologies
http//gollem.swi.psy.uva.nl/workshops/ka2-99/c
amready/shum.pdf
22
Ontology Visualization
Ontology-based Information Visualization, F.
vonHarmelen, et al, Proceedings of the workshop
on Visualization of the Semantic Web (VSW'01)",
2001
23
Visualization of the Ontology A Consensus
Development-Tool Need
J. Risch of Pacific-NW Battelle was also a
workshop attendee and discussed Starlights
capabilities it is a capability reflective of
the state-of-the-art in advanced visualization
tools
24
Concurrent Information Analysis in Starlight
25
Summary Action Plan Tasks
  • Define Participants
  • Begin the Systems Engineering process for
    Ontology Development
  • Task(s) within an Future Combat System scenario
  • Coordination with CECOM, DARPA
  • Role
  • Coordination with CECOM, DARPA
  • Ontology Requirements to include Formality
    requirements
  • Define also Visualization-Support Requirements
    and Visualization Interface
  • Encoding
  • Test and Evaluation
  • Reviewing master EEI-set as a foundation for an
    initial Taxonomy for L2, L3
  • Determine coarse and fine filters for EEI
    selection
  • Defining and executing the proof of concept demo
  • Scenario One of the approved FCS scenarios
  • Metrics and evaluation approach TBD
  • Scope TBD
  • Develop an approach to Consensus-forming
  • Coordination with US, NATO, TTCP, ISIF

26
Visualization Challenges of Homeland Defense
  • Homeland defense is protecting a nation-states
    territory, population and critical infrastructure
    at home by
  • Deterring and defending against foreign and
    domestic threats.
  • Supporting civil authorities for crisis and
    consequence management.
  • Intelligent Response and Recovery
  • Helping to ensure the availability,
    integrity, survivability, and adequacy of
    critical national assets.
  • Planning and Mitigation
  • US Army TRADOC White Paper http//www.fas.org/spp
    /starwars/program/homeland/final-white-paper.htm

27
Homeland Defense and WMD (CBRN)
  • Whats different about WMD?
  • Situations not easily recognizable
  • Situations may comprise multiple, phased events
  • Most likely a complex (3D) urban landscape
    environment
  • Broad repertoire of input sources
  • Typical Multi-sensor/multi-source
  • Atypical eg Epi-Intel (human, epizootic, food
    surety)
  • Responders at high risk that risk must be
    factored into response plan
  • Location of incident is a crime scene requiring
    evidence preservation
  • Subtle contamination-propagation must be
    accounted for
  • Incident scope may grow exponentially, stressing
    multi-jurisdictional resources
  • Strong public reaction fear, panic, chaos, anger
  • Time critical
  • Responder facilities may in fact be targets eg
    PSAPs

United States Government Interagency Domestic
Terrorism Concept of Operations Plan
28
Homeland Defense Applications Visualization
Examples WMD and InfoWar
29
Urban Landscapes
30
Urban Landscapes
31
Urban Landscapes
32
3D CFD Chemical Plume Dispersion CT-Analyst _at_ NRL
33
Chemical Agent Dispersion Software Solutions and
Environmental Services Company
34
Building Internal Structures Army Corps of Engrs
35
Subway Applications Argonne Natl Lab Sandia
36
Network Intrusion Detection
37
Next CMIF Workshop Army-Sponsored
  • Ontology and Visualization of Data Fusion
    Concepts Support to Command and Control in a
    Network-Centric Warfare Environment
  • Four Tracks
  • Evaluation
  • Impacts of the Distributed Environment
  • Notion of Contextual Understanding
  • Homeland Defense Applications
  • Dates TBD, Summer or early Fall 2003
  • Location Beaver Hollow Conference Center, Java,
    NY

38
(No Transcript)
39
Ordnance Explosive Power from Remote Sensing Oak
Ridge Natl Lab
About PowerShow.com