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@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 capability—but 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) Operational Benefits of Multiple SensorData Fusion Detection Tracking ID Aggregation Behavior Events Lethality Intent Opportunity • Multiple • Sensors • Reliability • Improved Detection • Extended Coverage (spatial and temporal) • Improved Spatial Resolution • Robustness (Weather/visibility, Countermeasures) • Improved Detection • Improved State Estimation (Type, Location, Activity) • CBRN Point and Standoff Sensors • CBRN Data Sources • Intel Sources • Air Surveillance • Surface Sensors • Standoff Sensors • Space Surveillance • Multiple Platform Sensors Sensor Mgmt Process Mgmt Level 0 — Sub-Object Data Association & Estimation: pixel/signal level data association and characterization • Diverse Sensors Level 1 — Object Refinement: observation-to-track association, continuous state estimation (e.g. kinematics) and discrete state estimation (e.g. target type and ID) and prediction State Estimates of Reduced Uncertainty And Improved Accuracy Level 2 — Situation Refinement: object clustering and relational analysis, to include force structure and cross force relations, communications, physical context, etc. Level 3 — Impact Assessment: [Threat Refinement]: threat intent estimation, [event prediction], consequence prediction, susceptibility and vulnerability assessment Level 4: Process Refinement: adaptive search and processing (an element of resource management) INFORMATION FUSION PROTOTYPE JEM JEM JWARN3 GCCS Level 0Processing Sub-object DataAssociation & Estimation Level 1Processing Single-ObjectEstimation Level 2Processing SituationAssessment Level 3Processing Threat/ImpactAssessment Methods: --Combinatorial Optimization --Linear/NL Estimation --Statistical --Knowledge-based --Control Theoretic JWARN3 Level 4Processing Adaptive ProcessRefinement Data BaseManagement System GCCS SupportDatabase FusionDatabase

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 don’t 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 • The Data Fusion community is progressing toward meaningful achievements in Level 2 and 3 fusion processing capability—a 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 • 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 proof—there 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” Ontology’s Contribution to Superior Fusion System Performance • These activities would comprise just a part of a larger Action Plan.

11. Ontology-Related Track: Key Issues for an Action Plan • An Action Plan for Ontology—What 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 Center’s study of Essential Elements of Information (EEI’s) • Cost-Efficient • EEI’s 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 exist—but-- • 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 Int’l 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 Int’l Community Required: eg, Canadian and Australian IF communities are addressing Ontological matters; TTCP and NATO both active • Broad communication, coordination required: • Website(s) • VTC’s • 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. 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. Ontology Construction Approach 1.ID Data Fusion Ontology Task – ID Military Utility 2.Data Fusion Ontology Purpose, Scope, Formality 3.Build Taxonomy; then specify concepts Select Tool 4.Collaborative Development Real World 5. Evaluate DF Ontology Verify Utility Validate

18. Ontology Construction • From Boury-Bisset, Gauvin:

19. Ontology Construction • From Boury-Bisset, Gauvin:

20. Requirements for an Ontology-Development Tool • Web-based collaborative environment • Flexible Meta-model • Dynamic configuration of the environment • Knowledge-level modelling • Ontology editing and discussing Ontology Construction • From Boury-Bisset, Gauvin:

21. Viewing Ontologies* * http://gollem.swi.psy.uva.nl/workshops/ka2-99/camready/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. Ontologies Inherently Reflect Complex Interrelationships Visualization of the Ontology Structure is Needed as a Construction Aid Visualization Tools are Needed That can Show Many, Complex Interrelationships Visualization of the Ontology*:A Consensus Development-Tool Need * J. Risch of Pacific-NW Battelle was also a workshop attendee and discussed Starlight’s capabilities; it is a capability reflective of the state-of-the-art in advanced visualization tools

24. Concurrent Information Analysisin Starlight

25. SummaryAction 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-state’s 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) • What’s 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 PSAP’s * United States Government Interagency Domestic Terrorism Concept of Operations Plan

28. Homeland Defense ApplicationsVisualization Examples: WMD and InfoWar

29. Urban Landscapes

30. Urban Landscapes

31. Urban Landscapes

32. 3D CFD Chemical Plume DispersionCT-Analyst @ NRL

33. Chemical Agent DispersionSoftware Solutions and Environmental Services Company

34. Building Internal StructuresArmy Corps of Engrs

35. Subway ApplicationsArgonne 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

39. Ordnance Explosive Power from Remote SensingOak Ridge Natl Lab