Visualization, Level 2 Fusion, Visualization, Level 2 Fusion, and Homeland Defenseand Homeland Defense

Dr. James LlinasResearch Professor, Director

Center for Multisource Information Fusion

University at Buffalo

llinas@eng.buffalo.edu

OutlineOutline• 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

The WorkshopThe Workshop

--Ontology Action Plan

--Perspectives on Visualization (Kesavadas)

Workshop AssertionWorkshop 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.

Data Fusion Functional Model(Jt. Directors of Laboratories (JDL), 1993)

Level 0 — Sub-Object Data Association & Estimation: pixel/signal level data association and characterization

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

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)

Level 0Processing

Sub-object DataAssociation &

Estimation

Level 1Processing

Single-ObjectEstimation

Level 2Processing

SituationAssessment

Level 3Processing

Threat/ImpactAssessment

Level 4ProcessingAdaptive Process

Refinement

Data BaseManagement System

SupportDatabase

FusionDatabase

INFORMATION FUSION PROTOTYPEJEM

JWARN3GCCS

JWARN3

GCCS

JEM• CBRN Point and

Standoff Sensors• CBRN Data

Sources• Intel Sources• Air Surveillance• Surface Sensors• Standoff Sensors• Space

Surveillance

• Reliability• Improved Detection

• Extended Coverage(spatial and temporal)

• Improved SpatialResolution

• Robustness (Weather/visibility, Countermeasures)

• Improved Detection

• Improved State Estimation (Type, Location, Activity)

• DiverseSensors

• MultiplePlatform Sensors

• MultipleSensors

Operational Benefits of Multiple SensorOperational Benefits of Multiple SensorData FusionData Fusion

State Estimates of Reduced UncertaintyAnd Improved Accuracy

DetectionTracking

ID

AggregationBehavior

Events

LethalityIntent

Opportunity

Sensor MgmtProcess Mgmt

Methods:--Combinatorial Optimization

--Linear/NL Estimation--Statistical

--Knowledge-based--Control Theoretic

Ontology-Based Fusion & Visualization*

“Raw Data”(Truly raw and also L1 estimates)

Associated to Ontologically-Based L2 Fusion Process

The Results of WhichProvide the Raw Material

For Visualization

* Ontology-based Information Visualization, F. vonHarmelen, et al, Proceedings of the workshop on Visualization of the Semantic Web (VSW'01)", 2001

(Which we don’t have)

Visualization Challenges:--the Ontology itself (presuming it is large and complex)

--the L2 fusion results (complex, high-dimensional, abstract concepts, not spatially referenced)

An Ontology Action Plan for An Ontology Action Plan for the Information Fusion the Information Fusion

Community:Community:Results of a DARPA/CMIF Workshop, Nov. 2002

Dr. James Llinas

Dr. Eric Little

Center for Multisource Information Fusion

University at Buffalo

BackgroundBackground

• 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.

Ontology TrackOntology Track

A Tentative ConclusionA 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 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.

• 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

Ontology-Related Track: Ontology-Related Track: Key Issues for an Action PlanKey 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

Nature and Size of the L2, L3 TaxonomyNature 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

Formality in Ontology-DevelopmentFormality 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

Selecting the Level of Selecting the Level of FormalityFormality

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Integrated Data FusionDictionary for the designers, users

Computational Ontology suitable for automated

reasoning

Ontology suitable for structured data

managementfrom: Deborah McGuiness, “Ontologies Come of Age”

Consensus-FormingConsensus-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

• 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*

Ontology ConstructionOntology Construction

•Anne-Claire was a workshop attendee and briefed the attendees on the cited topic

Ontology Construction ApproachOntology Construction Approach

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.

1.ID Data Fusion Ontology Task – ID Military Utility

2.Data Fusion Ontology Purpose, Scope, Formality

3.Build Taxonomy; then specify concepts

4.CollaborativeDevelopment

SelectTool

5. Evaluate DF Ontology

RealWorld

Validate

VerifyUtility

Ontology ConstructionOntology Construction• From Boury-Bisset, Gauvin:

Defence R&D Canada – Valcartier • R & D pour la défense Canada – Valcartier

Ontology development methodologies

• Most important methodologies– The TOVE (Toronto Virtual Enterprise) project [Uschold

andGruninger, 96]

– The Enterprise ontology [Uschold and Gruninger, 96]

– The Methontology method [Fernandez et al, 97]

– The IDEF5 ontology capture method [KBSI, 94]

• Main stages– Identification of the task for which the ontology is being developed;

– Definition of the requirements for the ontology: purpose and scope;

– Informal specification: Build informal specification of concepts;

– Encoding: Formally represent the concepts and axioms in a language;

– Evaluation of the ontology.

• From Boury-Bisset, Gauvin:

Defence R&D Canada – Valcartier • R & D pour la défense Canada – Valcartier

Comparison of ontological engineering tools

Evaluation criteria / tools

OntolinguaServer

WebOnto Protégé 2000 OntoSaurus ODE

High-level primitives

Primitives from the Frame Ontology

Language OCML. Many primitives available

OKBC knowledge model

LOOM language Conceptual level: concepts, relations, axioms, subclass-of, etc.

Existence of Libraries

Important repository of ontologies

Many examples Not available Yes Examples only

Interface Good graphical interfaceDifficult to use

Very goodClear

Good interface. Customizable layout

Ontology browsing mainly.

Not available for evaluation

collaborative ontology buiding

Best tool. Users, groups.R/W accessNotification

Synchronous cooperation: lock function.Broadcast / receive

Not a collaborative tool

Lock function for collaborative editing

Asynchronous cooperation only

Config. Web Server Web Server Local installation Both Local installation

Import / Export

Many formalisms: CLIPS, LOOM, CORBA’s IDL

No export JDBC DatabaseRDF

Export in Ontolingua, KIF, IDL.

Export: Ontolingua, F-Logic

Ontology ConstructionOntology Construction

• From Boury-Bisset, Gauvin:

Ontology ConstructionOntology Construction

• Web-based collaborative environment

• Flexible Meta-model

• Dynamic configuration of the environment

• Knowledge-level modelling

• Ontology editing and discussing

Requirements for an Ontology-Development Tool

Defence R&D Canada – Valcartier • R & D pour la défense Canada – Valcartier

Collaborative Ontology Development using OntoCINC Server

TOOL

FORMALISM

ROLE

METHOD

Ontology DevelopmentEnvironment

Decisions

Comments Forum discussion

Concepts

Attributes

Relations

Semanticmeaning

Synonyms

Metamodel management

Presentation

Captureexpertise

Viewing Ontologies*

* http://gollem.swi.psy.uva.nl/workshops/ka2-99/camready/shum.pdf

Ontology Visualization*

* Ontology-based Information Visualization, F. vonHarmelen, et al, Proceedings of the workshop on Visualization of the Semantic Web (VSW'01)", 2001

Visualization of the Ontology*:Visualization of the Ontology*:A Consensus Development-Tool NeedA Consensus Development-Tool Need

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Information ModelingInformation ModelingInformation Modeling

Note that only the types of possible relationships are predefined; not the contents of the objects themselves.

This makes possible the integration and concurrent analysis of an unlimited variety of information types

• Hybrid model captures multiple types of relationships that may exist among arbitrary “information objects”

• Supports both information retrieval and comparison operations

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Starlight Global VisualizationsSStarlight Global Visualizationstarlight Global Visualizations

NetworkRelationships

Hierarchical Relationships

Text Similarity Discrete PropertyCorrelations

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Concurrent Information AnalysisConcurrent Information AnalysisConcurrent Information AnalysisOntologies 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

* 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

Concurrent Information AnalysisConcurrent Information Analysisin Starlightin Starlight

SummarySummaryAction Plan TasksAction 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

Visualization Challenges of Visualization Challenges of Homeland DefenseHomeland 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 RecoveryIntelligent Response and Recovery

• Helping to ensure the availability, integrity, survivability, and adequacy of critical national assets.

• Planning and MitigationPlanning and Mitigation

US Army TRADOC White Paper: http://www.fas.org/spp/starwars/program/homeland/final-white-paper.htm

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

Homeland Defense Applications

Visualization Examples: WMD and InfoWar

Urban LandscapesTerraSim - Philly & Pittsburgh examples

Harris RealSite

Geometry is Auto-Extracted from Imagery

Building Sides textures generated from satellite, aerial, or handheld photos

Models can be hand enhanced as needed after auto-extraction

Interactive environmentInReality provides mensurationtests for information discovery from datasets

Urban Landscapes

Harris InReality

Mensuration - determines distance and line of sight from any point on the database

Urban Landscapes

3D CFD Chemical Plume DispersionCT-Analyst @ NRL

Chemical Agent DispersionSoftware Solutions and Environmental Services Company

Building Internal StructuresArmy Corps of Engrs

Subway ApplicationsArgonne Natl Lab + Sandia

Network Intrusion Detection

• “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

Next CMIF Workshop:Army-Sponsored

Ordnance Explosive Power from Remote SensingOak Ridge Natl Lab