# 1 # 1 model-based communication networks, valued information at the right time (virt) & rich...

# # 11

Model-based Communication Networks, Model-based Communication Networks, Valued Information at the Right Time (VIRT) Valued Information at the Right Time (VIRT)

& Rich Semantic Track (RST):& Rich Semantic Track (RST):

Filtering Information by Value to Improve Filtering Information by Value to Improve

Collaborative Decision-MakingCollaborative Decision-Making

Project Report on CEC CollaborationProject Report on CEC Collaboration

Rick Hayes-Roth & Curt BlaisRick Hayes-Roth & Curt Blais

[email protected]@nps.edu & & [email protected]@nps.edu

August 21, 2008

# # 22

OutlineOutline

Overall Vision: Model-based Communication Overall Vision: Model-based Communication Networks, VIRT, and Rich Semantic TrackNetworks, VIRT, and Rich Semantic Track

CEC/VIRT Project Prior Results and CEC/VIRT Project Prior Results and AccomplishmentsAccomplishments

2008 Statement of Work2008 Statement of Work Recent ResultsRecent Results Where Do We Go from Here?Where Do We Go from Here?

# # 33

OutlineOutline




# # 44

COIs

Overall Vision: Model-based Overall Vision: Model-based Communication Networks, VIRT Communication Networks, VIRT

and Rich Semantic Trackand Rich Semantic Track

Past

GlobalInformation

Grid

PastPresent

Future

PresentFuture Present

Past

Present

Future

Past

Future

COIs

Shared World Models

Common Track SemanticsState-full Network

# # 55

Overall Vision: Model-based Overall Vision: Model-based Communication Networks, VIRT Communication Networks, VIRT

and Rich Semantic Trackand Rich Semantic Track

Past

PastPresent

Future

PresentFuture Present

Past

Present

Future

Past

Future

Common Track SemanticsState-full Network

Valued Bits

Value

d Bits

GlobalInformation

Grid

Shared World Models

# # 66

OutlineOutline




# # 77

Key Results Previously ReportedKey Results Previously Reported Advanced theory and implementation of Valued Information at the Advanced theory and implementation of Valued Information at the

Right Time (VIRT) and Rich Semantic Track (RST)Right Time (VIRT) and Rich Semantic Track (RST) Demonstrated significant reductions in bandwidth from 2-5 orders of Demonstrated significant reductions in bandwidth from 2-5 orders of

magnitude in initial studies to 45-90% in CEC-specific simulated magnitude in initial studies to 45-90% in CEC-specific simulated message streamsmessage streams

Developed simulation framework for studying VIRT and RSTDeveloped simulation framework for studying VIRT and RST Prepared/published/presented several theses and papersPrepared/published/presented several theses and papers Established VIRT as key focus of W2COGEstablished VIRT as key focus of W2COG

This work now embraced by DISA and JITCThis work now embraced by DISA and JITC Helped PACOM start up CMA JCTD around sharing of rich track Helped PACOM start up CMA JCTD around sharing of rich track

information; leading design and development of the MIEMinformation; leading design and development of the MIEM Advised Joint Track Management (JTM) Architecture Working Group Advised Joint Track Management (JTM) Architecture Working Group

and now CNDE (Consolidated Navy Data Enterprise) and now CNDE (Consolidated Navy Data Enterprise) Conducted computational analysis of CEC simulated message Conducted computational analysis of CEC simulated message

streamsstreams Mapped CEP-to-Track-User message elements to abstract Rich Mapped CEP-to-Track-User message elements to abstract Rich

Semantic Track modelSemantic Track model Defined initial measures of performance for evaluating VIRT Defined initial measures of performance for evaluating VIRT

applicationsapplications

# # 88

CEC/VIRT Message Stream AnalysisCEC/VIRT Message Stream Analysis

Simulated message streams provided by JHU/APLSimulated message streams provided by JHU/APL

Focus on application of VIRT at an intermediary node Focus on application of VIRT at an intermediary node (“VIRT Track User”) between the CEC network and the (“VIRT Track User”) between the CEC network and the general GIG networkgeneral GIG network

Value determined by “GIG User” Conditions of Interest Value determined by “GIG User” Conditions of Interest (COIs) relating to expected position and velocity, track (COIs) relating to expected position and velocity, track identification, and engagement statusidentification, and engagement status

Computed bit traffic reduction of 45-90% in short Computed bit traffic reduction of 45-90% in short duration, highly dynamic air tracking scenariosduration, highly dynamic air tracking scenarios

Demonstrated capability to “tune” bit traffic flows Demonstrated capability to “tune” bit traffic flows based on user-defined thresholds in accuracy of based on user-defined thresholds in accuracy of estimationsestimations

# # 99

CEC Message Stream AnalysisCEC Message Stream Analysis

Can achieve significant reduction in bit traffic from Can achieve significant reduction in bit traffic from attention to user information needs (COIs)attention to user information needs (COIs)

Demonstrated mechanisms for enabling CEC Demonstrated mechanisms for enabling CEC message traffic to be filtered for non-CEC usersmessage traffic to be filtered for non-CEC users

Opportunity for further research into user-specified Opportunity for further research into user-specified COIs and message processing using more of the COIs and message processing using more of the message content (e.g., certainty and accuracy data) message content (e.g., certainty and accuracy data)

# # 1010

OutlineOutline




# # 1111

SOW Adjusted to $100K BudgetSOW Adjusted to $100K Budget

1.1. Develop and analyze strategies for optimizing Develop and analyze strategies for optimizing distribution of track data among distributed nodes, with distribution of track data among distributed nodes, with participating nodes having a variety of needs for participating nodes having a variety of needs for precision and timelinessprecision and timeliness

2.2. Enhance the initial simulation environment to create and Enhance the initial simulation environment to create and analyze a variety of usage scenarios, to support the analyze a variety of usage scenarios, to support the analysis and to improve the demonstration of resultsanalysis and to improve the demonstration of results

3.3. Improve the methods and tools available for describing Improve the methods and tools available for describing what information is valued, selecting it from available what information is valued, selecting it from available track data, and providing it to interested clientstrack data, and providing it to interested clients

4.4. Help assure that CEC approaches work harmoniously Help assure that CEC approaches work harmoniously with other DoD initiatives and other DHS agency efforts with other DoD initiatives and other DHS agency efforts that require air tracking capabilitiesthat require air tracking capabilities

-

+

# # 1212

OutlineOutline




# # 1313

Demonstration SoftwareDemonstration Software

Web-based application to enter VIRT Web-based application to enter VIRT conditions of interestconditions of interest

Server-side software checks COIs against input Server-side software checks COIs against input data streams (XML) and generates alerts to data streams (XML) and generates alerts to registered clients (sent via e-mail, cell phone registered clients (sent via e-mail, cell phone text message, or with other transport text message, or with other transport mechanisms possible)mechanisms possible)

# # 1414

User Registration and LoginUser Registration and Login

User Registration User Login

# # 1515

Create New COIs or Display/Edit/Subscribe to Create New COIs or Display/Edit/Subscribe to Existing COIs (using Cursor-On-Target Data)Existing COIs (using Cursor-On-Target Data)

# # 1616

Editing a COIEditing a COI

# # 1717

Subscribing to a COISubscribing to a COI

# # 1818

Ongoing DevelopmentOngoing Development

Adding track stream generatorAdding track stream generatorAdding geographic visualizationAdding geographic visualizationAdding predictive event recognizer: Adding predictive event recognizer:

At some future time At some future time tt, where , where tt < < TTmaxmax, determine if the , determine if the distance between object distance between object AA and object and object BB is less than (or, the is less than (or, the alternative, greater than) some threshold distance alternative, greater than) some threshold distance DD, and , and where the probability of this assertion being true is greater where the probability of this assertion being true is greater than 1 – than 1 – αα, where , where αα is a given significance level. is a given significance level.

Integrating knowledge base Integrating knowledge base representation of Rich Semantic Trackrepresentation of Rich Semantic Track

Using RST as a translation hub among diverse track data modelsUsing RST as a translation hub among diverse track data models

# # 1919

Progress on Standardizing RST: Progress on Standardizing RST: The VisionThe Vision

Rich Semantic Track- conceptual hub for

interchange and automatedreasoning

CECTrack

Messages

JointTrack

ManagementData Model

CMAMaritime

Information Exchange Model

OtherTrack

Data Models

# # 2020

The Rich Semantic The Rich Semantic Track ModelTrack Model

TrackTrackBeliefsBeliefs

Identity and CharacteristicsIdentity and CharacteristicsDynamic State at Time TDynamic State at Time THistory of states (past “track”)History of states (past “track”)Predicted states (future “track”)Predicted states (future “track”)

Meta-Information (applicable to each element of belief)Meta-Information (applicable to each element of belief)Evidence Evidence InferencesInferencesError and uncertainty estimatesError and uncertainty estimatesTemporal qualificationsTemporal qualificationsSpatial qualificationsSpatial qualifications

The top-level conceptual hierarchy for Track.The full hierarchy has more than 125 high level concepts.

# # 2121

MIEM ObjectivesMIEM ObjectivesShare actionable maritime intelligence Share actionable maritime intelligence

in a net-centric way:in a net-centric way:Simple/raw data exchangeSimple/raw data exchangeFusion output representationFusion output representationAdvanced analytics supportAdvanced analytics support

Establish unambiguous maritime Establish unambiguous maritime lexicon that:lexicon that:

Supports communication among data Supports communication among data providers and consumersproviders and consumers

Embodies broad expertise covering the Embodies broad expertise covering the extent of the maritime domainextent of the maritime domain

Leverages existing models and Leverages existing models and knowledge basesknowledge bases

Enable communications from/with Enable communications from/with future services and capabilitiesfuture services and capabilities

Permit extension to more sophisticated Permit extension to more sophisticated data without change to existing data without change to existing systemssystems

Import partners’ databases and export to Import partners’ databases and export to them with efficient translatorsthem with efficient translators

DynamicDynamic nature of all quantities nature of all quantities (potentially any belief/value (potentially any belief/value can vary in time) can vary in time)

Inexact Inexact information (“roughly 30 information (“roughly 30 feet long”)feet long”)

RelativeRelative information (“a mile information (“a mile from the leader of XYZ”)from the leader of XYZ”)

ConditionalsConditionals (“all facilities open (“all facilities open on Thursdays”)on Thursdays”)

ComplexComplex queries (“return last queries (“return last five ports of call for vessels five ports of call for vessels flying Chinese of Korean flying Chinese of Korean flags and within 3 days of US flags and within 3 days of US costal waters”)costal waters”)

PedigreePedigree (“position was derived (“position was derived from AIS message A, ELINT from AIS message A, ELINT data B, and HUMINT source data B, and HUMINT source C, using inference rule D”)C, using inference rule D”)

BeliefBelief conditions (“value is conditions (“value is considered 85% reliable”, considered 85% reliable”, “value was provided by “value was provided by source S”, etc.)source S”, etc.)

BehaviorsBehaviors, states & histories , states & histories (“the events make up an (“the events make up an overall fishing voyage”)overall fishing voyage”)

# # 2222

Levels of Value Added InformationLevels of Value Added Information

LevelLevel TypeType ExampleExample Value addedValue added

1 (lowest)1 (lowest) Sensor system reportsSensor system reportsAIS AIS (Automatic (Automatic Information System)Information System)

Reduced development Reduced development costs for consumerscosts for consumers

22Caveats & simple meta-Caveats & simple meta-datadata Sensor type, classificationSensor type, classification

Implicit quality Implicit quality assessmentassessment

33Fused data & inferred Fused data & inferred beliefsbeliefs Position, crewPosition, crew

Synergistic Synergistic improvement in SAimprovement in SA

44Degree of belief & Degree of belief & pedigreepedigree Evidence, qualityEvidence, quality

Explicit information Explicit information about qualityabout quality

55Multiple alternatives & Multiple alternatives & analysisanalysis Ambiguity, uncertaintyAmbiguity, uncertainty

Explicit assertions of Explicit assertions of certaintycertainty

66History, behavior & future History, behavior & future projectionsprojections

Voyages & Voyages & predicted coursespredicted courses

Enables basic predictive Enables basic predictive analysisanalysis

77““Of interest” conditions & Of interest” conditions & watch listswatch lists Suspicious cargo on Suspicious cargo on

boardboard

Increased analytical Increased analytical efficiencyefficiency

88Threats & anomaliesThreats & anomalies Dangerous undeclared Dangerous undeclared

cargocargoIncreased pre-emptive Increased pre-emptive threat reductionthreat reduction

9 (highest)9 (highest) Case files for key entitiesCase files for key entitiesHistories, highlights, Histories, highlights, comprehensive detailscomprehensive details

Enables in-depth Enables in-depth predictive analysispredictive analysis

# # 2323

MIEM is a Nascent StandardMIEM is a Nascent Standard

CMA JCTD transfers it to MDA COI in Oct.CMA JCTD transfers it to MDA COI in Oct. Navy and USCG committed to MIEMNavy and USCG committed to MIEM NIEM (DHS) also committed to MIEMNIEM (DHS) also committed to MIEM RST concepts should propagate more widelyRST concepts should propagate more widely

# # 2424

Primary Object TypesPrimary Object Types

• Vessels - Characteristics, capabilities, dynamic state, and relationships

• Persons - Identification, description, whereabouts, relationships

• Cargo - Shipments, equipment, manifest, and goods

• Facilities - Ports, organizations, and governments

• Events - Relates entities with associated causes and effects

• Threats - Capability, opportunity, level, threatening entity, and target

• Of Interest Lists - Heterogeneous lists of MIEM objects

# # 2525

Vessel Model DetailsVessel Model Details

• Persons• Cargo• Facilities

Affiliations

• Equipment• Cargo• Events

State

• Movement Segments• Ports of Call• Voyages

MovementCharacteristics

• Range• Speed• Cargo

Capabilities

• Size• Structure• Design

Physical

• Home Port• Classification

Miscellaneous

• ISSC• NOA• Safety Cert

Documentation

• Name• Call Sign• MMSI• IMO

Identifiers

Vessel

• Base Metadata• Extended Metadata

Extended Base

• Voyage Type• Track Type• Kinematics Type• Boarding Type

Support Types

# # 2626

related-to

Typical Vessel RelationshipsTypical Vessel Relationships

• Number • Origin/Destination• Type• Use Type

Voyage Ports Of Call

• Time of Arrival• Time of Departure• Port Identifier

Port Of Call

Vessel

0..* 0..*

0..*

1 1 1

1

• Passenger Reference• Crew Member Reference

Persons On Board

• Name• Citizenship

Person

0..*

has-a

on-board

• Embedded (“has-a”)

has-a has-a

has-a

• Associations (“on board”) - Strong, explicit relationships - Defined Association Types

• Affiliations (“related-to”) - Weak relationships between entities - ID/IDREFS references

# # 2727

Person Model DetailsPerson Model Details

• Family• Organization• Employment

Affiliations

• Work• Current• Residence• Temporary

Whereabouts

• Height• Weight• Color• Gender• Marks

PhysicalCharacteristics

• Birth• Death• Biometrics• Events

Details

• Name• Citizenship• SSN

Identifiers

Person


Extended Base

• Handedness• Gender• Associations

Support Types

# # 2828

Facility Model DetailsFacility Model Details

• Contractors• Organization• Government• Staff

Affiliations

• Location• Accessibility• Sub-Facility• Parent-Facility


• Name• BE Number• Type

Identifiers

Facility


Extended Base

• Port Associations• COTP Region

Support Types

• Certifications

Documentation

Port

• Port Name• Code• Type

Identifiers

• Vessels

State

• Depth• Max Vessels• Number Docks• Cargo Capabilities


• Cargo

State

# # 2929

Cargo Model DetailsCargo Model Details

• Equipment• Goods Items• Involved Party

Affiliations

• Weights• Measures• Declared Values• Route

Characteristics

• HazMat• Status• Biometrics• Events

Status

• Bill Of Lading• Booking Number• Identifier

Identifiers

Shipment


Extended Base

• Goods Item• Manifest• Associations

Support Types

Equipment

• Owner• Shipment• Vessel• Facility

Affiliations• Security Devices• Weights• Measures• Temperature Controls

Characteristics• HazMat• Empty• Events

Status• Number• Identifier• Type

Identifiers

# # 3030

AbstractAbstract Types: Types: Threats, Of Interest Lists & EventsThreats, Of Interest Lists & Events

• Name• Start/End Time• Description• Category• Type• Location• Affiliated Entities

Event


Extended Base

• Severity• Casualty Details

Incident

• Capability• Intent• Description• Level• Opportunity• Threatening Entity • Target of Threat

Threat

• Name• Publisher• POC• Type• Items

Of Interest List

# # 3131

Base Types with MetadataBase Types with Metadata

• Affiliations• Comments• Validity Time• Confidence• Completeness

Basic Metadata

• Information Source• Analysis• Anomaly• Data Rights• Pedigree• Vulnerability

Extended Metadata

• All beliefs carry Metadata• Simple beliefs carry Basic Metadata

• MIEM Support Types carry basic Metadata• Complex beliefs carry Extended Metadata

• MIEM Primary Types carry extended metadata

AddressTypeNameType

POCTypeVesselType

EventTypePersonType

# # 3232

How do we Use the MIEMHow do we Use the MIEM to Describe Situations? to Describe Situations?

As of February 2008, the ship was sold to an Iranian company, IC2, and was reflagged as a Panamanian. It sailed from Portland, ME to Abu Dhabi where it had some new equipment EQ1, EQ2 added to it by organization ORG3. Then it made a new voyage to South Africa, with stops at Djibouti and Dar es Salaam before arriving at Cape Town with a filed crew and passenger manifest. We have good track observations on the first leg of this voyage only.

An Illustration of Vessel State and Entity Relationships

# # 3333

Example Vessel State andExample Vessel State andEvent RelationshipsEvent Relationships

Sold Re-flag

Ownership Flag

Port ofCall

Port ofCall

Port ofCall

Ports Of Call

Equipment Change

Voyage Start/Stop/End

Port Departures(4)

Voyage Details

Equipment

Vessel State

Event Details

Port ofCall

Track

Movement Details

Port Arrivals(4)

Part-of relationships(Embedded)

Explicit relationships(Associations)

Weak relationships(Affiliations)

VoyageManifest

Crew

Passenger

PersonsOn Board

Person 001

Person 002

Person n

Boarding

# # 3434

Example Vessel State andExample Vessel State andEvent Relationships (XML)Event Relationships (XML)

Capture simple concepts simply – vessel name is “MV1”Relate entities to the underlying Events that cause them:Change of Ownership causes the state of the “owner” to

change

Describe complex relationships between many entities:

Persons On Board include a crew member with id “PERSON0001” who has the crew role the “Captain”. He embarked at the Port with

id “PORT0001”

The person with id “PERSON0001” is defined as having the name “John Doe” and has an affiliation to a vessel with id “VESSEL001”

which he boarded at the port with id “PORT0001”

The Port with id “PORT0001” is defined as being the port of “Portland, ME” in the country “USA” with a defined set of properties.

# # 3535

Final Technical ObservationsFinal Technical Observations

Powerful language for expressing actionable Powerful language for expressing actionable intelligence documentsintelligence documents

Extensible – Model can be extended to Extensible – Model can be extended to produce application specific schemasproduce application specific schemas

# # 3636

StatusStatus

Beta test version released February 8, Beta test version released February 8, 20082008

Incremental Design review held June 10-Incremental Design review held June 10-11, 200811, 2008

Version 1.0 product release scheduled Version 1.0 product release scheduled September 19, 2008September 19, 2008

# # 3737

Beta TestersBeta Testers

CMA JCTD -CargoCMA JCTD -CargoMASTER JCTDMASTER JCTDMDA DS COIMDA DS COIMAGNET/MIFCPACMAGNET/MIFCPACCMA – SingaporeCMA – SingaporeSeahawkSeahawkNSA - RTRGNSA - RTRGNAVAIR - Tampa Bay Maritime Domain Awareness NAVAIR - Tampa Bay Maritime Domain Awareness

System (MDAS) System (MDAS) TTCP – Maritime AG 8TTCP – Maritime AG 8 International MDAInternational MDASPAWAR Charleston - MDA Non Classified Enclave SPAWAR Charleston - MDA Non Classified Enclave

# # 3838

Transition to MDA-NIEMTransition to MDA-NIEM

Transition the MIEM to Transition the MIEM to the DHS/DOJ National the DHS/DOJ National Information Exchange Information Exchange Model (NIEM) as a new Model (NIEM) as a new Maritime DomainMaritime Domain

Transition the MDA DS Transition the MDA DS COI DMWG to an MDA COI DMWG to an MDA Data Management Data Management GroupGroup

The MDA-DMG becomes The MDA-DMG becomes the Maritime Domain the Maritime Domain ownerowner

Maritime Domain Owner

MDA – Data Mgt Group

Planned FY09

# # 3939

MIEM ConclusionMIEM Conclusion

MIEM provides a language for expressing MIEM provides a language for expressing actionable intelligenceactionable intelligence– Rich semantics for pragmatics of “tracking”– Supports higher-levels of analysis– Directly supports resource cueing & interdiction

MIEM’s focus on document sharing supports MIEM’s focus on document sharing supports a vital vision of collaborative intelligencea vital vision of collaborative intelligence

The NIEM will incorporate the MIEM directly & The NIEM will incorporate the MIEM directly & as a guide for higher-levels of Core valueas a guide for higher-levels of Core value

MIEM elements and approach should benefit MIEM elements and approach should benefit many intelligence suppliers and consumersmany intelligence suppliers and consumers

# # 4040

OutlineOutline




# # 4141

Observations about our CollaborationObservations about our Collaboration

CEC & IWS have provided generous supportCEC & IWS have provided generous support NPS is good at several thingsNPS is good at several things

Academic work by professors Academic work by professors Theses by students, but opportunisticallyTheses by students, but opportunistically Applied research if predictable and sustainableApplied research if predictable and sustainable Opportunistic cross-pollinationOpportunistic cross-pollination Review and collaboration of others’ plans & Review and collaboration of others’ plans &

technical approachestechnical approaches

# # 4242

Prospects for Further CollaborationProspects for Further Collaboration

CEC & IWS might want to apply some of our CEC & IWS might want to apply some of our findingsfindings We can helpWe can help Perhaps there’s some important sustainable Perhaps there’s some important sustainable

applied research NPS could staff to supportapplied research NPS could staff to support

CEC & IWS might want to continue funding CEC & IWS might want to continue funding NPS to do what it does wellNPS to do what it does well Academic work by professors Academic work by professors Theses by students, but opportunisticallyTheses by students, but opportunistically Opportunistic cross-pollinationOpportunistic cross-pollination Review and collaboration of others’ plans & Review and collaboration of others’ plans &

technical approachestechnical approaches

Or, we could declare “success” and stopOr, we could declare “success” and stop

# # 4343

ConclusionConclusion We have laid the groundwork for much more intelligent, We have laid the groundwork for much more intelligent,

efficient, and effective collaboration networksefficient, and effective collaboration networks– Model-basedModel-based

– Bits flow by valueBits flow by value

– Individual operators establish conditions of interestIndividual operators establish conditions of interest

– Rich semantic tracks underlie models of tracked entitiesRich semantic tracks underlie models of tracked entities

– Standardized forms of RST will power much improved Standardized forms of RST will power much improved information sharing throughout defense & law enforcementinformation sharing throughout defense & law enforcement

We have had an extremely productive collaborationWe have had an extremely productive collaboration

We’ve learned that $100K is below critical mass for We’ve learned that $100K is below critical mass for staffing and conducting applied researchstaffing and conducting applied research

# 1 # 1 model-based communication networks, valued information at the right time (virt) & rich...

Documents

key results

outline overall vision

cois overall vision

filtering information

network slide

rst preparedpublishedpresented

key focus of w2cog

cma jctd