Project Report on CEC Collaboration Rick Hayes-Roth & Curt Blais

1. Model-based Communication Networks, Valued Information at the Right Time (VIRT) & Rich Semantic Track (RST):Filtering Information by Value to Improve Collaborative Decision-Making Project Report on CEC Collaboration Rick Hayes-Roth & Curt Blais hayes-roth@nps.edu & clblais@nps.edu August 21, 2008

2. Outline • Overall Vision: Model-based Communication Networks, VIRT, and Rich Semantic Track • CEC/VIRT Project Prior Results and Accomplishments • 2008 Statement of Work • Recent Results • Where Do We Go from Here?

3. Outline • Overall Vision: Model-based Communication Networks, VIRT, and Rich Semantic Track • CEC/VIRT Project Prior Results and Accomplishments • 2008 Statement of Work • Recent Results • Where Do We Go from Here?

4. Overall Vision: Model-based Communication Networks, VIRT and Rich Semantic Track Common Track Semantics State-full Network Global Information Grid COIs COIs Future Present Present Present Future Shared World Models Future Past Past Present Future Past Past

5. Overall Vision: Model-based Communication Networks, VIRT and Rich Semantic Track Common Track Semantics State-full Network Global Information Grid Valued Bits Valued Bits Future Present Present Present Future Shared World Models Future Past Past Present Future Past Past

6. Outline • Overall Vision: Model-based Communication Networks, VIRT, and Rich Semantic Track • CEC/VIRT Project Prior Results and Accomplishments • 2008 Statement of Work • Recent Results • Where Do We Go from Here?

7. Key Results Previously Reported • Advanced theory and implementation of Valued Information at the Right Time (VIRT) and Rich Semantic Track (RST) • Demonstrated significant reductions in bandwidth from 2-5 orders of magnitude in initial studies to 45-90% in CEC-specific simulated message streams • Developed simulation framework for studying VIRT and RST • Prepared/published/presented several theses and papers • Established VIRT as key focus of W2COG • This work now embraced by DISA and JITC • Helped PACOM start up CMA JCTD around sharing of rich track information; leading design and development of the MIEM • Advised Joint Track Management (JTM) Architecture Working Group and now CNDE (Consolidated Navy Data Enterprise) • Conducted computational analysis of CEC simulated message streams • Mapped CEP-to-Track-User message elements to abstract Rich Semantic Track model • Defined initial measures of performance for evaluating VIRT applications

8. CEC/VIRT Message Stream Analysis • Simulated message streams provided by JHU/APL • Focus on application of VIRT at an intermediary node (“VIRT Track User”) between the CEC network and the general GIG network • Value determined by “GIG User” Conditions of Interest (COIs) relating to expected position and velocity, track identification, and engagement status • Computed bit traffic reduction of 45-90% in short duration, highly dynamic air tracking scenarios • Demonstrated capability to “tune” bit traffic flows based on user-defined thresholds in accuracy of estimations

9. CEC Message Stream Analysis • Can achieve significant reduction in bit traffic from attention to user information needs (COIs) • Demonstrated mechanisms for enabling CEC message traffic to be filtered for non-CEC users • Opportunity for further research into user-specified COIs and message processing using more of the message content (e.g., certainty and accuracy data)

10. Outline • Overall Vision: Model-based Communication Networks, VIRT, and Rich Semantic Track • CEC/VIRT Project Prior Results and Accomplishments • 2008 Statement of Work • Recent Results • Where Do We Go from Here?

11. -  +  SOW Adjusted to $100K Budget  • Develop and analyze strategies for optimizing distribution of track data among distributed nodes, with participating nodes having a variety of needs for precision and timeliness • Enhance the initial simulation environment to create and analyze a variety of usage scenarios, to support the analysis and to improve the demonstration of results • Improve the methods and tools available for describing what information is valued, selecting it from available track data, and providing it to interested clients • Help assure that CEC approaches work harmoniously with other DoD initiatives and other DHS agency efforts that require air tracking capabilities 

12. Outline • Overall Vision: Model-based Communication Networks, VIRT, and Rich Semantic Track • CEC/VIRT Project Prior Results and Accomplishments • 2008 Statement of Work • Recent Results • Where Do We Go from Here?

13. Demonstration Software • Web-based application to enter VIRT conditions of interest • Server-side software checks COIs against input data streams (XML) and generates alerts to registered clients (sent via e-mail, cell phone text message, or with other transport mechanisms possible)

14. User Registration and Login User Registration User Login

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

16. Editing a COI

17. Subscribing to a COI

18. Ongoing Development • Adding track stream generator • Adding geographic visualization • Adding predictive event recognizer: • At some future time t, where t < Tmax, determine if the distance between object A and object B is less than (or, the alternative, greater than) some threshold distance D, and where the probability of this assertion being true is greater than 1 – α, where α is a given significance level. • Integrating knowledge base representation of Rich Semantic Track • Using RST as a translation hub among diverse track data models

19. Progress on Standardizing RST: The Vision CMA Maritime Information Exchange Model CEC Track Messages • Rich Semantic Track • conceptual hub for interchange and automated • reasoning Joint Track Management Data Model Other Track Data Models

20. The Rich Semantic Track Model • Track • Beliefs • Identity and Characteristics • Dynamic State at Time T • History of states (past “track”) • Predicted states (future “track”) • Meta-Information (applicable to each element of belief) • Evidence • Inferences • Error and uncertainty estimates • Temporal qualifications • Spatial qualifications The top-level conceptual hierarchy for Track. The full hierarchy has more than 125high level concepts.

21. MIEM Objectives Dynamic nature of all quantities (potentially any belief/value can vary in time) Inexact information (“roughly 30 feet long”) Relative information (“a mile from the leader of XYZ”) Conditionals (“all facilities open on Thursdays”) Complex queries (“return last five ports of call for vessels flying Chinese of Korean flags and within 3 days of US costal waters”) Pedigree (“position was derived from AIS message A, ELINT data B, and HUMINT source C, using inference rule D”) Belief conditions (“value is considered 85% reliable”, “value was provided by source S”, etc.) Behaviors, states & histories (“the events make up an overall fishing voyage”) • Share actionable maritime intelligence in a net-centric way: • Simple/raw data exchange • Fusion output representation • Advanced analytics support • Establish unambiguous maritime lexicon that: • Supports communication among data providers and consumers • Embodies broad expertise covering the extent of the maritime domain • Leverages existing models and knowledge bases • Enable communications from/with future services and capabilities • Permit extension to more sophisticated data without change to existing systems • Import partners’ databases and export to them with efficient translators

22. Levels of Value Added Information

23. MIEM is a Nascent Standard • CMA JCTD transfers it to MDA COI in Oct. • Navy and USCG committed to MIEM • NIEM (DHS) also committed to MIEM • RST concepts should propagate more widely

24. Primary 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

25. Extended Base Support Types • Base Metadata • Extended Metadata • Voyage Type • Track Type • Kinematics Type • Boarding Type Vessel Vessel Model Details Identifiers Characteristics Documentation Movement State Affiliations • Name • Call Sign • MMSI • IMO • ISSC • NOA • Safety Cert • Movement Segments • Ports of Call • Voyages • Equipment • Cargo • Events • Persons • Cargo • Facilities Capabilities Physical Miscellaneous • Range • Speed • Cargo • Size • Structure • Design • Home Port • Classification

26. Ports Of Call Vessel Person Persons On Board • Name • Citizenship • Passenger Reference • Crew Member Reference Voyage • Number • Origin/Destination • Type • Use Type Port Of Call • Time of Arrival • Time of Departure • Port Identifier Typical Vessel Relationships 1 1 1 has-a has-a has-a 0..* 0..* 0..* 1 on-board related-to has-a 0..* • Embedded (“has-a”) • Associations (“on board”) • - Strong, explicit relationships • - Defined Association Types • Affiliations (“related-to”) • - Weak relationships between entities • - ID/IDREFS references

27. Support Types Extended Base • Handedness • Gender • Associations • Base Metadata • Extended Metadata Person Person Model Details Identifiers Physical Characteristics Details Whereabouts Affiliations • Name • Citizenship • SSN • Height • Weight • Color • Gender • Marks • Family • Organization • Employment • Birth • Death • Biometrics • Events • Work • Current • Residence • Temporary

28. Extended Base Support Types • Base Metadata • Extended Metadata • Port Associations • COTP Region Port Facility Facility Model Details Identifiers Physical Characteristics State Affiliations Documentation • Name • BE Number • Type • Location • Accessibility • Sub-Facility • Parent-Facility • Cargo • Contractors • Organization • Government • Staff • Certifications Physical Characteristics Identifiers State • Depth • Max Vessels • Number Docks • Cargo Capabilities • Port Name • Code • Type • Vessels

29. Extended Base Support Types • Base Metadata • Extended Metadata • Goods Item • Manifest • Associations Equipment Shipment Identifiers Characteristics Affiliations • Bill Of Lading • Booking Number • Identifier • Weights • Measures • Declared Values • Route • Equipment • Goods Items • Involved Party Cargo Model Details Status • HazMat • Status • Biometrics • Events Identifiers Characteristics Affiliations Status • Number • Identifier • Type • Security Devices • Weights • Measures • Temperature Controls • Owner • Shipment • Vessel • Facility • HazMat • Empty • Events

30. Extended Base • Base Metadata • Extended Metadata Event Threat Incident Of Interest List • Severity • Casualty Details • Name • Start/End Time • Description • Category • Type • Location • Affiliated Entities • Capability • Intent • Description • Level • Opportunity • Threatening Entity • Target of Threat • Name • Publisher • POC • Type • Items Abstract Types: Threats, Of Interest Lists & Events

31. AddressType NameType POCType VesselType EventType PersonType Extended Metadata Basic Metadata • Information Source • Analysis • Anomaly • Data Rights • Pedigree • Vulnerability • Affiliations • Comments • Validity Time • Confidence • Completeness Base Types with 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

32. How do we Use the MIEM to Describe Situations? An Illustration of Vessel State and Entity Relationships 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.

33. Example Vessel State andEvent Relationships Vessel State Part-of relationships (Embedded) Voyage Details Explicit relationships (Associations) Track Persons On Board Weak relationships (Affiliations) Movement Details Ports Of Call Passenger Port of Call Port of Call Port of Call Port of Call Crew Ownership Flag Equipment Port Arrivals(4) Person 001 Person 002 Port Departures(4) Equipment Change Sold Re-flag Person n Voyage Start/Stop/End Voyage Manifest Boarding Event Details

34. Example Vessel State andEvent Relationships (XML) The Port with id “PORT0001” is defined as being the port of “Portland, ME” in the country “USA” with a defined set of properties. 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” Relate entities to the underlying Events that cause them: Change of Ownership causes the state of the “owner” to change Capture simple concepts simply – vessel name is “MV1” 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”

35. Final Technical Observations • Powerful language for expressing actionable intelligence documents • Extensible – Model can be extended to produce application specific schemas

36. Status • Beta test version released February 8, 2008 • Incremental Design review held June 10-11, 2008 • Version 1.0 product release scheduled September 19, 2008

37. Beta Testers • CMA JCTD -Cargo • MASTER JCTD • MDA DS COI • MAGNET/MIFCPAC • CMA – Singapore • Seahawk • NSA - RTRG • NAVAIR - Tampa Bay Maritime Domain Awareness System (MDAS) • TTCP – Maritime AG 8 International MDA • SPAWAR Charleston - MDA Non Classified Enclave

38. Transition to MDA-NIEM Planned FY09 • Transition the MIEM to the DHS/DOJ National Information Exchange Model (NIEM) as a new Maritime Domain • Transition the MDA DS COI DMWG to an MDA Data Management Group • The MDA-DMG becomes the Maritime Domain owner MDA – Data Mgt Group Maritime Domain Owner

39. MIEM Conclusion • MIEM provides a language for expressing actionable intelligence • Rich semantics for pragmatics of “tracking” • Supports higher-levels of analysis • Directly supports resource cueing & interdiction • MIEM’s focus on document sharing supports a vital vision of collaborative intelligence • The NIEM will incorporate the MIEM directly & as a guide for higher-levels of Core value • MIEM elements and approach should benefit many intelligence suppliers and consumers

40. Outline • Overall Vision: Model-based Communication Networks, VIRT, and Rich Semantic Track • CEC/VIRT Project Prior Results and Accomplishments • 2008 Statement of Work • Recent Results • Where Do We Go from Here?

41. Observations about our Collaboration • CEC & IWS have provided generous support • NPS is good at several things • Academic work by professors • Theses by students, but opportunistically • Applied research if predictable and sustainable • Opportunistic cross-pollination • Review and collaboration of others’ plans & technical approaches

42. Prospects for Further Collaboration • CEC & IWS might want to apply some of our findings • We can help • Perhaps there’s some important sustainable applied research NPS could staff to support • CEC & IWS might want to continue funding NPS to do what it does well • Academic work by professors • Theses by students, but opportunistically • Opportunistic cross-pollination • Review and collaboration of others’ plans & technical approaches • Or, we could declare “success” and stop

43. Conclusion • We have laid the groundwork for much more intelligent, efficient, and effective collaboration networks • Model-based • Bits flow by value • Individual operators establish conditions of interest • Rich semantic tracks underlie models of tracked entities • Standardized forms of RST will power much improved information sharing throughout defense & law enforcement • We have had an extremely productive collaboration • We’ve learned that $100K is below critical mass for staffing and conducting applied research