Semantic SOA: Key Technologies for DoD Net-Centric Computing. Sense, Share, Discover, Understand

Transcription

1 Semantic SOA: Key Technologies for DoD Net-Centric Computing Sense, Share, Discover, Understand NASA Earth Science Sensor Web Meeting February 14, 2007 Tom Velez, CEO John McKim, CTO Computer Technology Associates

2 Agenda Net-Centricity: The Promise vs. the Reality, and the Emerging Role of Semantics The Emerging Role of Web Services/Agents in DoD Sensor Webs WebAgents a Semantic Agent Framework for Geospatial Intelligence Near Term Application Time Critical Operations Future Vision Achieving the Promise via the Semantic Wave

3 The DoD Sensor Web Evolution Sneaker-nets Machine-to-Machine Semantic Interoperability

4 Net-Centricity: Promise vs. Reality The growth and nature of time critical threats is driving the DoD towards Network-Centric Warfare and upstream Multi- Sensor ( Multi-INT ) Fusion The promise of these ideas is that horizontal integration can shorten decision timelines by leveraging the dynamic relationships among: Multi-INT ISR Sensors regardless of platform/platform owners Decision Makers (strategic/tactical) regardless of their location Effectors (warfighters and operators) regardless of service SOAs enable interoperability and dynamic compositions of services across diverse tactical network architectures; intelligent agents can autonomously infer and execute actions to provide smart push services

5 Net-Centricity: Promise vs. Reality (cont.) However most existing DoD ISR sensor data systems lack semantics As a result, although Global Net-Centricity can make disparate data available anywhere at any time, people, not machines are still used to connect the dots Efforts are underway to change this picture: W3C/DAML Programs DoD XML Registry; Intelligence Community Markup Language (ICML); DoD Discovery Metadata Specification Horizontal Fusion Portfolio/Net-Centric Enterprise Services (NCES) Initiatives FEA Data Reference Models (DRM)-Semantic Interoperability Community of Practice (SICoP)

6 Semantics: Making Sense of the Data The real race will be won by those who create the most valuable configuration of knowledge in the shortest time. Semantic technologies make the meaning of data explicit by representing meanings, associations, and know-how (e.g. rules) about the uses of things separately from data and program code. This knowledge representation is called an ontology a runtime semantic model of information, defined using constructs for: Concepts Relevant to the Domain of Interest Properties and Attributes of Concepts Constraints on properties and attributes Instances of concepts individuals, threats, places (data, facts) Ontologies can make digital content (any form of digital media such as imagery, video, text, etc.) smart when annotated with metadata based on an ontology

7 More Metadata/Knowledge Modeling More Reasoning Capability* Ontology Spectrum *Source: DRM 2.0 Implementation Guide

8 The IC Establishes Virtual Sensor Webs Through Semantics *Source: Joel Gladding, SAIC

9 Event Attributes: Start Date Time End Date Time Type/Target Count Wounded Descriptive URLs Terror Event conducted in State Event Communication Attributes: Start Date Time End Date Time Type URL for Transcript Language Sample Terrorist Ontology Schema (1) received by (2) participated by (3) originated by responsible for (1) funded by (2) affiliated with (3) part of Terror Org. Attributes: Recruit Method Training method claims responsibility (1) directly involved in (2) coordinated leader of member of Organization occurred at occurred at State Org. NOTE: Lines with arrows indicate relationships, and lines without arrows indicate subclass spoken in spoken by City Academic Org Political Religious Charitable Corporate Financial Place (1) located in (2) capital of Person (1) faculty at (2) educated at Terror Agent listed in Watch list Agent State Agent (1) operates in (2) based at (1) friends with (2) family with (3) associated with (1) primarily lives in (2) operates in (3) national of Country

10 Strategic Semantic Challenge: Linking Ontologies* * Source: Mills Davis Semantic Wave 2006 Keynote Presentation

11 Illustrative DoD Net-Centric Efforts: The Emerging Role of SOA/Agents in DoD Sensor Webs

12 A Driving Net-Centric Challenge: Time Critical Targeting (TCT) Best to Worst Case: min (prosecute only); min (with BDA) Pr ocess JFC Guidance Prepare IPB Build Emer ging TCT Picture Construct Target List Target -Weapon Pair ing Battle Damage Assessment (BDA) min min min 4-45 min 3-46 min min Issues Manual read of a large text plan; manual deconfliction for coalition ops Clients A2IPB Issues On Target Detection, Trafficability, Target History Analysis, EBO considerations Subtasks Task ISR Sensors, 5-30min Redeploy Sensors, min Fuse Multisource INT, 15-45min Additional Analysis, 0-15min Mensuration, 1-20min Clients TCTA JSWS JTAT Issues Extensive check for Collateral damage considerations Subtasks Nominate Targets, 1-1min Target Deconfliction, 0-20min Prioritize Targets, 1-3min Select Targets, 0-1min Coordination, 1-20min Issues Range of available weapons Subtasks Weather Effects, 0-1min Weapon Selection, 1-10min Route Analysis, 0-5min Package Construction, 1-10min Coordination, 1-20min Clients JEES AODA Subtasks Task ISR Sensors, 5-30min Redeploy Sensors, min Fuse Multisource INT, 15-45min

13 Key DoD Transformational Themes TCT Operations from hours < 10 minutes Fixed Targets Time-Sensitive Targets On the Move Stovepipes Synchronized Effects-Based Ops Deliberate Planning Dynamic Plan/Re-Plan On the Fly Theater Global Focus ISR Constellations FIND ASSESS FIX ENGAGE TARGET TRACK Weapon Systems

14 Global Net-Centric Surveillance and Targeting To deliver decision quality intelligence information to the Warfighter by leveraging automated NRT correlation/fusion of airborne and national Multi-Int data to detect, locate, identify, and assess previously undetected/unlocated mobile targets (initially SAMs, SSMs) Application of Technology Agent-based data access Model-based upstream fusion Collaborative visualization Enables Critical Process Improvements Navigation of Stovepipes Upstream Fusion Specific Target ID Dynamic Re-tasking Nominations Integrated Picture Producing Reduced decision cycles Ambiguity reduced and precision increased Unclassified / For Official Use Only

15 Raytheon Team Proprietary Distributed Common Ground System (DCGS) Google -like Intelligence Sharing Sensor-to-Shooter Support with Full Integration of Fire Control/C2 Systems ACS Global Hawk ONI WPC Predator 1 USMC MEF Predator 2 JSTARS Firescout Army UA Tactical Sensors data added to Sensor Grid Tactical IMINT/ SIGINT, F/A18 SHARP, Pioneer, Firescout, Hunter HUMINT Mobile DCGS-A (UE) F/A 18 SOF (Seals) DCGS-N DCGS-N DCGS Integrates Theater and National Intelligence National IMINT and SIGINT U-2, GH, Predator, ACS, RJ, JSTARS, etc. Multi-INT Visualization provides COP to all forces ISR Sensors (National, U-2, GH, Predator, ACS, RJ, UGS,etc) Blue Force Tracking GCCS-M, ASAS Red/Blue OB

16 DCGS- CONOPS BDA RETASKING ACCESS WEAPONS ON TARGET ENGAGE TCT CELL TARGET Multi-INT EXPLOITATION U2 GLOBAL HAWK PREDATOR JSTARS RJ National POSITION DATA SENSOR DATA COLLECTION PLAN MTI SIGINT FIND/TRACK SIGINT Networks INTEL Databases SIGINT MTI ISR ASSET DATA Multi-Source Database (MSDB) 2D / 3D VISUALIZATION AND RETASKING TARGET NOMINATIONS SYNC MATRIX ACCURATE TARGET GEOLOCATION U2 TCT RETASKING GLOBAL HAWK FIX PREDATOR JSTARS Sensor Planners PRIMARY IMAGERY FIX FIX SIGINT MTI ISR ASSET DATA MSDB

17 DCGS Metadata Catalog The DCGS Metadata Service capabilities include Search catalog Catalog data Subscribe and Unsubscribe Agent-Based Alert Notification service (sentries, alert types, etc.) The Metadata Catalog Query capabilities include Geospatial search point radius, polygon, spatial operators Temporal search start and end times Contextual search exact match, and, or, either or, wild cards, etc. Compound search combinations of the above queries

18 DCGS Metadata Catalog Contents Air Tasking Order (including Special Instructions) Airspace Control Order Named Areas of Interest Prospective Enemy Courses of Action Target Areas of Interest Suspected Time Critical Target (TCT) Infrastructure Areas Suspected Launch Basket Infrastructure Areas High Value Target locations Lines of Communication Restricted Areas, No-Strike Zones, No-Fly Zones Forward Line of Troops Fire Support Coordination Line Imagery (IMINT), Multiple All Source Intelligence (MASINT), Signals Intelligence (SIGINT)

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20 Space-Based Radar (SBR) Constellation CONUS Crosslink PLAN FIND FIX TRACK TARGET ENGAGE ASSESS Broadcast Products. 3 Other Sensors DTED Wide Area Search HRR GMTI/ ISAR Engage SAR MC2A Theater DCGS Reachback NGA 3-D Map Wide Area MTI Focused HRR MTI ISAR Minutes HRR-MTI Overlay. SAR DCGS JAOC CONUS THEATER COMMUNICATIONS Attributes: Theater-wide strike support Responsive, timely collections Precise tracking/geolocation Pre/Post Strike Imagery

21 Space Radar C2 Environment Collection Requirements Exploitation Requirements Users Requirements Management RMS Doctrine Tactical Analysts Requirements CTA Manager Rules Target Requirements Approved Tasking Cyclic Or One-shot Tasks Exploitation Requirements High/Low Priority Tasking Products Imagery/ Video Mission Management Mission Planning Processing and Exploitation Image Processing SOC Scheduling Packaging Compress S/C and Instrument Commands Daily Plans High-Speed Data Constellation Weekly Plans

22 Experimental Agents for SBR C2 TCS RELAY SOH, SC/CMD, C2 DATA GMTI//SAR//HRTI Constellation DATA COLLECTION Spacecraft Operations Center (SOC) Requirements Manager SOH, SC/CMD, C2 DATA Mission Planning/ Scheduling Monitor New Collection Request Monitor 1. Agent Central determines if semantically equiv alent Tasking product already av ailable. Negotiates Authority with User agent. (CTA) Agent Agent RECEIVE TASKING (We need Product) PROCESS 3. Agents can monitor SR processes and ground/air-based collections in support of multi-int fusion and optimization EXPLOIT of mission planning (e.g. eliminate redundant tasking). Agent SBIR/ONIR Event? 2. Dynamic Self-cueing. Based on Overhead Non-Imaging Infrared (ONIR) ev ents, generate and submit SR collection requirements, w hich inv olv es the capability to screen ONIR events, SR capability matching, comparing ONIR with other senor data, and learning which cued events are most effective. Monitor Is Product X available? Theater DCGS Can You Accept Product Y? Need Product X? DISSEMINATE Finished Product (SAR, MTI, etc)

23 Web Based SBR Agent User Client Agent Tool Bar Notification Agent Space Radar Agent Imagery Agent Resource Agent UI Agents Task Agents Plan Agents WMS Layer Ontology Agent SR Imagery Request WebAgents Agent Container and HTTP Communication to Server

24 Experimental Architecture: SBR C2 Windows, Linux, Mac OS Space Radar Requirements Manager Web-based Client Agent Container ACL WS Pushed Ev ents/data Java Windows, Linux, Mac OS Space Radar Tactical Web-based Client Agent Container Java ACL WS Pushed Ev ents/data Maps/ Data KML Collaboration Mission Folders Google Earth Information KML Maps/ Data KML Windows FalconView C# SR Agents FalconView Plugin Pushed Ev ents/data ACL WS WFS WCS WMS WS Maps Imagery Features ACL WS Linux, MacOS, Sun Solaris WFS WCS WMS WS Unclassified Maps/News/ Features/ Weather Sites NGA Holdings WFS/WCS/WMS/WS WFS/WCS/WMS/WS Maps Imagery Features Agent Container Java C++ Space Radar Agent Experiment Information Server Signal Intelligence Feeds SIGINT WS/HTTP/Proprietary WMS Web Map Serv ice WFS Web Feature Serv ice WCS Web Cov erage Serv ice WS Web Serv ices (SOAP, WSDL) KML Key hole Markup Language ACL FIPA Agent Communication Language FIPA Foundations f or Intelligent Phy sical Agents DCGS Military Databases Classified Data Feeds

25 Dynamically Discover and Add OGC compliant Web Map Services (WMS) Ne w WM SL ay er d de d a

26 WebAgents: An Agent Framework for Actionable Geospatial Intelligence

27 CTA WebAgents Platform WebAgents Developed as element of CTA AFRL SBIR Agents Compose Complex Web Services Utilizing OWL-S Agents Provide Intelligence to Net-Centric Sensor Webs Addresses issues typically associated with existing agent platforms Communications based on RMI, JINI, and sockets - can t easily go through firewalls. Not integrated with nor based on semantic web technologies Lack of language and machine interoperability Do not leverage Network Transparency benefits of SOA Not designed for performance Optimized for distributed performance in bandwidth constrained environments Can be readily configured to collect up-to-date SA information from diverse data sources based on the user s goals and mission ontology Supports autonomous operations during disconnected periods, anticipating user s information needs Supports advanced reasoning/collaboration services

28 WebAgents: Convergence of Key Processes, Standards and Technologies Geospatial and Sensor Standards Semantic Web WMS WFS WCS SensorML Sensor Standards Intelligent Processing SPARQL Agents OWL Neural Nets RDF-S RDF XSD XML WebAgents Sem antic Service Oriented Architecture Bayesian Nets Inference Rules KR SOAP WSDL UDDI OWL-S

29 WebAgents MAS Architecture Flexible and Efficient Agent Communication -Agents talk to other agents in peer-to-peer Web Services fashion. Service Request Locally memory calls Remote web services calls (synchronous & dual-channel asynchronous) WebAgents Platform Service Management ACL Requests Requests Message -Agents use Services Local (on same machine) memory calls Remote web services Agent calls Container URL large data file transmission (images, video, etc) Task Library Source Agents Broker Agents Optimizing of Communication Bandwidth -Resource Agents Monitor Comms and Machine -Offload processing onto servers -Communication of only Changed Data UDDI -Pass only metadata when possible Registry -Chipping of only needed detailed imagery and maps. Custom Agents Agent WSDL Descriptions Plan Library Directory Facilitator Service Agents Prioritized Scheduling of agents -Based on Java Prioritized Threading Model -Thread Pool Model -Prioritized Pre-emption Scheduling Scheduler Plan Agents FOL and DL Reasoner Notification Agents Agent Services Services Monitoring Agents Ontology Model -Reasoning OWL-S -Ontology Descriptions -Security based on WS-* specifications and flexible policy. -White and Yellow Pages Lookup (UDDI and semantic service registry) -Collaboration Via Databases Sharing Mission Folders Whiteboard Ontology And Instances

30 Near Term WebAgents Application: SOF CAS Operations

31 Current TACP/SOF Elements TACP Mission Direct Digital Link to OFP Litton MK VII LRF HARRIS AN/PRC-117F Targeting Support Applications FalconView/BAO/Maps/Imagery Provide Terminal Attack Control TACTER-31A

32 WebAgents: Virtual Sensor Webs for Warfighters Information Grid Engagement Grid Sensor Grid A/C - SOF-FO - CAOC User Interface Agents Agents Facilitate MMI, provide customized interface, facilitate intermittent comms, anticipate user needs Information Information Command & Control Information Control PGWs Tactical Sensors Information Control Data / Information / Knowledge Flow Adaptive Processing Control Weapons Control Sensor Data WDL Aimpoint Mediator Agents Anticipate User Needs Broker Agents Dissemination & Discovery Weapons Threats Source & Asset Agents UAV Tasking Targets Applications Agents Middle Tier Agents Source Agents BFT Agents support precision target coordinate generation, track weapon position, and uplink coordinates to PGW in flight Agents map cohesive views of battlespace for users. Agents support sensor tasking, BFT, intelligent dissemination, bandwidth management DPPDB Source agents support integration of domain/context knowledge

33 What Warfighters Need from Smart Systems* Information Management Help me manage my time, the data I need, the knowledge I m sent Reach back/intelligent Push/Chipping Handle the ISR requests that go to C2 nodes; help me get most relevant spot area imagery Locations of Interest Where are my buddies, where are the threats CoT Events Mission ICONS on the map plus, e.g., take picture of a suspect, then send picture and location as a CoT event Ice Fishing Wait to see what shows up, then provide me changes that occur History Channel What do we know about personalities, patterns of behavior, relationships, of objects I am observing? * Mobile users. Source: TSgt A. Yoshida (AFSOC), July 2005

34 AIMPOINT/WebAgents Architecture Windows, Linux, Mac OS Windows Client View AIMPT Web-based Client Agent Container Java Maps/ Data Collaboration Mission Folders Google Earth Information Maps/ Data FalconView C# AIMPT Agents FalconView Plugin Web Services WMS/WFS/WCS Linux, MacOS, Sun Solaris Linux, MacOS, Sun Solaris Linux, MacOS, Sun Solaris Server View WebAgents Platform Semantic Server WebAgents Platform Situation Server WebAgents Platform AIMPT Tactical Information Server (TIS) Web Services/Custom Interfaces WMS/WFS/WCS Data View SIGINT Unclassified Maps/News/ Features/ Weather Sites Military Reports NGA Holdings Military Databases Classified Data Feeds

35 Geospatial Semantics Agents GIS Layers Missions Ontology Alerts/Event Notifications Semantic WFS Features Connectivity Status

36 Dynamically Discover and Add OGC compliant WFS Semantic Features New Sem a ntic S earc h La yer Search Results

37 Dynamically Discover and Add Semantic Feature Metadata View Content

38 Dynamically Discover and Add Semantic Feature Metadata View Content

39 Future Vision: Achieving the Promise via the Semantic Wave

40 Semantic Interoperability: Dynamically Discover, Access, Fuse Multi-Source Intelligence Blue Force Tracking IF any threats < 1 KM from my position AND last know target ID = I454 in Baghdad area THEN notify me AND set current alert status = High Airport Database DAFIF FOL/DL-Reasoner Inference Engine Target Same Concept Person Same Instance ID=I454 Red Force Tracking Immigration and Customs CIA/DIA Reports

41 Real-Time Sensor Webs: Avoid Friendly Fire while Accelerating TCT 1 3 Divert to pre-determined safe areas Aircraft releases weapon Against enemy C2 Node Link 16/TTNT 2 Enemy C2 Node JSOTF agents, integrating data from multiple data sources, identify potential conflict and notify observers of coalition noncombatants moving into target zone; observers (or their agents) send wave-off command to weapon in flight 4 Agent-enabled JSOTF C2 Node Weapons safe area

42 Potential NASA Semantic Sensor Web Interoperability Framework Ocean Observation Sensor Web Automated Cueing And Notifications Space Based Sensor Web Web Services Semantic Broker Web Services Semantic Integration Web Services SensorML Ontology Semantic Broker Services: Search And Reasoning Web Services Earth System Models Support Integrated Demonstrations Semantic Publication/Subscriptions Web Services Web Browsers and Other Applications Sensor Analysis Models Allow Other NASA Applications to Tie Into Sensor Web

43 Systems that know Semantic Technology will be key to Achieving the True Value of Net-Centric Operations as well as Future Social Interactions?