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Fusion Based Knowledge for the Objective Force: A Science and Technology Objective Presented April 3, 2003 By Barbara D. Broome ARL Data Mining Working Group Army Science Board* Estimates of Technology Readiness for Select Fields Technology Readiness Levels Enabling Technologies Aided ATR Smart Portals to push pull Mobile Wireless (pagers, PDA) Malicious Mobile Code Visualization - Presentation Data Extraction Virtual environment Automatic routers, priorities 2004 3 6 6 1 4 6 3 5 Data fusion, information fusion 2 3 Secure Intelligent Agents Encryption and authentication Exploitation Algorithms and assist RTIC Future Internet Individual Soldier Tech. Collaboration Technologies Sync Distributed Secure Data base Secure Access Technology Biometrics Translingual language transcription Soldier Education Associates Next Generation Internet 2 4 2 5 6 4 6 4 3 4 6 6 6 5 7 2 8 9 8 9 7 8 6 8 7 9 *ASB Study - Knowledge Management and Information Assurance, dtd 09/01 2008 3 9 9 2 7 8 6 8 Commercial 2 9 7 3 6 8 6 5 7 6 2 9 5 8 5 5 7 7 5 9 Joint Directors of Laboratories (JDL) Fusion Levels Level 0: Sensor-level target identification - Processing raw data near the sensor Level 1: Where is the enemy? (Multi-sensor correlation) - Multi INT Correlation for highly detailed Enemy Situation ---------------------------------------------------------------------------- Level 2: What is the enemy doing? - Aggregation for COP - Interpreting activities in context - Develop hypotheses about current ECOA - Cluster analysis - Trend analysis - Association rules Level 3: What are the enemy’s goals? - Future ECOA’s - Predict Intent and Strategy Level 4: How should we respond? − How do we redirect the ISR system to get better SU? DARPA Programs Related to Levels 2 & 3 Fusion Where What When Who Level 2: Situation Refinement Level 1: Object Refinement Why How Level 3: Global Threat Refinement How well Level 4: Performance Refinement DATA FUSION PROCESSING ENABLING TECHNOLOGIES physical objects individual organizations events Evidence Extraction & Link Detection specific aggregated environment & enemy tactics local Dynamic Data Exchange global enemy doctrine objectives & capability CoABS DAML RKF CPOF local global friendly vulnerabilities & mission Dynamic Tactical Targeting Battle Adv. Assessment & ISR Data Mgmt Dissemination Ref: DARPA IXO(SUO-SAA) Information Fusion Workshop, final briefing, 28 Feb 2002 options needs effectiveness battle theatre resource management local global Why We Need Fusion Information volume exceeds war-fighter capabilities to develop situational understanding required for planning and acting within the adversary’s decision cycle Echelon # Msg’s per hour* # full time Analysts, w/ workstations Latency for Level III Fusion 15 1 Hr Legacy Division 400-600 Future UA Bde 17,000** 0-6 (TBD) NRT (req) Future UA Bn 4,000** Zero NRT (req) Future UA Company 1,200** Zero NRT (req) * Current and estimated bottom-up sensor feeds; Top-down feed is much larger ** (Date) Sensor briefing from CG, USAIC&FH to Dir, UAMBL / MAPEX indicates an order of magnitude increase Reports Without Fusion Bde COP UE Bn COP Plus…Information from echelons above UA 170K+ Reports/Hour Report count based on DCGS-A MAPEX results using Caspian Sea Scenario Reports generated from FCS EO/IR and COMINT Sensors only. Add MASINT sensors and reporting at UA goes to @ 600K/hour. Co COP 56K+ Reports/Hour 18K+ Reports/Hour PLT COP Mr. Hayward’s Brief, Force Operating Capability (FOC) S&T Assessment Review 6K+ Reports/Hour FBKOF: Overcoming Information Overload BARRIERS • • • • • Limited computational models Knowledge/algorithms scenario dependent COTS knowledge acq. technology slow Information sources poorly integrated Knowledge discovery tool limited APPROACH • • • • • Constrain the problem scope to UA Apply Blackboard architecture, Bayesian belief nets, and cooperative human-machine hypothesis generation and management Exploit DARPA rapid knowledge formation technologies to develop knowledgeintensive reasoning for interpretation Leverage Semantic Web techniques for source integration. Integrate and tailor COTS tools for directed knowledge discovery DELIVERABLES • SW for knowledge generation/explanation to answer CCIR’s in a timely manner • Ontology based information agents for objective force systems User-directed knowledge discovery tools • • Modeling and simulation tools Schedule Tasks FY03 FY04 FY05 FY06 FY07 • Baseline / Assess Knowledge tools and Fusion Algorithms 2 3 4 • Knowledge Acquisition 2 3 4 3 4 • Mining-Component Development • Knowledge Infrastructure Development 3 • Modeling and Simulation Support • C4I experiments and evaluations • Transitions Decision Points 1.7 2.1 2.2 4 5 Semantic Web Concepts Providing a Knowledge Environment (Agents and Ontologies) Interfac e GOALS • • • • • • • • Data- DataDatabase base base OLAP Minimize burden on user – Automate well-structured problems – Support ill-structured problems Interface tuned to the task and to the user Task centered, not tool centered Support information push and pull Support collaboration Accommodate multi-modal data types Visualization tools to support understanding Smarter integration of sources DBMS Knowledge Base Fusion – Limit the number of required retrievals (bandwidth) – – Minimize exploration after retrieval (time constrained) Automate and personalize the process Interfac e Web Search Engine Ontology: formal description of the relationships among terms. Notional Blackboard Architecture for Fusion Module Levels of Analysis Answers to PIRs COAs and COA Fragments Relations between objects (command hierarchy, behavioral) Events &Activities Objects (equipment and platform-level entities) Knowledge Sources Blackboard Plans KS • History : • Doctrine • Terrain & Weather • Activities KS • Force Structure : • Commo Patterns • Tactics • Terrain & Weather Sensor-Data Fusion KS : • Platform & Equipment Classification • Terrain & Weather CONTROL Agents-Based What are they? (ATL) • • • • -- Huhns The concept of software agents represents a new way of applying artificial intelligence techniques such as machine reasoning and learning. Software agents are computer programs designed to operate in a manner analogous to human agents. Human agents, such as real-estate agents, carry out tasks on your behalf using expertise you may not have. Software agents carry out information processing functions in the same manner. Agents can be thought of, in software engineering terms, as a step beyond the objects of object-oriented programming. Whereas objects are passive entities that must be invoked to execute, agents use AI mechanisms such as machine reasoning to actively operate as autonomous entities. Research has shown greatest utility in multi-agent applications is information mgmt. How do they help? • • • • • Active, persistent sw components that perceive, reason, act and communicate Huge problem broken into small components Much can be handled in parallel rather than serially Reflect changes in priorities without coding changes Technology is coming of age Many web applications [6, 9]: mediator, personal assistant Source Interface Agent Functionality • • • • • • • • • • Filter Monitors Alert Retrieve – pull Disseminate – push Mediate across legacy systems Intruder detection Policy enforcement Adapt to the user priority Adapt to the environmental changes Brigade level DCGS-A Data Store Single-INTs COMINT ELINT MASINT Imagery Images/ Video/ Audio MTI HUMINT Other Multimedia Open Source External COPs (above/below/beside) COP COP COP COP COP MIDB Blue Asset Mgmt Terrain Weather Targets CCIR/ IR/ OPLANs Alert/ Search Criteria All Source Fusion (ASFDB) Units Pieces of Equipment Facilities Events Individuals Organizations And their interrelationships PROBLEMS • Agents new, few success stories and limited developmental environments • Present complex parallel processing paradigm • Issues of teaming, security, mobility, efficiency • Establishing optimum ontology size/approach • Integrating ontologies across heterogeneous sources Ontology: a formal description of the relationship among terms. Ontology-Facilitated • Information heterogeneous (type, syntax, semantics) • Heterogeneity of semantics results in conflicts (naming, scaling, confounding) • Ontologies explicitly describe information sources • Identify and share formal descriptions of domain-relevant concepts • Identify classes of objects and organized them hierarchically • Characterize classes by the properties they share • Identify important relationships between classes Brigade level Mediator Agent DCGS-A Data Store Single-INTs Fusion Prioritzer Reasoner Agent Commo Module Agent Ontology COMINT ELINT MASINT Imagery Images/ Video/ Audio MTI HUMINT Other Multimedia Open Source External COPs (above/below/beside) COP COP COP COP COP MIDB Blue Asset Mgmt Terrain Weather Targets CCIR/ IR/ OPLANs Alert/ Search Criteria All Source Fusion (ASFDB) Units Pieces of Equipment Facilities Events Individuals Organizations And their interrelationships Providing User-Directed Knowledge Discovery Tools • • • • • • • • • On Line Analytical Processing (OLAP) emerged in the early 90’s (Inmon, Codd) Multi-dimensional data structure Better (more flexibly) address decision process (forecasting, time-series analysis, link analysis) More natural & efficient storage and retrieval mechanism Provides a mechanism for accommodating time and space Support drill down and roll up functionality ANALYZING THE DATA Flexible graphical interface Commercial Product Natural Transition to Data Mining Total Cost X Disaster Type PROBLEMS • • • • • Representation of space and time Complexity of user interface Inefficiency of algorithms Difficulty in extending functionality Difficulty in modifying the structure Team Members Information Agents Data Mining Tim Hanratty Joan Forester John Dumer Ann Brodeen George Hartwig Mike Evans Mario Torres John Raby Sam Chamberlain Ed Measure Partners and Leveraged Programs • • • • • • • • • • CECOM/I2WD Army G2 (Woodson / Walsh / ISR Working Group) Huachuca (Schlabach – Cahill) ADA CTA (U W Fl, UMD, SA Tech, Ohio State?) ARMY HPC Program (Namburu/UMINN, Data mining) ARL CENTERS OF EXCELLENCE (Evans CAU, Data mining) PENN State (Yen, Teaming Agents) C2CUT and Warrior’s Edge DARPA: Kessler (PBA Seedling); Taylor (ATA); Kott (AIM); RKF; Burke (DAML, CoABS Grid) ENDORSEMENTS: BCBL-H; BCBL-L; PM DCGS-A, PM IE, PM FCS FY03 Deliverables FY03 : (1) Work with CECOM and the user community in conducting a knowledge audit to design the Human-Computer Interface (HCI) and identify the fusion tasks most likely to be useful to the user. (2) Develop a small prototype Knowledge Environment (KE) that uses agent techniques to access the two highest priority data sources. This will establish a baseline system on which to build in out years, demonstrate our initial concept of the use of ontologies by the KE agent communities, and provide a mechanism for integrating CECOM’s fusion modules. (3) Conduct an internal demonstration of the baseline system to support refinement of the HCI/KE concepts FY04 : (1) Integrate two more data sources into the baseline system to assess the extensibility of the infrastructure and provide the CECOM fusion module access to a greater variety of data sources. (2) Develop and populate a prototype multi-dimensional data structure for user directed data mining or knowledge discovery (KD). This will allow us to explore the use of user-in-the-loop fusion tools to supplement CECOM automated fusion techniques. (3) Conduct an internal joint CECOM/ARL demonstration to refine the HCI and KE concepts. FY05 : (1) Modify the KE system architecture, based on the FY04 evaluation and integrate 5th data/information source. (2) Jointly demonstrate to DCGS-A and user communities the integration of CECOM’s fusion algorithms, the userdirected KD tools and 5 data sources. This provides a formal review for the targeted transition system developers (FCS/DCGS-A) of the refined approach at a point when all the required components are in place. FY06 : (1) Finalize user-directed mining scripts and system architecture, based on FY05 evaluation. The goal will be to simplify access to the KD tools. (3) Develop information agents to support I2WD fusion task. These agents will be directed toward increasing the efficiency and effectiveness of information push/pull. (2) Internally demonstrate automated cross-source integration using the enhanced agent environment and work with CECOM to evaluate and enhance the system’s functionality. FY07 : (1) Finalize system development, based on FY06 evaluation. (2) Jointly conduct the final system demonstration and evaluation to support system transition to FCS LSI contractor, PM-CGS, and PM-IF. Progress Task Accomplishments Remaining Conduct Baseline Knowledge Audit of UAS2 • Interviewed SME’s • Extracted data sources from DCGS-A ORD • Participation in G2’s ISR Working Group • Integrate SME modifications • Document • Participate in Follow-on MAPEX Prototype HCI for the S2 • SA-Tech developed Goal-Directed Task Analysis • Code/evauated against user requirements Configure Info Mgmt for Agent-Based Information Infrastructure • • • • Evaluated ontologies for weather/terrain/red Held IMPACT class Installed CoABS Grid (operational) Installed CAST agents to monitor unit movement on DaVinci map as client • Installed EMAA (operational and tested) • EMAA training • Develop wrappers for ASAS-Lite, IMETS/IWEDA • Choose/extend ontologies • Develop agent functionality • Develop API for fusion sw Develop User-Directed Data Mining functions • Installed internal MDDB tools (Oracle Express, Oracle 9i, other?) • Held Data Mining Workshop (WSMR) • Reviewed CAU demonstrations and proposal • Data Mining Follow-up (U MN, Apr 3) • Identify/mod/develop demo functionality (Climatology to weather, spatial displays, model validation) Develop Evaluation Methods • Worked with SME’s to identify metrics • Design pilot study • Develop data collection sw • Integrate pilot with demo Demonstrate integrated components • Identified and ordered hardware requirements • Identified software requirements • Order remaining sw requirements • Integrate components • Conduct demonstration Action Items from Last Meeting • Provide to UMINN and CAU actual meteorological measurements, along with requisite format information, for a 2-3 week period in an area for which we have corresponding model data. (Passner/Raby) • Upon receiving the measurement data, initiate a data mining effort to predict visibility, identify air mass clusters and their movement, and identify associations between measurement parameters and accuracy of forecast predictions. (Kumar/Shekhar/George) • Coordinate a follow up meeting to discuss the results of initial data mining findings on the measurement data in the late March timeframe. Jon Mercurio can provide a meteorology tutorial for the data miners. Hopefully this can be held at one of the Universities to involved more faculty and students in the discussion. (Forester) • Contact McWilliams for more detail on an upcoming weather data mining workshop in the Washington area. (Broome) • Identify weather data overlays that can be displayed on the FBKOF architecture in August and agree on a format for information exchange. (Evans/Hanratty) • Follow up on Shekhar research on spatial data mining and its application to the spatial data requirements for Online Analytical Processing. (Broome) Data Mining Issues • Incorporating remote sites is extremely time consuming, and as unplanned meetings arise, cross-site coordination is the first thing to go. – – – – • Establishing a baseline system this FY – – – – – – – • Impacts data mining effort most VTC/conference calls not enough ARLpartners may help Occasional one-site meetings critical, especially this year as everyone is learning their role Topic: Weather, terrain, MAPCUBE, intrusion detection Function: Mining forecast errors, micro-scale weather feature clusters, short-term trends Location: Georgia/Ft Benning Agent Accessed Data source: IMETS, IWEDA, Other (GA, Nat’l Weather Service) Structure: Multi-dimensional or Flat File, Standard Database: Commercial or Inhouse Integration across partners: ATL, CAU, UMINN Expanding beyond the weather in out years (distinguishing between BED and FBKOF) – – – – – Terrain Time Space Friendly forces Enemy forces Data Mining Issues • Obtaining data – – • Integrating with Agents Software – – – – • Contract scope Location: Atlanta, WSMR, ALC, APG* Staff/students/postdocs Data Mining (hands-on) courses Papers/publications – – • What operating system: Windows 2000 What software: Oracle 9i, MatLab, Java, C++ Software handoff: Documentation, installation, demonstration Standard data structure Summer faculty/visits – – – – • Weather-related (BED) Scenario-related (Warrior’s Edge Demo) Venue Collaborations Supporting ARL Data Mining Initiatives – McWilliams, National Environmental Satellite Data Information Service Data Mining Workshop on extracting information from large heterogeneous data sets. Summary • Goal: Facilitate quick decisions that fully leverage the huge volumes of information that the UA will receive. – Includes, but goes beyond, weather data • Proposed relatively modest software readiness levels, due to difficulty of the task, but driving to get a transition: – PM DCGS-A demonstration in 05, with a transition decision point in 07 – PM IE demonstration in 05, transition decision point in 07 – Demonstration to FCS LSI 05, AMSAA transition decision point in 07 • Data mining resources far exceed initial expectations, but not all can be targeted toward FBKOF. • First year of agents development will receive a boost from related ARL programs (C2CUT, Warrior’s Edge), and can enhance source integration requirements for data mining applications • Strong support from user community – Need to tie work to that community, involve them in the process