Knowledge Enabled Services (KES) for Decision Support in Control Rooms.

1. gtdSISTEMAS DE INFORMACIÓN Knowledge Enabled Services (KES) for Decision Support in Control Rooms. The CESADS(KES) Case Study at ESA/ESOC. FR1.3-5I Javier Busto, GTD Sistemas de Información iCALEPCS 2005 Conference Geneva, 10-14 Oct 2005

2. Context Addressed Problem Methods & Solution Approach Results Conclusions Outline

3. ESA/ESOC Space Operations Center (Darmstad, Germany) ESTRACK Kiruna (Sweden) Redu (Belgium) Perth (Australia) Kourou (French Guiana) Malindi (Kenya) Villafranca (Spain) Context The European Space Agency (ESA), from its Space Operations Center (ESOC) located in Darmstad (Germany), is able to operate up to 15 Spacecraft (S/C) missions in paralel, using a worldwide network of Ground Stations (G/S) called the European Space Tracking Network,ESTRACK. During S/C Mission Control lifecycle, one of the most impacting problems is that of the Space Link (S/L) loss. New missions, specially low-orbit ones like ENVISAT, with high frequency of pass over the G/S lead to critical and knowledge intensive tasksto the mission control operators, posing a stressed and limiting scenario.

4. Context Addressed Problem Methods & Solution Approach Results Conclusions Outline

5. In particular: to build a Centralised ESTRACK Anomaly and Diagnostic System (CESADS), to support ESOC/ESTRACK operators in its tasks. CESADS goals: End-to-End Space Link (S/L) Monitoring and Diagnostics Integrated Supervision of Independent Systems Status Mission Control System (MCS), G/S Monitoring & Control System (CSMC), Network Management System (NMS), Flight Dynamics (FOCC). S/L Knowledge Management: Capture, Maintain, and Apply the Knowledge of the experts. Reduce Operator Stress by providing with integrated monitoring, diagnostic and troubleshooting guidelines. In general: the problem of Decision Support in Control Rooms, and the Knowledge Enabled Services (KES) approach. Adressed Problem

6. Context Addressed Problem Methods & Solution Approach Results Conclusions Outline

7. Development Methodologies: Knowledge Engineering: CommonsKADS Software Engineering Agile methods: Rational, DSDM. Knowledge Technologies applied: For K.Representation (K.R.): For Domain Knowledge: Frame based models (leading to Ontologies) For Inference Knowledge: Declarative Production Rules For Uncertainty handling: Fuzzy Logic For K. Acquisition (K.Acq.) Manually, with domain experts supported by a forms-based K.Acq.Tool. Automatic learning, on the basis of enough historical log data and operators feedback over the generated alarms. For K.Application (K.App) Fuzzy Rule Base Reasoning Engines SW Tools& Architectures: Protégé: Frame&Ontology modelling & KB system platform. Jess & FuzzyJess: Reasoning engine Java2 Enterprise Edition (J2EE) platform RMI-based Observable-Observer pattern in Client-Server communications CommonKADS Methods & Solution Approach

8. Development Methodology: Combination CommonKADS + SW Agile Processes (RUP , DSDM) Context Analysis Concept Analysis Artifact Analysis

9. Analytic Task Classification Assessment Diagnosis Monitoring Prediction Knowledge-intensive Task Types CommonKADS classifies the knowledge-intensive tasks in two main groups: • Synthetic Task • Design • Modelling • Planning • Schedulling • Assignment For each one, cKADS provides reference Knowledge Model Templates.

10. abstraction Import Import knowledge Additional Initial abstraction Abstract Abstract parameters parameters knowledge What can verify the hypothesis? Imported variables In fuzzy terms Initial association Additional Observation Specify knowledge observation Normality observation hypothesis Detect Hypothesize Verify Abnormality observation diagnosis Indicators behaviour cause Fuzzy inference Possible causes for a problem (vector of hypothsis) knowledge knowledge Monitoring Diagnostic Template for Monitoring & Diagnosis tasksAdapted to knowledge techniques applied (fuzzy logic). fuzzyfication Fuzzy Sets Imported crispy variables

11. Knowledge Base (KB) Domain Knowledge Inference Knowledge Structure Behaviour Production Rules Case Base Experts Design Model: Pattern of a Knowledge Based System A typical KBS is composed of three main elements: • A Knowledge AcquisitionTool, • A Knowledge Base, having Domain Knowledge and Inference Knowledge, • A set of Problem Solving Methods (PSM), having appropriate Inference Engines, and effectively operating over the Aplication Domain (operational) data. Operators Knowledge Acquisition Tool (based on Protege) Operation Console Monitoring Tool Problem Solving Methods (PSM) Application Operation Database Inference Engines Inference Engines Operate over load Production Rule engine CBR engine Jess Tool

12. Development Phases: 3 iterations following the DSDM Agile Methodology approach. CESADS Concept CESADS Project Scope Operational Prototype Integration in context Tests&Validation Functional Prototype Scope & feasibility Techs demonstration. Design Prototype SW design & development • Design Prototype: • Single-mission (Envisat) • Simulated systems interface (CSMC, MCS, NMS, etc) • Integrated Monitoring • Rule-Based Inference • Operational Prototype: • Single-mission (Envisat) • Real systems interface (CSMC, MCS, NMS, etc) • Integrated Monitoring • Knowledge Management • Rule-Based Inference • Basic Diagnosis • CESADS Concept: • Multi-mission • Multiple Systems Interface • Integrated Monitoring • Enhanced Knowledge Mngt. • Enhanced Diagnosis • Variety of Inference Methods • Configurable • Scalable • Functional Prototype: • Knowledge Elicitation • cKADS Knowledge Model • Problem Solving Methods prototype, Inference Engines

13. Context Addressed Problem Methods & Solution Approach Results: The CESADS Testbed Prototype Conclusions Outline

14. Results: The CESADS Testbed Prototype System CESADS System CESADS S/L Anomaly and Monitoring Tool CESADS Management Tool CESADS Knowledge Acquisition Tool CESADS Server ESOC Systems CESADS Reporting Tool CESADS Clients

15. Knowledge Model: Behaviour & Workflows • Elements (CSMC, SEVT, NMS…) • System Parameters (Variables and Indicators) • Variables (throughput, delay, TM_Counter…) • Indicators (TC_Ind, throughput_Indicator, Network_Status…) • Rule Sets:

16. C-SLAM: S/L Monitoring & Analyis Client tool Synoptic View ESTRACK Top-Level Indicators Time and Events. Start-End of the Pass: AOS: Acquisition of Signal. LOS: Loss of Signal Event Log.

17. C-SLAM: S/L Monitoring & Analyis Client tool Indicators Table View, with Alarm System Explanation Table of indicators generated by the system. Fired Rules that produced the alarm, and input Parameters originating that firing. Form to acquire formal (ontology-enabled) feedback from the Operator.

18. C-KAE: Knowledge Acquisition Tool (1/8) Domain Abstraction Knowledge: Vocabulary of Fuzzy Sets & Fuzzy Terms Enabling the mapping of quantitative values of parameters, into qualitative fuzzy terms (fuzzyfication). Catalogue of FuzzySets for the abstraction in (Fuzzy Terms) of the Parameters (Variables&Indicators) E.g.: Fuzzy Terms for networkLatency concept.

19. C-KAE: Knowledge Acquisition Tool (2/8): Domain Structure Knowledge: Hierarchy of Elements & subElements in the domain. Modelling the hierarchy of Elements and its associated Parameters (external imported Variables and inferenced Indicators). 4 main subElements compose ESTRACK 6 main (highlevel) indicators have been define to monitor ESTRACK.

20. C-KAE: Knowledge Acquisition Tool (3/8) : Domain Structure Knowledge: Paremeters associated to the Elements Modeling the Parameters, using the fuzzy sets abstraction knowledge. Fuzzy Sets selected to represent the values of the parameters. Settings colors for display.

21. C-KAE: Knowledge Acquisition Tool (4/8) : Domain Behaviour Knowledge: Processes Modeling the unitary Processes (Import or Transformation/Reasoning) that can be executed by the system. Processes defined: 7 Data Import processes 6 Fuzzy Inference Processes Highest level SpaceLink FuzzyInferenceSystem process.

22. C-KAE: Knowledge Acquisition Tool (5/8): Domain Behaviour Knowledge: Workflows Defining the workflows of processes, to be executed during the different operational phases. Two main workflows: During PrePass-Phase During Pass-Phase Parent workflow. Sub-Workflows can be chained. Processes to be executed by this workflow.

23. C-KAE: Knowledge Acquisition Tool (6/8) : Behaviour (Inference) Knowledge: Rulesets of the Fuzzy Inference Processes. Rule sets. Rule edition panel.

24. C-KAE: Knowledge Acquisition Tool (7/8) : Validation of Knowledge Consistency: Protégé Axiom Language (PAL) rules Checking that Domain Knowledge settings and Inference Rules are consistent. PAL is analogue to what represents be OCL in a UML model. Protege allows to define and execute Constraint rules over the Frame based model. Consistency rule description and code.

25. C-KAE: Knowledge Acquisition Tool (8/8) : Storing and loading Knowledge Model into the CESADS-Server. All Acquired Knowledge is Stored in an XML-based schemas, and loaded into the CESADS-Server system, in a SQL database. When the C-Server is started, it creates all the Processes Threads agents, and starts the execution of the controlling workflows. Then, it is ready to serve the Parameter Objects to which C-SLAM monitoring& diagnostic client application can subscribe to.

26. Context Addressed Problem Methods & Solution Approach Results Conclusions Outline

27. CommonKADS has been proved as a valid Knowledge Management and Engineering methodology. Knowledge technologies provide added value to the CESADS Adressed Problems: Knowledge Formalisation: Frame Based systems, Ontologies. Rule Based Inference Production Engine Fuzzy Logic Rules. Successful generation of high-level qualitative indicators, providing an integrated view of a big system (ESTRACK). Evolution: however, more AI technologies (particularly that of the Ontologies) are arriving to the IT mainstream (SemanticWeb vision), and therefore a deep platform evolution is envisioned, towards a concept of: “Knowledge Enabled Services (KES) Grid for Decision Support”. … as follows: Conclusions

28. Frameworks Public Private Research Commercial Stakeholders Defense&Security Academy Institutional Industry Ambient Inteligence Network Centric Operations (NCO) E-Business E-Commerce E-Science E-Gov ECM BI Targetted Needs CRM ERP Typical Business Solutions Web-based “Knowledge Enabled Services (KES) Grid” Platform Information Exploitation chain Metadata & Semantic Content Annotation SOA Distributed Computing Collaborative Environment ESB Knowledge Represent. & Sharing EAI Middleware & integration Globus Toolkit-4 Service Registries Service Orchestration Application Servers UDDI SWS (OWL-S, WSMO) WSDL Ontologies SOAP WS-I WS-RF OWL ebXML BPEL OGSA RDF XML J2EE .NET Semantic Technology Grid Technology Web Enterprise Platforms Technology WebServices Technology Internet Grounding, Foundations Conclusion in terms of SW Evolution for KES platformsConvergence of major tech branches into a “KES-Grid” platform concept. Key role of Semantic Web & Ontologies, and particularly the W3C´s RDF/OWL-XML languages, as enabler of a semantic interoperability layer, and sharable knowledge representation. RDF&OWL are being integrated as specific application components, but also enabling extension of other web-based communication and control protocols. Semantic Web Grid Computing WebServices Business Process Mngt. Technology Umbrellas Technology Components Technology Standards

29. CommonKADS approaches (Knowledge Intensive Tasks analysis, Knowledge Models Templates, Libraries of Problem Solving Methods) can be migrated to these new state-or-art technology landscape: Domain Knowledge to be represented with RDF/OWL ontologies. Domain Behaviour as workflows based on WS-BPEL, chaining distribued & loosely coupled inference engines that shall be view as webservices. Domain Elements that are provide useful Parameters for the Monitoring and Diagnostic task, can publish parameters semantically annotated (e.g., RSS feeds) Used tools (Protégé, Jena) support this migration: Protégé is most-widely use ontology modelling tool. It integrates Jena (from HP Laps) to support OWL & RDF JESS is evolving, last version now with an IDE based on Eclipse plugin. Thus going towards the combination of benefits of the well founded cKADS Knowledge Engineering methodology, toguether with the emerging benefits of the Semantic Service Oriented Architectures. Conclusion in terms of SW Evolution for KES platformsCommonKADS approaches over a KES-Grid platform concept.

30. Questions and Answers Thank You Contacts: Javier Varas, GTD Sistemas de Información, Email: javier.varas@gtd.int ; Javier Busto, GTD Sistemas de Información, Email: javier.busto@gtd.es;