A Layered Approach to Composition and Interoperation in Complex Systems Dr. Andreas Tolk Department of Engineering Management and Systems Engineering Old Dominion University, Norfolk, VA Saikou Y. Diallo, Robert D. King, and Charles D. Turnitsa Virginia Modeling Analysis and Simulation Center (VMASC) Old Dominion University, Norfolk, VA Prepared for the HRA INCOSE Requirements Analysis and Management Seminar November 4, 2008, Newport News

Structure of the Presentation Levels of Interoperation – Currently applied Architecture Frameworks – Levels of Conceptual Interoperability Model Services, Agents, and Systems of Systems – What is needed to understand a component – What is needed to integrate a component Engineering Methods – Data Engineering – Process Engineering – Constraint Engineering Does it work? – Case Examples from NATO, Joint Forces Command, Department of Energy, Department of Homeland Security HRA INCOSE Nov 2008, Newport News 2Tolk et al.: Layered Models

LEVELS OF INTEROPERATION Section 1 HRA INCOSE Nov 2008, Newport News 3Tolk et al.: Layered Models

Framework Support The task – Integrate legacy solutions providing needed functionality in an aligned, orchestrated, and consistent way The answer – Service oriented architectures – Grid solutions – Federations of systems – System of systems The challenge – How to ensure aligned, orchestrated and consistent integration? – What frameworks and supporting artifacts are needed? HRA INCOSE Nov 2008, Newport News 4Tolk et al.: Layered Models

State of the Art: Current Focus Architectural Views – Functional – Physical – Operational System Views – Function – Structure – Behavior Unified Views (SysML, OPM, …) Focus lies still on Developing One System HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models5

What is needed for SOSE? Alignment – Can data be obtained – Can data be mediated Orchestration – When to call which service/procedure – Integration of new functions into existing logical flow – Timing and synchronization – Post- and pre-conditions Consistency – Assumptions, constraints, and assertions HRA INCOSE Nov 2008, Newport News 6Tolk et al.: Layered Models and all supported by Machines …

Levels of Conceptual Interoperability Model (LCIM) Level 5 Dynamic Interoperability Level 4 Pragmatic Interoperability Level 3 Semantic Interoperability Level 2 Syntactic Interoperability Level 0 No Interoperability Level 1 Technical Interoperability Level 6 Conceptual Interoperability Increasing Capability for Interoperation Modeling / Abstraction Simulation / Implementation Network / Connectivity HRA INCOSE Nov 2008, Newport News 7Tolk et al.: Layered Models

SERVICES, AGENTS, AND SYSTEMS OF SYSTEMS Section 2 HRA INCOSE Nov 2008, Newport News 8Tolk et al.: Layered Models

Technical Solutions Service-oriented architectures – Services are loosely coupled to provide needed functionality – XML, XSD, SOAP, UDDI, XSLT, … – OWL, OWL-S, … Agents – Intelligent Software Agents – Representing the functions/services – Collaborate with each other System of Systems HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models9 They should make life easier, but do they?

Artifacts needed In order to allow systems support, we need to make the functionality understandable for software – Web services needed – System components reusable – Agents representing functions correctly Challenge based on current support – Current solution focus on integration and interoperability (technical challenges based on implementation) – Real interoperation requires conceptual alignment as well HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models10

Understanding in Models The Three Premises for Understanding Zeigler B.P. Toward a Simulation Methodology for Variable Structure Modeling, In Elzas/Oren/Zeigler (Eds.) Modeling and Simulation Methodology in the Artificial Intelligence Era, North Holland, 1986 Perception Meta-Models Relationship Mapping Observed System Observing System HRA INCOSE Nov 2008, Newport News 11Tolk et al.: Layered Models

HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models12 The objective of ontologies is to document the conceptualization, which is another word for the result of the modeling process. This is done in a specified way, which means the application of engineering methods guided by rules and methods. The result is formalized, which means that machines and computers can not only read the result, but also make sense out of it in the context of their applications. Controlled Vocabularies Thesauri Taxonomies Ontologies Logical Models Applying the Ontological Spectrum

Semantic Web Extensible Markup Language – XML – XML enables data interchange between services and applications – XML supports the Syntactic Level of Interoperability by enabling a common structure of data Research Description Framework – RDF RDF Schema – RDFS – RDF/RDFS enables data interchange between services and applications – RDF/RDFS supports the Syntactic Level of Interoperability by enabling a common structure of data Web Ontology Language – OWL – OWL is based on XML and RDFS and supports therefore the Syntactic Level as they do – OWL was designed to support Strong Semantics supporting the Semantic Level in machine readable form OWL for Services – OWL-S – The service defines the context of the data exchange, so that OWL-S supports Pragmatic Interoperability – The Services Model of OWL-S can support Dynamic Interoperability, but the current versions (IOPE) do not deal with dynamic description of services in sufficient detail HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models13

Interoperability Contributions HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models14 Dyn Pragm Sem Syn none Tech Con XMLRDF (S)OWLOWL-S

ENGINEERING METHODS Section 3 HRA INCOSE Nov 2008, Newport News 15Tolk et al.: Layered Models

Challenges of Interoperation Triangles of Interoperation – Scope – Resolution – Structure – Conceptual model – Logical model – Physical model – Data – Processes – Constraints HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models16 Can we make the system describing and organizing the information exchange by itself?

Level 5 Dynamic Interoperability Level 4 Pragmatic Interoperability Level 3 Semantic Interoperability Level 2 Syntactic Interoperability Level 1 Technical Interoperability Level 6 Conceptual Interoperability Constraints Data Engineering Process Engineering Organizational and Business Model HRA INCOSE Nov 2008, Newport News 17Tolk et al.: Layered Models

Data Engineering Data Administration – Data Administration identifies and manages the information exchange needs between candidate systems (focusing on clearly defining the direction of data flow) Data Management – The goal of Data Management is to map concepts, data elements and relationships from the source model to the target model. Data Alignment – The goal of data alignment is to identify gaps between the source and the target. Data Transformation – The goal of Data Transformation is to align models in terms of their level of resolution. HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models18

Process Engineering Process Cataloging – The important goal to achieve with cataloging is to gain an understanding of where these processes are to be used Process Identification – Providing a description of what the process does, what its resource and time requirements are to complete, and what data it operates on Process Alignment – Comparison of the information provided for two processes that are part of the exchange of information for interoperability Process Transformation – Identify differences between processes and accommodate them by middle-ware processes HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models19

Constraint Engineering Capturing assumption and constraints – The objective is to write down what the main concepts are Encoding Propositions – Encoded objectives in a knowledge representation language Comparing Assumption/Constraint Lists – Produce a measure of the semantic distance between propositions to understand differences in machine coded form Adjudication and Resolution of Conflicts – Identify resolvable and irresolvable conflicts HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models20

CASES STUDIES Section 4 HRA INCOSE Nov 2008, Newport News 21Tolk et al.: Layered Models

22Tolk et al.: Layered Models HRA INCOSE Nov 2008, Newport News GE Adapter C2 Linkopping PABST M&S Meppen SICF C2 Paris SIMBAD M&S Madrid WebCOP C2 Norfolk SitaWare C2 Norfolk C-BML enabling Web Services NATO MSG-027 PATHFINDER Integration Environment Experiment C2-M&S Coupling November 9, 2006

HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models23

24 SITAWARE HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models PABSTSICF SIMBAD

Joint Rapid Scenario Generation Web Service JEDIS DB ASIT Client (TBB) JIDPS Client (TBB) UOBDAT Client (TBB) SGS Init Client (TBB) ACSIS SGS JIDPS UOBDAT JCATS Product Generator (JIDPS TBB) AWSIM Products Generator (SGS TBB) JCATS Init Products AWSIM Init Products JEDIS Web Service Authoritative Data Sources Mediation Clients Cohesive Data Product Repository Data Consumers Data Products JEDIS Scenario Overview JSAF Products Generator (SGS TBB) JSAF Init Products HRA INCOSE Nov 2008, Newport News 25Tolk et al.: Layered Models

DOE and HLS In particular Data Engineering is recognized to be needed to gain a common understanding of operations – Several ontological works – Common vocabularies Idea of the LCIM applied in different contexts – GridWise Architecture Framework – Ontology tool development Presentations of LCIM and Data Engineering HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models26

SOME PROVOCATIVE IDEAS For the end HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models27

Do we still need Systems Engineering? System of Systems focus on cross-using functionality between legacy solutions Services encapsulate functionality for re-use in new contexts System borders become more fluent Requirements are valid until they are fixed, then they change immediately New world: continuous flux of reuse and reconfiguration Instead of Systems Engineering we need to educate for System of Systems Engineering System Engineering knowledge must be captured in machine understandable form, as the lion share will be done by machines in the future System Engineers are needed, but they must to start to encode their knowledge HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models28

Literature Andreas Tolk, Robert D. Aaron: Data Engineering for Data-Rich Integration Projects: Case Studies Addressing the Challenges of Knowledge Transfer, Engineering Management Journal, in press Andreas Tolk, Charles D. Turnitsa, Saikou Y. Diallo: Implied Ontological Representation within the Levels of Conceptual Interoperability Model, International Journal of Intelligent Decision Technologies (IDT), Special Issue on Ontology Driven Interoperability for Agile Applications using Information Systems: Requirements and Applications for Agent Mediated Decision Support, Volume 2, Issue 1, pp. 3-19, January 2008 Andreas Tolk, Saikou Diallo: Model-Based Data Engineering for Web Services, IEEE Internet Computing Volume 9 Number 4, pp , July/August 2005 Andreas Tolk, Saikou Y. Diallo, Robert D. King, Charles D. Turnitsa: A Layered Approach to Composition and Interoperation in Complex Systems, Chapter 3 in Tolk and Jain (Eds.): Complex Systems in Knowledge based Environments: Theory, Models and Applications. Series: Studies in Computational Intelligence, Vol. 168, Springer, 2009 Andreas Tolk, Saikou Y. Diallo: Model-based Data Engineering for Web Services, Chapter 6 in Nayak et al. (Eds.): Evolution of the Web in Artificial Intelligence Environment, SCI 130, pp. 137–161, Springer, 2008 HRA INCOSE Nov 2008, Newport News Tolk et al.: Layered Models29

Questions and Comments Dr. Andreas Tolk Associate Professor Engineering Management and Systems Engineering Old Dominion University Norfolk, VA Saikou Y. Diallo Robert D. King Charles D. Turnitsa Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk, VA HRA INCOSE Nov 2008, Newport News 30Tolk et al.: Layered Models