A Layered Approach

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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


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Tolk et al.: Layered Models

LEVELS OF INTEROPERATION

Section 1

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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?

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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

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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

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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

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SERVICES, AGENTS,

AND SYSTEMS OF SYSTEMS

Section 2

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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

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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

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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

1

2

3

Observed

System

Observing

System

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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

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Interoperability Contributions

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Dyn

Pragm

Sem

Syn

none

Tech

Con

XML

RDF (S)

OWL

OWL
-
S

ENGINEERING METHODS

Section 3

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Challenges of Interoperation


Triangles of Interoperation


Scope


Resolution


Structure



Conceptual model


Logical model


Physical model



Data


Processes


Constraints

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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

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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.

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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



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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


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CASES
STUDIES

Section 4

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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

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24

SITAWARE

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PABST

SICF

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

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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

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SOME PROVOCATIVE IDEAS

For the end

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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

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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. 65
-
70, 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


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Questions and Comments


Dr. Andreas Tolk

Associate Professor

Engineering Management
and Systems Engineering

Old Dominion University

Norfolk, VA 23529


Saikou Y. Diallo

Robert D. King

Charles D. Turnitsa

Virginia Modeling Analysis
and Simulation Center

Old Dominion University

Suffolk, VA 23435

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