Adaptive Information Improving Business Through Semantic Interoperability, Grid Computing, and Enterprise Integration

Adaptive Information Improving Business Through Semantic Interoperability, Grid Computing, and Enterprise Integration

by Jeffrey T. Pollock, Ralph Hodgson
Released September 2004
Publisher(s): Wiley-Interscience
ISBN: 9780471488545

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Book description

  • New Paradigm for considering application integration and B2B problems

  • Heightens the importance of conveying meaning between systems

  • Addresses movement in the EAI space toward more data handling capabilities

  • Offers a solution for the multitude of managers disconnected with the latest technologies

  • Leverages the technical advances made in complex data integration over 15 years

  • Shifts the focus from technology solutions to information solutions

  • Relies heavily on the use of practical examples, tips, definitions, and soapbox excerpts throughout the main body of text

Table of contents

  1. Copyright
  2. Foreword
  3. Preface
    1. HOW THIS BOOK WILL HELP
    2. ROAD MAP
    3. GUIDE TO THE READER
    4. Book Iconography and Picture Key
    5. PEDAGOGICAL ELEMENTS IN THE TEXT
  4. Acknowledgments
  5. 1. Why Semantic Interoperability?
    1. 1. Semantic Interoperability Gives IT Meaning
      1. 1.1. DOES IT MATTER ANYMORE?
      2. 1.2. SEMANTIC INTEROPERABILITY: 15-YEAR OVERNIGHT SUCCESS
        1. 1.2.1. Semantic Interoperability vs. the Semantic Web
        2. 1.2.2. Differing Uses of the Term "Interoperability"
      3. 1.3. THE THIRD WAVE
        1. 1.3.1. Strategies for Competitive Advantage
        2. 1.3.2. Mergers and Acquisitions
        3. 1.3.3. Corporate Consolidation
        4. 1.3.4. Increased IT Spending, Increased Agility?
      4. 1.4. BUSINESS AND APPLICATION IMPACTS
        1. 1.4.1. Key Industries and Vertical Markets
        2. 1.4.2. Key Processes and Application Areas
      5. 1.5. THE INFORMATION EXPLOSION
        1. 1.5.1. Information Matters More Than Ever
        2. 1.5.2. Data Rich, Information Poor
        3. 1.5.3. Information Dominance
      6. 1.6. SYSTEMIC BARRIERS TO IT INNOVATION
        1. 1.6.1. The Speed of Change Barrier
        2. 1.6.2. Entrenched IT Infrastructures
        3. 1.6.3. Fuzzy Data, Fuzzy Process
        4. 1.6.4. The "H" Factor
      7. 1.7. SUMMARY AND CONCLUSIONS
    2. 2. Information Infrastructure Issues and Problems
      1. 2.1. LIMITATIONS OF CURRENT TECHNOLOGY APPROACHES
        1. 2.1.1. Off-the-Shelf Integration Products
        2. 2.1.2. Web Services and Service-Oriented Architectures
        3. 2.1.3. Data Warehouses and Metadata Management
        4. 2.1.4. Portals
        5. 2.1.5. Systems Integrators—Custom Solutions
        6. 2.1.6. Standard Data Vocabularies
      2. 2.2. TRADITIONAL APPLICATION INTEGRATION
        1. 2.2.1. Key Components
        2. 2.2.2. Disadvantages and Concerns
        3. 2.2.3. Application Integration Trend: Target the Vertical Markets
        4. 2.2.4. Integration's Core Issue
      3. 2.3. APPLICATION INTEGRATION AS PLUMBING
        1. 2.3.1. Why Connecting Systems is Necessary
        2. 2.3.2. The Coupling Question
        3. 2.3.3. Business Process Plumbing
        4. 2.3.4. Service-Oriented Plumbing
        5. 2.3.5. Why Plumbing is Insufficient
      4. 2.4. ENTERPRISE CONTENT, NOT PLUMBING, IS KING
        1. 2.4.1. Enterprise Information Data Structures
        2. 2.4.2. Continuum of Knowledge
      5. 2.5. FINAL THOUGHTS ON OVERCOMING INFORMATION INFRASTRUCTURE PROBLEMS
        1. 2.5.1. Semantic Information Interoperability
    3. 3. Promise of Frictionless Information
      1. 3.1. ORGANIC MIDDLEWARE: "SOFTWARE, INTEGRATE THYSELF!"
        1. 3.1.1. A Tough Road Forward
      2. 3.2. ORGANIC MIDDLEWARE HINGES ON SEMANTIC INTEROPERABILITY
      3. 3.3. FRAGMENTED INDUSTRY EFFORTS AND ORGANIC COMPUTING
        1. 3.3.1. Autonomic Computing
        2. 3.3.2. Semantic Web
        3. 3.3.3. Semantic Web Services
        4. 3.3.4. Service Grid
        5. 3.3.5. Model-Driven Architecture
        6. 3.3.6. Intelligent Agents
      4. 3.4. ACHIEVING SYNTHESIS
      5. 3.5. INTELLIGENCE INFORMATION SHARING IN THE TWENTY-FIRST CENTURY
        1. 3.5.1. Information Sharing Imperative
        2. 3.5.2. Information Sharing Conundrum
        3. 3.5.3. Toward a Pragmatic Solution: Semantic Interoperability
      6. 3.6. SEMANTIC INTEROPERABILITY FRAMEWORK CHARACTERISTICS
        1. 3.6.1. Dynamic
        2. 3.6.2. Real Time
        3. 3.6.3. Loosely Coupled
        4. 3.6.4. Highly Flexible
        5. 3.6.5. Secure
        6. 3.6.6. Open
        7. 3.6.7. Service Oriented
        8. 3.6.8. Information-Centric
        9. 3.6.9. Autonomic
      7. 3.7. DEVELOPING A SEMANTIC INTEROPERABILITY SOLUTION ARCHITECTURE
        1. 3.7.1. Design Time
        2. 3.7.2. Run Time
      8. 3.8. FINAL THOUGHTS ON FRICTIONLESS INFORMATION
  6. 2. Semantic Interoperability Primer
    1. 4. Foundations in Data Semantics
      1. 4.1. INTRODUCTION
      2. 4.2. THE GREAT DEBATE
        1. 4.2.1. Plato and Aristotle Argue About Truth
        2. 4.2.2. Kant Searches for What is Knowable
        3. 4.2.3. C.S. Peirce Redefines Logic and Meaning
        4. 4.2.4. Wittgenstein Finds Meaning and Nonsense in Language Games
        5. 4.2.5. Bridging the Gap Between Philosophy, Linguistics, and Computer Science
      3. 4.3. NATURAL LANGUAGE AND MEANING
        1. 4.3.1. Why Twentieth-Century Philosophy Stalled Out
        2. 4.3.2. Fuzzy Language
      4. 4.4. CONTEXT AND MEANING
        1. 4.4.1. Domain Context
        2. 4.4.2. Local Context
      5. 4.5. WHAT ARE SEMANTICS IN DIGITAL SYSTEMS?
        1. 4.5.1. Semantics Are Real Time
        2. 4.5.2. Semantics Must Be Explicit to Be Useful
        3. 4.5.3. Continuum of Semantic Approaches
        4. 4.5.4. Semantics as Pattern Analysis
        5. 4.5.5. Semantics as Definition and Synonym Relationships
        6. 4.5.6. Semantics as Inference and Deductive Logic
        7. 4.5.7. Semantics as Context-Aware Schema Mappings
        8. 4.5.8. Semantics as Guesswork and Abductive Logic
      6. 4.6. INFORMATION TECHNOLOGY
        1. 4.6.1. Information is Data with Meaning, in Context
        2. 4.6.2. Where Is the Information in IT?
        3. 4.6.3. Introduction to Knowledge Representation and Ontology
        4. 4.6.4. Information Is at the Eyeball, but Also at the Interface
      7. 4.7. CONCLUSIONS
    2. 5. Semantic Conflict Solution Patterns
      1. 5.1. FINDING SEMANTICS IN ENTERPRISE SOFTWARE
      2. 5.2. SEMANTIC CONFLICTS IN COMMUNICATION
        1. 5.2.1. A Short Note About Structure and Style
        2. 5.2.2. Summary of Data Conflicts
        3. 5.2.3. Data Type Conflicts
        4. 5.2.4. Labeling Conflicts
        5. 5.2.5. Aggregation Conflicts
        6. 5.2.6. Generalization Conflicts
        7. 5.2.7. Value Representation Conflicts
        8. 5.2.8. Impedance Mismatch Conflicts
        9. 5.2.9. Naming Conflicts
        10. 5.2.10. Scaling and Unit Conflicts
        11. 5.2.11. Confounding Conflicts
        12. 5.2.12. Domain Conflicts
        13. 5.2.13. Integrity Conflicts
      3. 5.3. SEMANTIC SOLUTION PATTERNS
        1. 5.3.1. Machine Learning Pattern
        2. 5.3.2. Third-Party Reference Pattern
        3. 5.3.3. Model-Based Mapping Pattern
        4. 5.3.4. Inference Pattern
      4. 5.4. CONTEXT BOUNDARIES FOR SEMANTIC INTERPRETATION
        1. 5.4.1. To Computers, All Context Is String
        2. 5.4.2. Context Models and Taxonomy
      5. 5.5. FINAL THOUGHTS ON CONFLICT AND CONTEXT
    3. 6. Metadata Archetypes
      1. 6.1. WHAT IS METADATA?
        1. 6.1.1. The Ultimate Overloaded Term
        2. 6.1.2. All Systems Are Metadata Systems
        3. 6.1.3. Metadata Is Necessary, but Not Sufficient
      2. 6.2. HIERARCHY OF METADATA
        1. 6.2.1. Layer 1: Instance Data
        2. 6.2.2. Layer 2: Syntactic Metadata
        3. 6.2.3. Layer 3: Structural Metadata
        4. 6.2.4. Layer 4: Referent Metadata
        5. 6.2.5. Layer 5: Domain Metadata
        6. 6.2.6. Layer 6: Rules
      3. 6.3. ENVISIONING A MODERN OSI COMMUNICATIONS STACK
        1. 6.3.1. Network Communications Stack
        2. 6.3.2. Payload, Not Just Packets
      4. 6.4. METADATA ARCHITECTURES FROM THE OBJECT MANAGEMENT GROUP
        1. 6.4.1. Common Warehouse Metamodel
        2. 6.4.2. Model-Driven Architecture
        3. 6.4.3. Criticism of OMG and Metadata Architectures
        4. 6.4.4. Future Developments of UML—A Visual Ontology Language?
      5. 6.5. FINAL THOUGHTS ON METADATA ARCHETYPES
    4. 7. Ontology Design Patterns
      1. 7.1. CORE ENGINEERING PATTERNS
        1. 7.1.1. Indirection
        2. 7.1.2. Abstraction
      2. 7.2. WHAT IS ONTOLOGY?
      3. 7.3. ONTOLOGY TYPES AND USAGE
        1. 7.3.1. Interface Ontology
        2. 7.3.2. Process Ontology
        3. 7.3.3. Policy Ontology
        4. 7.3.4. Information Ontology
      4. 7.4. SPECTRUM OF ONTOLOGY REPRESENTATION FIDELITY
        1. 7.4.1. Controlled Vocabulary
        2. 7.4.2. Taxonomy
        3. 7.4.3. Relational Schema
        4. 7.4.4. Object-Oriented Models
        5. 7.4.5. Knowledge Representation Languages
      5. 7.5. ONTOLOGY ARCHITECTURE PATTERNS
        1. 7.5.1. Pattern Summaries
        2. 7.5.2. Core Three Schema
        3. 7.5.3. Conceptual Identity
        4. 7.5.4. Design Moderator
        5. 7.5.5. Ontology Broker
        6. 7.5.6. Motivation
        7. 7.5.7. Decoupled Blueprint
        8. 7.5.8. Model-Driven Federated Governance
        9. 7.5.9. Logical Hub and Spoke
        10. 7.5.10. Smart Query
        11. 7.5.11. Herding Cats
        12. 7.5.12. Structure Reuse
      6. 7.6. REPRESENTATION AND LANGUAGES
        1. 7.6.1. Web Ontology Language (OWL)
        2. 7.6.2. Unified Modeling Language (UML)
        3. 7.6.3. Resource Description Framework (RDF)
        4. 7.6.4. Knowledge Interchange Format (KIF)
        5. 7.6.5. Other Modeling Formats
      7. 7.7. ONTOLOGY TRANSFORMATION
        1. 7.7.1. Why Bother Transforming Ontology?
        2. 7.7.2. Issues with Ontology Transformation
        3. 7.7.3. Solutions for Ontology Transformation
      8. 7.8. FINAL THOUGHTS ON ONTOLOGY
    5. 8. Multimodal Interoperability Architecture
      1. 8.1. A SIMPLE SEMANTIC ARCHITECTURE—WHY NOT?
      2. 8.2. GENERAL MULTIMODAL SEMANTIC ARCHITECTURE
        1. 8.2.1. Architectural Modes
        2. 8.2.2. Key Technology Goals
        3. 8.2.3. Semantic Interoperability Architecture Principles
        4. 8.2.4. Functional Decomposition
        5. 8.2.5. Conceptual View: Dynamic and Autonomic Capabilities
        6. 8.2.6. Component View: Multimodal Semantic Interoperability Architecture
        7. 8.2.7. Logical View: Models, Maps, Schema, and Logics
        8. 8.2.8. Process View: Dynamic Service Discovery
        9. 8.2.9. Process View: Dynamic Service Collaboration
      3. 8.3. EXAMPLE DYNAMIC INFORMATION HUB: PLM INTEROPERABILITY
        1. 8.3.1. Use Case View: Machine-to-Machine Product Data Exchanges
        2. 8.3.2. Key Assumptions
        3. 8.3.3. Component View
        4. 8.3.4. Logical View
        5. 8.3.5. Architecture Patterns
        6. 8.3.6. Infrastructure Benefits
      4. 8.4. FINAL THOUGHTS ON SEMANTIC INTEROPERABILITY ARCHITECTURES
    6. 9. Infrastructure and E-Business Patterns
      1. 9.1. SEMANTIC INTEROPERABILITY INFRASTRUCTURE PATTERNS
        1. 9.1.1. Infrastructure Pattern Summary
        2. 9.1.2. Policy Governor
        3. 9.1.3. Generative Integration
        4. 9.1.4. Dynamic Information Hub
        5. 9.1.5. Adaptive System of Systems
      2. 9.2. INFRASTRUCTURE PATTERNS IN BUSINESS CONTEXT
        1. 9.2.1. Business Schematic Summary
        2. 9.2.2. Direct to Customer
        3. 9.2.3. Full-Service Provider
        4. 9.2.4. Whole of Enterprise
        5. 9.2.5. Intermediary
        6. 9.2.6. Shared Infrastructure
        7. 9.2.7. Virtual Community
        8. 9.2.8. Value Net Integrator
        9. 9.2.9. Content Provider
      3. 9.3. FINAL THOUGHTS ON INFRASTRUCTURE PATTERNS AND BUSINESS SCHEMATICS
  7. 3. Adopting Semantic Technologies
    1. 10. Capability Case Studies
      1. 10.1. INTRODUCING CAPABILITY CASES
        1. 10.1.1. Why Capability Cases?
        2. 10.1.2. The Language of Capability Cases
        3. 10.1.3. What is the Relation Between Capability Cases and Use Cases?
        4. 10.1.4. The Capability Case Template
        5. 10.1.5. Why Use Capability Cases for Case Studies?
        6. 10.1.6. Semantic Technology Capability Cases Summary
      2. 10.2. APPLICATION INTEROPERABILITY
        1. 10.2.1. Capability Case: Semantic Application Integrator
        2. 10.2.2. Solution Story: Cargo Airlines System Integration
        3. 10.2.3. Solution Story: US Army Knowledge Asset Management Network
        4. 10.2.4. Capability Case: Product Design Advisor
        5. 10.2.5. Solution Story: Test Car Configurator
      3. 10.3. DATA INTEROPERABILITY
        1. 10.3.1. Capability Case: Content Annotator
        2. 10.3.2. Solution Story: AeroSWARM Automated Markup
        3. 10.3.3. Solution Story: MINDlab SWAP RDF Editor
        4. 10.3.4. Solution Story: McDonald-Bradley Annotation of DoD Documents
        5. 10.3.5. Capability Case: Semantic Data Integrator
        6. 10.3.6. Solution Story: FAA Air Passenger Threat Analyzer
        7. 10.3.7. Solution Story: Cogito's Integrated Data Surveillance Solution
        8. 10.3.8. Solution Story: Major Electronics Manufacturer Data Integration
        9. 10.3.9. Solution Story: Children's Hospital Lab Data Mediation
        10. 10.3.10. Solution Story: Consulting Services Company Data Quality
        11. 10.3.11. Solution Story: US Air Force Enhanced Information Management
        12. 10.3.12. Capability Case: Semantic Data Acquirer
        13. 10.3.13. Solution Story: Oceanic and Atmospheric Sciences Data Automation
        14. 10.3.14. Capability Case: Semantically Enriched Multimedia Integrator
        15. 10.3.15. Solution Story: CoAKTinG Distributed Meetings Manager
        16. 10.3.16. Capability Case: Semantic Content Registry
        17. 10.3.17. Solution Story: European Environment Agency Content Registry
      4. 10.4. SERVICES INTEROPERABILITY
        1. 10.4.1. Capability Case: Task Composer
        2. 10.4.2. Solution Story: STEER—a User-Centered Environment for Task Computing
        3. 10.4.3. Capability Case: Process-Aware Web Services Match-Maker
        4. 10.4.4. Solution Story: Service Discovery Using Process Ontology Queries
      5. 10.5. PROCESS INTEROPERABILITY
        1. 10.5.1. Capability Case: Process Aggregator
        2. 10.5.2. Solution Story: Ford e-Hub—a Process Aggregation Architecture
      6. 10.6. POLICY AND RULES INTEROPERABILITY
        1. 10.6.1. Capability Case: Rights Mediator
        2. 10.6.2. Solution Story: RightsCom Policy Engine
      7. 10.7. SUMMARY
    2. 11. Adoption Strategies
      1. 11.1. ACQUIRING NEW SKILLS AND COMPETENCIES
      2. 11.2. MANAGING IMPACT ON THE ENGINEERING LIFECYCLE
        1. 11.2.1. Lifecycle Activities of Ontology Engineering
        2. 11.2.2. Project Management and ROI Considerations
      3. 11.3. ENVISIONING THE SOLUTION
        1. 11.3.1. Solution Envisioning with Capability Cases
        2. 11.3.2. Solution Envisioning's Three Key Phases
      4. 11.4. CHOOSING THE RIGHT ONTOLOGY ENGINEERING METHODOLOGY
      5. 11.5. NONPROPRIETARY METHODS
        1. 11.5.1. Uschold and King (1995)
        2. 11.5.2. Grüninger and Fox (TOVE)
        3. 11.5.3. Organization Domain Modeling (ODM)
        4. 11.5.4. METHONTOLOGY
        5. 11.5.5. CommonKADs
        6. 11.5.6. On-To-Knowledge
      6. 11.6. PROPRIETARY METHODS
        1. 11.6.1. Modulant™
        2. 11.6.2. Unicorn Solutions
      7. 11.7. OVERCOMING POPULAR MYTHS
      8. 11.8. IMPLEMENTING BEST PRACTICES
    3. 12. Tomorrow's Adaptive and Dynamic Systems
      1. 12.1. BIG PICTURE MARKET TRENDS
        1. 12.1.1. Responding to Information Explosion
        2. 12.1.2. Confronting the Speed of Change Barrier
        3. 12.1.3. Harnessing Technology Waves
      2. 12.2. INDUSTRY ADOPTION OF SEMANTIC TECHNOLOGY
        1. 12.2.1. Semantic Technology Application Areas
      3. 12.3. RESEARCH PROGRAMS TO WATCH
        1. 12.3.1. Advanced Knowledge Technologies
        2. 12.3.2. MIT SIMILE
        3. 12.3.3. University of Maryland MINDlab
        4. 12.3.4. IBM Research's SNOBASE
        5. 12.3.5. Hewlett Packard Semantic Web Research Labs
        6. 12.3.6. Fujitsu Labs
      4. 12.4. TECHNOLOGIES TO WATCH
        1. 12.4.1. Framework—Model-Driven Architecture (MDA)
        2. 12.4.2. Semantic Web Services—OWL-S
        3. 12.4.3. Policy and Rules—SWRL
        4. 12.4.4. Ontology Language—OWL
        5. 12.4.5. Ontology Visual Models—UML/ODM
        6. 12.4.6. Ontology Bridges—Topic Maps
      5. 12.5. BARRIERS TO WIDESPREAD ADOPTION
        1. 12.5.1. Vocabulary Standards Hyperbole
        2. 12.5.2. Gaps in Metadata Standards
        3. 12.5.3. Complexity and Risk-Averse Companies
      6. 12.6. CROSSING THE CHASM
  8. A. Vendor Profiles, Tools, and Scorecards
    1. A.1. VENDOR OVERVIEWS
      1. A.1.1. Innovative Small and Mid-Sized Vendors
      2. A.1.2. Large Vendors Pursuing Semantics-Based Solutions
      3. A.1.3. Notable Mentions
    2. A.2. ONTOLOGY DEVELOPMENT TOOL SUPPORT
      1. A.2.1. Summary Conclusions
      2. A.2.2. Ontology Lifecycle and Tools
      3. A.2.3. Ontology Tools Survey
      4. A.2.4. Ontology Tool Capabilities and Standards Support
      5. A.2.5. Semantic Interoperability Platform Capabilities
    3. A.3. ABOUT VENDOR SELECTION
    4. A.4. VENDOR SCORECARD ANALYSIS (HOW TO MEASURE YOUR VENDOR)
      1. A.4.1. Context Mediation Capabilities
      2. A.4.2. Semantic Conflict Accommodation
      3. A.4.3. Full Lifecycle Tool Support
      4. A.4.4. Targeted Domain Fit
      5. A.4.5. Horizontal Domain Flexibility
      6. A.4.6. Formalized Methodology
      7. A.4.7. Level of Data Coupling
      8. A.4.8. Level of Service Coupling
      9. A.4.9. Automation Support
      10. A.4.10. Simplicity and Ease of Use
  9. B. Acronyms and Abbreviations
    1. B.1. Acronyms and Abbreviation Definitions
    2. B.2. Endnotes

Product information

  • Title: Adaptive Information Improving Business Through Semantic Interoperability, Grid Computing, and Enterprise Integration
  • Author(s): Jeffrey T. Pollock, Ralph Hodgson
  • Release date: September 2004
  • Publisher(s): Wiley-Interscience
  • ISBN: 9780471488545