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Conceptual Leap: Bridging Data Silos With Semantic Mediation
4 October 2011
 
Nancy McQuillen  

Problems of data silos persist, and are becoming worse as organizations find more business needs for data sharing within and beyond the enterprise. Conceptual models capture the semantics of business data and can unify data silos. Semantic metadata standards and tools are evolving to simplify modeling and mapping tasks and to enable greater expressiveness and precision of models. Corporate databases, data warehouses, data services, and information queries can all be mapped to central concepts and semantic models to ensure valid integration of enterprise information for enhanced insight and agility.








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Table of Contents

Contents
  • Summary of Findings
  • Analysis
    • The Escalating Problems of Data Silos
    • What Is Semantic Mediation?
    • Use Cases for Semantic Mediation
      • Data Integration Between Known Application Systems
      • Data Integration in Dynamic Sharing or Trading Networks
      • Data Warehouse Integration and Evolution
      • Reporting and Mashup of Disparate Datasets and Formats
    • The Semantic Mediation Solution
      • Gartner Support for Canonical and Semantic Approaches
      • Solution Patterns for Semantic Mediation
      • Levels and Types of Semantic Precision
    • Strengths
    • Weaknesses
  • Recommendations
  • The Details
    • Standards for Semantic Metadata and Interoperability
      • Data Naming and Metadata Registry Standard: ISO/IEC 11179
      • Controlled Vocabularies
      • Semantic Web Standards (Web 3.0)
      • OMG Model-Driven Message Interoperability (MDMI)
  • Recommended Reading
  • Notes
Tables
Table 1.
Common Synonyms for "Semantic Model"
Table 2.
Common Synonyms for Direct Interfaces
Table 3.
Explicit Concept Metadata
Figures
Figure 1.
A Comparison of Point-to-Point and Canonical Interfaces
Figure 2.
Types of Data Warehouse Evolution
Figure 3.
Semantic Web Metadata as Enterprise Conceptual Map
Figure 4.
Semantic Web Case Studies and Use Cases
Figure 5.
Integration Patterns Involving Semantic Mediation
Figure 6.
Vendor Technologies for Semantics
Figure 7.
Vendor Tools: Use Cases for Managing Semantics
Figure 8.
The Semantic Spectrum
Figure 9.
Concept Management in Dictionaries
Figure 10.
Concept IDs for Precision Across Data Names
Figure 11.
The Varied Scope of Data Sharing Efforts
Figure 12.
Semantic Object (i.e., Data Element) Example
Figure 13.
Context and Data Type Used in Semantic Mediation
Figure 14.
Standards for Managing Semantic Metadata (Examples)
Figure 15.
Data Element Concepts in the ISO/IEC 11179 Standard
Figure 16.
Definitions: Concepts and Conceptual Models (11179 Standard)
Figure 17.
Metadata Best Practices at National Cancer Institute
Figure 18.
UDEF Trees, Concept Names, and IDs
Figure 19.
UDEF Concept Names and IDs for Employee Identification
Figure 20.
UDEF IDs to Bridge Languages (English-Dutch Example)
Figure 21.
SNOMED CT Controlled Vocabulary Example
Figure 22.
Section of an HL7 CDA Clinical Document
Figure 23.
ISO 20022 Illustration of Point-to-Point Connections
Figure 24.
ISO 20022 Illustration of Canonical Model Approach
Figure 25.
Selected Modeling Keywords From RDF and OWL
Figure 26.
Semantic Web Ontology Example: Linked Open Drug Data
Figure 27.
OMG Model-Driven Message Interoperability (MDMI) — Architecture




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