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A Logical Model of Conceptual Integrity in Data Integration.

David Flater1

  • 1National Institute of Standards and Technology, Gaithersburg, MD 20899-8264.

Journal of Research of the National Institute of Standards and Technology
|July 15, 2016
PubMed
Summary

Maintaining conceptual integrity is crucial for effective data integration. This study introduces a logical model to describe both correct and incorrect data integrations, aiding in the analysis of legacy system flaws.

Keywords:
abstractiondataintegrationlogicsemantics

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Area of Science:

  • Computer Science
  • Information Science
  • Data Management

Background:

  • Data integration aims to create cohesive and sensible results.
  • Compromised conceptual integrity leads to semantic faults and integration bugs.
  • Existing models often focus on preventing faults, limiting analysis of imperfect systems.

Purpose of the Study:

  • To present a logical model for conceptual integrity in data integration.
  • To enable the description of both correct and incorrect integrations.
  • To facilitate formal analysis of flaws in imperfect legacy systems.

Main Methods:

  • Development of a logical model for conceptual integrity.
  • Application of the model to a simple example.
  • Comparison with constructive models that prevent semantic faults.

Main Results:

  • The proposed model can describe both valid and invalid data integrations.
  • It allows for the formal analysis of semantic faults.
  • It provides a framework for understanding and potentially remedying flaws in legacy systems.

Conclusions:

  • Conceptual integrity is a key factor in successful data integration.
  • The presented model offers a novel approach to analyzing data integration quality.
  • This model supports the formal assessment and remediation of issues in existing data systems.