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A Methodology for Clustering Entity Relationship Models — A Human Information Processing Approach

  • Daniel L. Moody
  • Andrew Flitman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1728)

Abstract

This paper defines a method for decomposing a large data model into a hierarchy of models of manageable size. The purpose of this is to (a) improve user understanding and (b) simplify documentation and maintenance. Firstly, a set of principles is defined which prescribe the characteristics of a “good” decomposition. These principles may be used to evaluate the quality of a decomposition and to choose between alternatives. Based on these principles, a manual procedure is described which can be used by a human expert to produce a relatively optimal clustering. Finally, a genetic algorithm is described which automatically finds an optimal decomposition. A key differentiating factor between this and previous approaches is that it is soundly based on principles of human information processing—this ensures that data models are clustered in a way that can be most efficiently processed by the human mind.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Daniel L. Moody
    • 1
  • Andrew Flitman
    • 2
  1. 1.Department of Information SystemsUniversity of MelbourneMelbourne
  2. 2.School of Business SystemsMonash UniversityAustralia

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