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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Moody, D.L., Flitman, A. (1999). A Methodology for Clustering Entity Relationship Models — A Human Information Processing Approach. In: Akoka, J., Bouzeghoub, M., Comyn-Wattiau, I., Métais, E. (eds) Conceptual Modeling — ER ’99. ER 1999. Lecture Notes in Computer Science, vol 1728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47866-3_8
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