Data Clustering for Effective Mapping of Object Models to Relational Models

Data Clustering for Effective Mapping of Object Models to Relational Models

Narasimha Bolloju (City University of Hong Kong, Hong Kong) and Kranti Toraskar (City University of Hong Kong, Hong Kong)
Copyright: © 1997 |Pages: 9
DOI: 10.4018/jdm.1997100102
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Abstract

Today the object-oriented model is increasingly used during the analysis and design stages of information systems development, while relational database management systems (RDBMS) are still the most popular implementation tools. Consequently, in practice it is becoming increasingly common to map the object model to an appropriate relational model. This mapping often results in excessively fragmented tables, and denormalization is a commonly used approach for improving the system performance in such cases. However, denormalization affects the flexibility, integrity and data accessibility of implementation, while reducing correspondence between the implementation and the original object model. Based on a particular type of physical data organization, called data clustering, this paper presents an approach to avoid or minimize the need for denormalization. We first examine the use of denormalization and discuss the associated problems in the context of mapping object models to relational models. Next, we present the concept of data clustering and its effect on the performance and storage requirements. Finally, we describe and illustrate how data clustering can be employed to avoid denormalization and to achieve a greater degree of correspondence between an object model and its relational implementation. We also discuss the various trade-offs involved in the use of data clustering.

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