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Managing operations research models for decision support systems applications in a database environment

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  • Michael Ball
  • Anindya Datta

Abstract

In this paper, we address the problem of building decision support systems that make use of multiple operations research models as database applications. The motivation for developing applications in a database environment is that, by doing so, the development effort can be substantially reduced, while, at the same time, the application inherits valuable database features. The paper contains two main contributions. First, we present a set of modeling constructs that should aid developers in structuring such applications and in carrying out the development process. Included in this material is a fairly comprehensive model for handling versions. Second, we discuss certain design alternatives and evaluate performance tradeoffs associated with them. In addition to evaluating the differences among competing database designs, we provide evidence that properly designed database applications show little performance degradation over file based applications. Copyright Kluwer Academic Publishers 1997

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  • Michael Ball & Anindya Datta, 1997. "Managing operations research models for decision support systems applications in a database environment," Annals of Operations Research, Springer, vol. 72(0), pages 151-182, January.
    • Handle: RePEc:spr:annopr:v:72:y:1997:i:0:p:151-182:10.1023/a:1018944204186
    DOI: 10.1023/A:1018944204186
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    Cited by:

    1. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.

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