Heuristic Optimization of Physical Data Bases: Using a Generic and Abstract Design Model

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Prashant Palvia, Joe Rosenthal Excellence Professor and Director of the McDowell Research Center for Global IT Management (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: Designing efficient physical data bases is a complex activity, involving the consideration of a large number of factors. Mathematical programming-based optimization models for physical design make many simplifying assumptions; thus, their applicability is limited. In this article, we show that heuristic algorithms can be successfully used in the development of very good, physical data base designs. Two heuristic optimization algorithms are proposed in the contest of a genetic and abstract model for physical design. One algorithm is based on generic principles of heuristic optimization. The other is based on capturing and using problem-specific information in the heuristics. The goodness of the algorithms is demonstrated over a wide range of problems and factor values.

Additional Information

Decision Sciences, Summer 1988, Vol. 19, No. 3, pp. 564-579.
Language: English
Date: 1988
Heuristics, Management Information Systems, Simulation

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