Optimization in Deductive and Conventional Relational Database Systems
A deductive relational database system is one which permits new relations to be derived from given relations stored in a conventional relational database system, and from axioms. It has been shown that a query in a deductive relational database system can be transformed, using the axioms, into a query that involves searches only over the relational database. The transformed query results in a set of conjuncts which generally share similar if not identical searches that must be made of the indexes and the tables storing the relations. The purpose of this paper is to describe a “global” optimizing algorithm which accounts for similarities between conjuncts.
The algorithm consists of two major parts: the preprocessor and the optimizer. The preprocessor is used once for a given set of axioms and indexes. Its functions are to: transform each atomic query type into a group of formulae, list all possible access methods for single tables and join-supported joins and to calculate costs for the access methods. The optimizer is used to select a method of evaluation of the formulae which answers the query in the shortest possible time. Details concerning the preprocessor and the optimizer are provided. An example is given that shows the effectiveness of “global” optimization in contrast to optimizing the retrieval of individual conjuncts. The changes needed to incorporate semantic knowledge into the algorithm are also given.
KeywordsDeductive System Access Method Semantic Knowledge Disjunctive Normal Form Relational Database System
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