Improved data retrieval using semantic transformation

Abstract

Semantic query optimisation uses knowledge about properties of the data, represented as a set of subset descriptor rules, to transform a query into another form that can be executed in a more efficient manner but still yields the same result as the original query. Commonly this 'semantic knowledge' in the form of rules is generated either during the query process itself or else is constructed in advance according to defined heuristics. Over a period of time the rule set may, therefore, become very large and the number of semantically equivalent queries that may be derived rises exponentially. Each rule use creates a new equivalent query. The problem is to identify one near optimal alternative query in a time that is minimal and also short relative to the overall query execution time. In this paper we propose a method for measuring the effectiveness of each rule and present a fast algorithm which selects the most cost effective transformations to directly yield the optimal alternative query. Experiments carried out on a large publicly available dataset show worthwhile savings using the approach. © Springer-Verlag Berlin Heidelberg 2004.