Semantic query optimisation is a comparatively recent approach for the transformation of a given query into equivalent alternative queries using matching rules in order to select an optimum query based on the costs of executing of these alternative queries. The most important aspect of this optimisation approach is that this resultant query can be processed more eflciently than the original query. This paper describes how a near optimal alternative query may be found in far less time than existing approaches. The method uses the concept of a 'search ratio' associated with each matching rule. The search ratio of a matching rule is based on the cost of the antecedent and consequent conditions of the rule. This cost is related to the number of instances in the database determined by these conditions. This knowledge about the number of instances is available and can be recorded when the rules are first derived. We then compare search ratios of rules to select the most restrictive rules for the construction of a near optimum query. The technique works eflciently regardless of the number of matching rules, since resources are not used to construct all alternative queries. This means that transformation and selection costs are minimised in our system. It is hoped that this method will prove a viable alternative to the expensive optimisation process normally associated with semantic query optimisation.

Abstract. Data analysis is needed in connection with query processing, to produce data summary information in the form of rules or assertions that allow semantic query optimisation or direct query answering without consulting the data itself. The goal of an intelligent analyser in this context is to produce robust rules, stable in the presence of data changes, which allow easy rule maintenance as data changes, and provide rapid query reformulation, refutation or answering. It must also limit the rule set to rules useful for query processing.

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Abstract. Semantic Query Optimisation makes use of the semantic knowledge of a database (rules) to perform query transformation. Rules are normally learned from former queries fired by the user. Over time, however, this can result in the rule set becoming very large thereby degrading the efficiency of the system as a whole. Such a problem is known as the utility problem. This paper seeks to provide a solution to the utility problem through the use of statistical techniques in selecting and maintaining an optimal rule set. Statistical methods have, in fact, been used widely in the field of Knowledge Discovery to identify and measure relationships between attributes. Here we extend the approach to Semantic Query Optimisation using the Chi-square statistical method which is integrated into a prototype query optimiser developed by the authors. We also present a new technique for calculating Chi-square, which is faster and more efficient than the traditional method in this situation.

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