A fast transformation method to semantic query optimisation

Abstract

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.

Extended Query Answering Using Integrity Rules

“…The process of evaluating this changed state against the integrity rules is then described in section 4 while finally our conclusions are presented in section 5. This work is an extension to the programme of research, by the authors, into the generation, evaluation and application of rules in databases [6,7,8,11].…”

Statistical query-based rule derivation system by backward elimination algorithm

“…The first component of the approach is to represent a query in a query language in order to express it in a way that can be understood by the chosen database environment. Many Relational Database Management Systems use the Structural Query Language and then internally the relational algebra and query trees are mainly used for this purpose [3,20,10,6].…”

“…In the second situation, the answer set can be found from the matching rules, without any need to access the database. These special situations are the most profitable occurrences of the approach, with substantial time savings (%99) reported in [20]. Otherwise the query optimiser constructs alternative queries using the rule set and then selects the best alternative as an optimum query.…”

“…Otherwise the query optimiser constructs alternative queries using the rule set and then selects the best alternative as an optimum query. In most situations, the execution time of queries could be reduced, especially when the consequent of a rule contains the primary key and indexes [13,7,3,20,10,6,1]. Moreover if n is the number of the matching rules, the number of alternative queries can be found from the equation of (2 n − 1).…”

A Fast Method for Ensuring the Consistency of Integrity Constraints

“…Thus recent developments in query optimisation [1,9,10,11,14] make use of the semantic information inherent in integrity constraints, together with rules derived from the data itself, to transform user queries into semantically equivalent alternatives which execute in far less time. Also many systems, such as taxation and social security, now incorporate facilities for interrogating the constraint base to provide answers to hypothetical 'what if' questions, e.g.…”