Estimating the cost of updates in a relational database
ACM Transactions on Database Systems (TODS)
R* optimizer validation and performance evaluation for local queries
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
The EXODUS optimizer generator
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Logic-based approach to semantic query optimization
ACM Transactions on Database Systems (TODS)
A method for automatic rule derivation to support semantic query optimization
ACM Transactions on Database Systems (TODS)
An approach to multikey sequencing in an equiprobable keyterm retrieval situation
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Design and Implementation of a Semantic Query Optimizer
IEEE Transactions on Knowledge and Data Engineering
Automatic Knowledge Acquisition and Maintenance for Semantic Query Optimization
IEEE Transactions on Knowledge and Data Engineering
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Learning Transformation Rules for Semantic Query Optimization: A Data-Driven Approach
IEEE Transactions on Knowledge and Data Engineering
A Formal Model of Trade-off between Optimization and Execution Costs in Semantic Query Optimization
VLDB '88 Proceedings of the 14th International Conference on Very Large Data Bases
QUIST: a system for semantic query optimization in relational databases
VLDB '81 Proceedings of the seventh international conference on Very Large Data Bases - Volume 7
A Fast Method for Ensuring the Consistence of Integrity Constraints
DEXA '99 Proceedings of the 10th International Conference on Database and Expert Systems Applications
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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 efficiently 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 efficiently 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.