Genetic Algorithm for the Multiple-Query Optimization Problem
- 19 December 2006
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)
- Vol. 37 (1), 147-153
- https://doi.org/10.1109/tsmcc.2006.876060
Abstract
Producing answers to a set of queries with common tasks efficiently is known as the multiple-query optimization (MQO) problem. Each query can have several alternative evaluation plans, each with a different set of tasks. Therefore, the goal of MQO is to choose the right set of plans for queries which minimizes the total execution time by performing common tasks only once. Since MQO is an NP-hard problem, several, mostly heuristics based, solutions have been proposed for solving it. To the best of our knowledge, this correspondence is the first attempt to solve MQO using an evolutionary technique, genetic algorithmsKeywords
This publication has 11 references indexed in Scilit:
- Multi-query optimization for on-line analytical processingInformation Systems, 2003
- Divide and conquer: A basis for augmenting a conventional query optimizer with multiple query-processing capabilitiesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Answering queries using views: A surveyThe VLDB Journal, 2001
- Pipelining in multi-query optimizationPublished by Association for Computing Machinery (ACM) ,2001
- Materialized view selection and maintenance using multi-query optimizationPublished by Association for Computing Machinery (ACM) ,2001
- Efficient and extensible algorithms for multi query optimizationPublished by Association for Computing Machinery (ACM) ,2000
- A framework for global optimization of aggregate queriesPublished by Association for Computing Machinery (ACM) ,1997
- Improvements on a heuristic algorithm for multiple-query optimizationData & Knowledge Engineering, 1994
- Multiple-query optimizationACM Transactions on Database Systems, 1988
- System RACM Transactions on Database Systems, 1976