Genetic algorithm for the multiple-query optimization problem

Date

2007-01-01

Author

Bayir, Murat Ali
Toroslu, İsmail Hakkı
Coşar, Ahmet

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

169
views

0
downloads

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 algorithms.

Subject Keywords

Database Query Processing, Genetic Algorithms (GA), Heuristics Techniques, Multiple-Query Optimization (MQO)

URI

https://hdl.handle.net/11511/32476

Journal

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS

DOI

https://doi.org/10.1109/tsmcc.2006.876060

Collections

Graduate School of Natural and Applied Sciences, Article

Suggestions

OpenMETU
Core

Improving the performance of Hadoop/Hive by sharing scan and computation tasks Özal, Serkan; Toroslu, İsmail Hakkı; Doğaç, Asuman; Department of Computer Engineering (2013) MapReduce is a popular model of executing time-consuming analytical queries as a batch of tasks on large scale data. During simultaneous execution of multiple queries, many oppor- tunities can arise for sharing scan and/or computation tasks. Executing common tasks only once can reduce the total execution time of all queries remarkably. Therefore, we propose to use Multiple Query Optimization (MQO) techniques to improve the overall performance of Hadoop Hive, an open source SQL-based distributed warehouse sy...
PARALLEL MULTILEVEL FAST MULTIPOLE ALGORITHM FOR COMPLEX PLASMONIC METAMATERIAL STRUCTURES Ergül, Özgür Salih (2013-11-09) A parallel implementation of the multilevel fast multipole algorithm (MLFMA) is developed for fast and accurate solutions of electromagnetics problems involving complex plasmonic metamaterial structures. Composite objects that consist of multiple penetrable regions, such as dielectric, lossy, and plasmonic parts, are formulated rigorously with surface integral equations and solved iteratively via MLFMA. Using the hierarchical strategy for the parallelization, the developed implementation is capable of simul...
Integer Linear Programming Solution for the Multiple Query Optimization Problem Dokeroglu, Tansel; Bayir, Murat Ali; Coşar, Ahmet (2014-10-28) Multiple Query Optimization (MQO) is a technique for processing a batch of queries in such a way that shared tasks in these queries are executed only once, resulting in significant savings in the total evaluation. The first phase of MQO requires producing alternative query execution plans so that the shared tasks between queries are identified and maximized. The second phase of MQO is an optimization problem where the goal is selecting exactly one of the alternative plans for each query to minimize the tota...
Using object-oriented materialized views to answer selection-based complex queries Alhajj, R; Polat, Faruk (1999-09-01) Presented in this paper is a model that utilizes existing materialized views to handle a wide range of complex selection-based queries, including linear recursive queries. Such queries are complex because it is almost impossible for naive users to predict the formulation of their predicate expressions. Object variables bound to objects in the result of a query are allowed to appear in the predicate of that query. Also, the predicate definition is extended to make it possible to have in the output only a sub...
Genetic algorithm for the personnel assignment problem with multiple objectives Toroslu, İsmail Hakkı (2007-02-01) The assignment problem is a well-known graph optimization problem defined on weighted-bipartite graphs. The objective of the standard assignment problem is to maximize the summation of the weights of the matched edges of the bipartite graph. In the standard assignment problem, any node in one partition can be matched with any node in the other partition without any restriction. In this paper, variations of the standard assignment problem are defined with matching constraints by introducing structures in the...

Citation Formats

M. A. Bayir, İ. H. Toroslu, and A. Coşar, “Genetic algorithm for the multiple-query optimization problem,” IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, pp. 147–153, 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32476.