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Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators
Author(s) -
Panahi Vahideh,
Navimipour Nima Jafari
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5218
Subject(s) - query optimization , computer science , query plan , sargable , join (topology) , genetic algorithm , online aggregation , overhead (engineering) , query expansion , database , web search query , data mining , algorithm , search engine , information retrieval , machine learning , mathematics , combinatorics , operating system
Summary As the main factor in the distributed database systems, query optimization is aimed at finding an optimal execution plan to reduce the runtime. In such systems, because of the repeated relations on various sites, the query optimization is very challenging. Moreover, the query optimization issue with large‐scale distributed databases is an NP‐hard problem. Therefore, in this paper, an Artificial Bee Colony Algorithm based on Genetic Operators (ABC‐GO) is proposed to find a solution to join the query optimization problems in the distributed database systems. The ABC algorithm has the global–local search capabilities and genetic operators to create new candidate solutions for improving the performance of the ABC algorithm. The obtained results have shown that the cost of the query evaluation is minimized and the quality of Top‐K query plans is improved for a given distributed query. Moreover, this method decreases the overhead. However, it needs a longer execution time.