DocumentCode
3584970
Title
An efficient multi join query optimization for DBMS using swarm intelligent approach
Author
Zager Al Saedi, Ahmed Khalaf ; Bt Ghazali, Rozaida ; Bin Mat Deris, Mustafa
Author_Institution
Fac. of Comput. Sci. & Inf. Technol., Univ. Tun Hussein Onn Malaysia, Parit Raja, Malaysia
fYear
2014
Firstpage
113
Lastpage
117
Abstract
In the era of information technology, various professions are Multi Join Query Optimization (MJQO) in database management system (DBMS) such as Search engine, Data mining, Decision support system, Data warehouse and Banking system, Information retrieval (IR) make very important field to study. The increase in the amount of database and number of tables and blocks in database as well as inadequate technology have caused the multi join query optimization (MJQO) problem to remain unsolved. The problems of MJQO include the large size of queries (measure of numbers in join relation), high processing cost and long query execution time. Using swarm intelligent approaches as method to solve (MJQO) problem is practical and provide benefits for DBMS. This paper gives out a Swarm intelligence (Bees Algorithm) method towards the optimization of DBMS queries, And the propose method find Reasonable solution more efficiency than PSO algorithm, which fastest convergence rate among all known solution for MJQO. It reduces the response time of query processing. Simulation shows proposed algorithm can solve MJQO problem in less amount of time than particle swarm optimization (PSO).
Keywords
database management systems; particle swarm optimisation; query processing; swarm intelligence; DBMS; MJQO; PSO; bees algorithm; database management system; information technology; multijoin query optimization; particle swarm optimization; query processing; swarm intelligent approach; Algorithm design and analysis; Computer science; Information technology; Optimization; Particle swarm optimization; Query processing; Artificial bees colony; Database Management system; Multi Join Query Optimization; Query Execution Plan; Query Execution Time; particle swarm optimization (PSO);
fLanguage
English
Publisher
ieee
Conference_Titel
Information and Communication Technologies (WICT), 2014 Fourth World Congress on
Print_ISBN
978-1-4799-8114-4
Type
conf
DOI
10.1109/WICT.2014.7077312
Filename
7077312
Link To Document