An efficient multi join query optimization for relational database management system using swarm intelligence approaches

Currently, it is fairly obvious that the Multi Join Query Optimization (MJQO) is becoming the centre of attention in the context of Database Management System (DBMS). The functions consist of combination of data from multiple tables, reducing the number of needed queries, optimizing the Query Execut...

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Main Author:	Alsaedi, Ahmed Khalaf Zager
Format:	Thesis
Language:	English English English
Published:	2016
Subjects:	QA Mathematics QA76 Computer software
Online Access:	http://eprints.uthm.edu.my/9999/2/24p%20AHMED%20KHALAF%20ZAGER%20ALSAEDI.pdf http://eprints.uthm.edu.my/9999/1/AHMED%20KHALAF%20ZAGER%20ALSAEDI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/9999/3/AHMED%20KHALAF%20ZAGER%20ALSAEDI%20WATERMARK.pdf
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spelling	my-uthm-ep.99992023-09-25T01:38:10Z An efficient multi join query optimization for relational database management system using swarm intelligence approaches 2016-06 Alsaedi, Ahmed Khalaf Zager QA Mathematics QA76 Computer software Currently, it is fairly obvious that the Multi Join Query Optimization (MJQO) is becoming the centre of attention in the context of Database Management System (DBMS). The functions consist of combination of data from multiple tables, reducing the number of needed queries, optimizing the Query Execution Plan (QEP}, and moving processing abounded database servers to enhance both data integrity and performance. MJQO is an optimization task, which serves to locate the optimal QEP of a RDBMS in query processing. A major problem associated with RDBMS is the fact that they are still unable to fully meet the demands of big data. The majority of MJQO techniques encompass solution space at an extremely reduced pace. Many queries attempted to gather information from multiple sites or correlations, while every relation are compelled to answer these query via their limited resources. This lead to the access of data from many locations that are limited in their memory retention capabilities, which inevitably increase the size of the database, the number of the join, and Query Execution Time (QET}. In order to eschew trapping and slow coverage difficulties in the quest to discover the optimal QEP and slow query execution time, this work proposes a total of three optimization algorithm that are based on Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Two-Phase Artificial Bee Colony (TPAPC) to solve the optimization problem in RDBMS Framework. The TP ABC algorithm can be utilized to solve MJQO problems via simulation and increasing exploration and exploitation whilst balancing them for optimal results from giving queries. A directed acyclic graph, based on materialized query graph, aids in the optimization of algorithms and solving MJQO by removing non-promising QEP, which decreases the QEP combination space. Finally, experimental results demonstrate that the performance of TP ABC, when compared to PSO, ACO, and native technique in the context of computational time, is very promising, which is indicative of the fact that the TP ABC algorithm is capable of solving MJQO problems in shorter amounts of time and at lower costs compared to other approaches 2016-06 Thesis http://eprints.uthm.edu.my/9999/ http://eprints.uthm.edu.my/9999/2/24p%20AHMED%20KHALAF%20ZAGER%20ALSAEDI.pdf text en public http://eprints.uthm.edu.my/9999/1/AHMED%20KHALAF%20ZAGER%20ALSAEDI%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/9999/3/AHMED%20KHALAF%20ZAGER%20ALSAEDI%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA Mathematics QA76 Computer software
spellingShingle	QA Mathematics QA76 Computer software Alsaedi, Ahmed Khalaf Zager An efficient multi join query optimization for relational database management system using swarm intelligence approaches
description	Currently, it is fairly obvious that the Multi Join Query Optimization (MJQO) is becoming the centre of attention in the context of Database Management System (DBMS). The functions consist of combination of data from multiple tables, reducing the number of needed queries, optimizing the Query Execution Plan (QEP}, and moving processing abounded database servers to enhance both data integrity and performance. MJQO is an optimization task, which serves to locate the optimal QEP of a RDBMS in query processing. A major problem associated with RDBMS is the fact that they are still unable to fully meet the demands of big data. The majority of MJQO techniques encompass solution space at an extremely reduced pace. Many queries attempted to gather information from multiple sites or correlations, while every relation are compelled to answer these query via their limited resources. This lead to the access of data from many locations that are limited in their memory retention capabilities, which inevitably increase the size of the database, the number of the join, and Query Execution Time (QET}. In order to eschew trapping and slow coverage difficulties in the quest to discover the optimal QEP and slow query execution time, this work proposes a total of three optimization algorithm that are based on Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Two-Phase Artificial Bee Colony (TPAPC) to solve the optimization problem in RDBMS Framework. The TP ABC algorithm can be utilized to solve MJQO problems via simulation and increasing exploration and exploitation whilst balancing them for optimal results from giving queries. A directed acyclic graph, based on materialized query graph, aids in the optimization of algorithms and solving MJQO by removing non-promising QEP, which decreases the QEP combination space. Finally, experimental results demonstrate that the performance of TP ABC, when compared to PSO, ACO, and native technique in the context of computational time, is very promising, which is indicative of the fact that the TP ABC algorithm is capable of solving MJQO problems in shorter amounts of time and at lower costs compared to other approaches
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Alsaedi, Ahmed Khalaf Zager
author_facet	Alsaedi, Ahmed Khalaf Zager
author_sort	Alsaedi, Ahmed Khalaf Zager
title	An efficient multi join query optimization for relational database management system using swarm intelligence approaches
title_short	An efficient multi join query optimization for relational database management system using swarm intelligence approaches
title_full	An efficient multi join query optimization for relational database management system using swarm intelligence approaches
title_fullStr	An efficient multi join query optimization for relational database management system using swarm intelligence approaches
title_full_unstemmed	An efficient multi join query optimization for relational database management system using swarm intelligence approaches
title_sort	efficient multi join query optimization for relational database management system using swarm intelligence approaches
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2016
url	http://eprints.uthm.edu.my/9999/2/24p%20AHMED%20KHALAF%20ZAGER%20ALSAEDI.pdf http://eprints.uthm.edu.my/9999/1/AHMED%20KHALAF%20ZAGER%20ALSAEDI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/9999/3/AHMED%20KHALAF%20ZAGER%20ALSAEDI%20WATERMARK.pdf
_version_	1783728983726096384

An efficient multi join query optimization for relational database management system using swarm intelligence approaches

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