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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| Format: | Thesis |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/9062/ http://eprints.uthm.edu.my/9062/1/Ahmed_Khalaf_Zager_Al_Saedi.pdf |
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| Summary: | 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 TPABC 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 TPABC, 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 TPABC algorithm is capable of
solving MJQO problems in shorter amounts of time and at lower costs compared to
other approaches. |
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