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 |
| Language: | English English English |
| Published: |
2016
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| 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 |
| 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 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 |
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