Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time
The relational database is defined as the database by connecting between tables. Each table has a collection of information. The information is processed in the database by using queries, such as data retrieval, data storage, and data conversion. If the information in the table or data has a large s...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Jurusan Ilmu Komputer Universitas Negeri Semarang
2018-05-01
|
| Series: | Scientific Journal of Informatics |
| Subjects: | |
| Online Access: | https://journal.unnes.ac.id/nju/index.php/sji/article/view/12720 |
| _version_ | 1818994490782777344 |
|---|---|
| author | Kukuh Triyuliarno Hidayat Riza Arifudin Alamsyah Alamsyah |
| author_facet | Kukuh Triyuliarno Hidayat Riza Arifudin Alamsyah Alamsyah |
| author_sort | Kukuh Triyuliarno Hidayat |
| collection | DOAJ |
| description | The relational database is defined as the database by connecting between tables. Each table has a collection of information. The information is processed in the database by using queries, such as data retrieval, data storage, and data conversion. If the information in the table or data has a large size, then the query process to process the database becomes slow. In this paper, Genetic Algorithm is used to process queries in order to optimize and reduce query execution time. The results obtained are query execution with genetic algorithm optimization to show the best execution time. The genetic algorithm processes the query by changing the structure of the relation and rearranging it. The fitness value generated from the genetic algorithm becomes the best solution. The fitness used is the highest fitness of each experiment results. In this experiment, the database used is MySQL sample database which is named as employees. The database has a total of over 3,000,000 rows in 6 tables. Queries are designed by using 5 relations in the form of a left deep tree. The execution time of the query is 8.14247 seconds and the execution time after the optimization of the genetic algorithm is 6.08535 seconds with the fitness value of 0.90509. The time generated after optimization of the genetic algorithm is reduced by 25.3%. It shows that genetic algorithm can reduce query execution time by optimizing query in the part of relation. Therefore, query optimization with genetic algorithm can be an alternative solution and can be used to maximize query performance. |
| first_indexed | 2024-12-20T20:58:47Z |
| format | Article |
| id | doaj.art-3c5027fc8555422e91c0c8c9cc01e5c6 |
| institution | Directory Open Access Journal |
| issn | 2407-7658 |
| language | English |
| last_indexed | 2024-12-20T20:58:47Z |
| publishDate | 2018-05-01 |
| publisher | Jurusan Ilmu Komputer Universitas Negeri Semarang |
| record_format | Article |
| series | Scientific Journal of Informatics |
| spelling | doaj.art-3c5027fc8555422e91c0c8c9cc01e5c62022-12-21T19:26:46ZengJurusan Ilmu Komputer Universitas Negeri SemarangScientific Journal of Informatics2407-76582018-05-015110.15294/sji.v5i1.127207440Genetic Algorithm for Relational Database Optimization in Reducing Query Execution TimeKukuh Triyuliarno HidayatRiza ArifudinAlamsyah AlamsyahThe relational database is defined as the database by connecting between tables. Each table has a collection of information. The information is processed in the database by using queries, such as data retrieval, data storage, and data conversion. If the information in the table or data has a large size, then the query process to process the database becomes slow. In this paper, Genetic Algorithm is used to process queries in order to optimize and reduce query execution time. The results obtained are query execution with genetic algorithm optimization to show the best execution time. The genetic algorithm processes the query by changing the structure of the relation and rearranging it. The fitness value generated from the genetic algorithm becomes the best solution. The fitness used is the highest fitness of each experiment results. In this experiment, the database used is MySQL sample database which is named as employees. The database has a total of over 3,000,000 rows in 6 tables. Queries are designed by using 5 relations in the form of a left deep tree. The execution time of the query is 8.14247 seconds and the execution time after the optimization of the genetic algorithm is 6.08535 seconds with the fitness value of 0.90509. The time generated after optimization of the genetic algorithm is reduced by 25.3%. It shows that genetic algorithm can reduce query execution time by optimizing query in the part of relation. Therefore, query optimization with genetic algorithm can be an alternative solution and can be used to maximize query performance.https://journal.unnes.ac.id/nju/index.php/sji/article/view/12720relational database, query, genetic algorithm, fitness, execution time |
| spellingShingle | Kukuh Triyuliarno Hidayat Riza Arifudin Alamsyah Alamsyah Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time Scientific Journal of Informatics relational database, query, genetic algorithm, fitness, execution time |
| title | Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time |
| title_full | Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time |
| title_fullStr | Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time |
| title_full_unstemmed | Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time |
| title_short | Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time |
| title_sort | genetic algorithm for relational database optimization in reducing query execution time |
| topic | relational database, query, genetic algorithm, fitness, execution time |
| url | https://journal.unnes.ac.id/nju/index.php/sji/article/view/12720 |
| work_keys_str_mv | AT kukuhtriyuliarnohidayat geneticalgorithmforrelationaldatabaseoptimizationinreducingqueryexecutiontime AT rizaarifudin geneticalgorithmforrelationaldatabaseoptimizationinreducingqueryexecutiontime AT alamsyahalamsyah geneticalgorithmforrelationaldatabaseoptimizationinreducingqueryexecutiontime |