QIACO: A Quantum Dynamic Cost Ant System for Query Optimization in Distributed Database
Query optimization is considered as the most significant part in a model of distributed database. The optimizer tries to find an optimal join order, which reduces the query execution cost. Several factors may affect the cost of query execution, including number of relations, communication costs, res...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9316285/ |
| _version_ | 1818879950652964864 |
|---|---|
| author | Sayed A. Mohsin Saad Mohamed Darwish Ahmed Younes |
| author_facet | Sayed A. Mohsin Saad Mohamed Darwish Ahmed Younes |
| author_sort | Sayed A. Mohsin |
| collection | DOAJ |
| description | Query optimization is considered as the most significant part in a model of distributed database. The optimizer tries to find an optimal join order, which reduces the query execution cost. Several factors may affect the cost of query execution, including number of relations, communication costs, resources, and access to large distributed data sets. The success of a processed query depends heavily on the search methodology that is implemented by the query optimizer. Query processing is considered as NP-hard problem and many researchers are focusing on this problem. Researches are trying to find an appropriate algorithm to seek an ideal solution especially when the size of the database increases. In case of large queries, classical heuristic methods such as ant colony and genetic algorithm can't cover all search space and may lead to falling in a local minimum. In this paper, quantum inspired ant colony algorithm (QIACO), as one of the hybrid strategy of probabilistic algorithms, is utilized to improve the query join cost in the distributed database model. The ability of quantum computing to diversify leads to cover query large search space, which helps in selecting the best trail and thus improves the slow convergence speed and avoid falling into a local optimum. Using this strategy, the algorithm aims to find an optimal join order which minimizes the total execution time. Experimental results show that the proposed model convergence faster with better goodness than the classic ant colony model for same number of ants used. |
| first_indexed | 2024-12-19T14:38:13Z |
| format | Article |
| id | doaj.art-222d71d17e0f486181674a2e47ae84f4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T14:38:13Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-222d71d17e0f486181674a2e47ae84f42022-12-21T20:17:11ZengIEEEIEEE Access2169-35362021-01-019158331584610.1109/ACCESS.2021.30495449316285QIACO: A Quantum Dynamic Cost Ant System for Query Optimization in Distributed DatabaseSayed A. Mohsin0https://orcid.org/0000-0003-1349-564XSaad Mohamed Darwish1https://orcid.org/0000-0003-2723-1549Ahmed Younes2Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, Alexandria, EgyptDepartment of Information Technology, Institute of Graduate Studies and Research, Alexandria University, Alexandria, EgyptDepartment of Mathematics & Computer Science, Faculty of Science, Alexandria University, Alexandria, EgyptQuery optimization is considered as the most significant part in a model of distributed database. The optimizer tries to find an optimal join order, which reduces the query execution cost. Several factors may affect the cost of query execution, including number of relations, communication costs, resources, and access to large distributed data sets. The success of a processed query depends heavily on the search methodology that is implemented by the query optimizer. Query processing is considered as NP-hard problem and many researchers are focusing on this problem. Researches are trying to find an appropriate algorithm to seek an ideal solution especially when the size of the database increases. In case of large queries, classical heuristic methods such as ant colony and genetic algorithm can't cover all search space and may lead to falling in a local minimum. In this paper, quantum inspired ant colony algorithm (QIACO), as one of the hybrid strategy of probabilistic algorithms, is utilized to improve the query join cost in the distributed database model. The ability of quantum computing to diversify leads to cover query large search space, which helps in selecting the best trail and thus improves the slow convergence speed and avoid falling into a local optimum. Using this strategy, the algorithm aims to find an optimal join order which minimizes the total execution time. Experimental results show that the proposed model convergence faster with better goodness than the classic ant colony model for same number of ants used.https://ieeexplore.ieee.org/document/9316285/Distributed database systemquantum computingquery optimizationant colony optimization |
| spellingShingle | Sayed A. Mohsin Saad Mohamed Darwish Ahmed Younes QIACO: A Quantum Dynamic Cost Ant System for Query Optimization in Distributed Database IEEE Access Distributed database system quantum computing query optimization ant colony optimization |
| title | QIACO: A Quantum Dynamic Cost Ant System for Query Optimization in Distributed Database |
| title_full | QIACO: A Quantum Dynamic Cost Ant System for Query Optimization in Distributed Database |
| title_fullStr | QIACO: A Quantum Dynamic Cost Ant System for Query Optimization in Distributed Database |
| title_full_unstemmed | QIACO: A Quantum Dynamic Cost Ant System for Query Optimization in Distributed Database |
| title_short | QIACO: A Quantum Dynamic Cost Ant System for Query Optimization in Distributed Database |
| title_sort | qiaco a quantum dynamic cost ant system for query optimization in distributed database |
| topic | Distributed database system quantum computing query optimization ant colony optimization |
| url | https://ieeexplore.ieee.org/document/9316285/ |
| work_keys_str_mv | AT sayedamohsin qiacoaquantumdynamiccostantsystemforqueryoptimizationindistributeddatabase AT saadmohameddarwish qiacoaquantumdynamiccostantsystemforqueryoptimizationindistributeddatabase AT ahmedyounes qiacoaquantumdynamiccostantsystemforqueryoptimizationindistributeddatabase |