Optimizing SQL Query Execution Time: A Hybrid Approach Using Machine Learning and Deep Learning Technique
Keywords:
Sql, Query optimization, machine learning, deep learning.Abstract
The escalating volume of global data in recent years has posed significant challenges to data management and analysis, particularly regarding query and processing speeds. In response to these challenges, the present research endeavors to advance large-scale data analytics by accelerating query processing and data retrieval by applying machine learning approaches. The proposed innovative machine learning model aims to improve data retrieval speeds and enhance analytical accuracy. By leveraging the estimated execution time as a guiding metric, the research provides a compass for optimizing query performance. This enables informed decision-making to meet performance requirements and ensures efficient resource utilization within real-time database systems. Notably, the hybrid method introduced in this study demonstrates a reduction in processing time and memory usage, signifying a comprehensive approach to enhancing the efficiency of data management and analysis in the face of burgeoning data volumes.
Downloads
References
Vaidya, K., Dutt, A., Narasayya, V., & Chaudhuri, S. (2021). Leveraging query logs and machine learning for parametric query optimization. Proceedings of the VLDB Endowment, 15(3), 401-413.
Marcus, R., Negi, P., Mao, H., Tatbul, N., Alizadeh, M., & Kraska, T. (2021, June). Bao: Making learned query optimization practical. In Proceedings of the 2021 International Conference on Management of Data (pp. 1275-1288).
Kumar, A., Boehm, M., & Yang, J. (2017, May). Data management in machine learning: Challenges, techniques, and systems. In Proceedings of the 2017 ACM International Conference on Management of Data (pp. 1717-1722).
Vu, T. (2019, June). Deep query optimization. In Proceedings of the 2019 International Conference on Management of Data (pp. 1856-1858).
Azhir, E., Navimipour, N. J., Hosseinzadeh, M., Sharifi, A., & Darwesh, A. (2019). Query optimization mechanisms in the cloud environments: a systematic study. International Journal of Communication Systems, 32(8), e3940.
Park, K., Saur, K., Banda, D., Sen, R., Interlandi, M., &Karanasos, K. (2022, June). End-to-end optimization of machine learning prediction queries. In Proceedings of the 2022 International Conference on Management of Data (pp. 587-601).
Krishnan, S., Yang, Z., Goldberg, K., Hellerstein, J., & Stoica, I. (2018). Learning to optimize join queries with deep reinforcement learning. arXiv preprint arXiv:1808.03196.
Kaoudi, Z., Quiané-Ruiz, J. A., Contreras-Rojas, B., Pardo-Meza, R., Troudi, A., & Chawla, S. (2020, April). ML-based cross-platform query optimization. In 2020 IEEE 36th International Conference on Data Engineering (ICDE) (pp. 1489-1500). IEEE.
Sikdar, S. (2021). Applying Machine Learning to Query Optimization (Doctoral dissertation, Rice University).
Yang, Z., Wang, Z., Huang, Y., Lu, Y., Li, C., & Wang, X. S. (2022). Optimizing machine learning inference queries with correlative proxy models. arXiv preprint arXiv:2201.00309.
Hasan, R., & Gandon, F. (2014, August). A machine learning approach to sparql query performance prediction. In 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) (Vol. 1, pp. 266-273). IEEE.
Doshi, L., Zhuang, V., Jain, G., Marcus, R., Huang, H., Altinbüken, D., ... & Fraser, C. (2023). Kepler: Robust Learning for Parametric Query Optimization. Proceedings of the ACM on Management of Data, 1(1), 1-25.
Ma, Q., & Triantafillou, P. (2019, June). Dbest: Revisiting approximate query processing engines with machine learning models. In Proceedings of the 2019 International Conference on Management of Data (pp. 1553-1570).
Wu, W., Chi, Y., Zhu, S., Tatemura, J., Hacigümüs, H., & Naughton, J. F. (2013, April). Predicting query execution time: Are optimizer cost models really unusable?. In 2013 IEEE 29th International Conference on Data Engineering (ICDE) (pp. 1081-1092). IEEE.
Bzdok, D., Nichols, T. E., & Smith, S. M. (2019). Towards algorithmic analytics for large-scale datasets. Nature Machine Intelligence, 1(7), 296-306.
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.
Li, Q., Chen, Y., Wang, J., Chen, Y., & Chen, H. (2017). Web media and stock markets: A survey and future directions from a big data perspective. IEEE Transactions on Knowledge and Data Engineering, 30(2), 381-399.
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Zheng, X. (2016). {TensorFlow}: a system for {Large-Scale} machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265-283).
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
