{"id":855,"date":"2026-03-08T01:04:32","date_gmt":"2026-03-07T17:04:32","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=855"},"modified":"2026-03-21T23:57:22","modified_gmt":"2026-03-21T15:57:22","slug":"slq-lightweight-detection-llm-generation-sql-selfoptimization-framework-2","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=slq-lightweight-detection-llm-generation-sql-selfoptimization-framework-2","title":{"rendered":"SLQ: Lightweight-Detection & LLM-Generation SQL SelfOptimization Framework"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 30<\/a><\/p>\n<\/div>\n\n\n\n

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Zhu Tianyou<\/i><\/a>, Qi Yaru, Jiang Kongchen, Sang Yanting, and Yang Chao<\/p>\n\n\n\n

State Grid Information & Telecommunication center (Big Data center), Beijing 100052, P. R. China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0October 13, 2025
Accepted:\u00a0November 16, 2025
Publication Date:\u00a0March 8, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Schematic Diagram of the SLQ Framework.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 RIS<\/a> | BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202607_30.025<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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In real-world database applications, SQL statements written by users often create performance bottlenecks because they violate best-practice rules. Traditional rule-based detectors have limited ability to recognize diverse and increasingly irregular statements and are costly to maintain. To address this, we propose SLQ, a two-stage intelligent SQL optimization framework. First, a lightweight stacked-LSTM module pinpoints problematic statements; then a pre-trained large language model, Qwen3, automatically generates explanations for each flaw and offers targeted rewrite suggestions, helping users quickly improve query quality. Evaluated on a standard dataset, SLQ achieves accuracy, precision, recall and F1 of 0.9841, 0.9974, 0.9702 and 0.9836 respectively, demonstrating superior detection and optimization capability and markedly enhancing SQL compliance and execution efficiency.<\/p>\n\n\n\n

Keywords:\u00a0SQL Query Optimization; Large Language Model (LLM); Long Short-Term Memory(LSTM)<\/em><\/p>\n\n\n\n

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  1. [1] J. Yuanli, (2005) \u201cOptimization Methods for Database SQL Query Statements” Ordnance Automation 24(6): 113\u2013114. DOI: 10.3969\/j.issn.1006-1576.2005.06.055.<\/li>\n
  2. [2] L. Jiajun and G. Mei, (2022) \u201cResearch and Application of Oracle Query Optimization” Information Technology and Informatization (1): 57\u201360. DOI: 10.3969\/j.issn.1672-9528.2022.01.016.<\/li>\n
  3. [3] Z. Zheng, X. Yukun, J. Chao, et al., (2024) \u201cResearch on Anomaly Detection in Electric Energy Metering Based on Improved Random Forest Algorithm” Electric Measurement and Instrumentation: 1\u20138.<\/li>\n
  4. [4] L. Wei, C. Dongsheng, F. Fuyong, et al., (2024) \u201cResearch on Short-Term Power Load Forecasting Based on DCT-CNN-GRU” Electric Measurement and Instrumentation: 1\u201311.<\/li>\n
  5. [5] S. Jinwei, L. Junni, X. Donghai, et al., (2024) \u201cResearch on Big Data Diagnosis Method for Transformer Condition Abnormality Based on Improved k-medoids Clustering and Carbon Constraint” Electrical Measurement and Instrumentation: 1\u201310.<\/li>\n
  6. [6] H. Dan, Z. Yonggang, L. Shanhua, et al., (2024) \u201cCalculation and Analysis of Theoretical Line Losses in LowVoltage Substations Based on AdaBoost Ensemble Learning Algorithm” Electrical Measurement and Instrumentation: 1\u20139.<\/li>\n
  7. [7] S. Maesaroh, H. Gunawan, A. Lestari, et al., (2022) \u201cQuery optimization in mysql database using index” International Journal of Cyber and IT Service Management 2(2): 104\u2013110.<\/li>\n
  8. [8] S. Palanisamy and P. SuvithaVani. \u201cA survey on RDBMS and NoSQL Databases MySQL vs MongoDB\u201d. In: 2020 international conference on computer communication and informatics (ICCCI). IEEE, 2020, 1\u20137.<\/li>\n
  9. [9] M. Malekpour, N. Shaheen, F. Khomh, et al., (2024) \u201cTowards optimizing sql generation via llm routing” arXiv preprint arXiv:2411.04319:<\/li>\n
  10. [10] M. M. Rahman, S. Islam, M. Kamruzzaman, et al., (2024) \u201cAdvanced query optimization in SQL databases for real-time big data analytics” Academic Journal on Business Administration, Innovation & Sustainability 4(3): 1\u201314.<\/li>\n
  11. [11] Y. Du, Z. Cai, and Z. Ding, (2024) \u201cQuery Optimization in Distributed Database Based on Improved Artificial Bee Colony Algorithm” Applied Sciences 14(2): 846.<\/li>\n
  12. [12] A. Uzzaman, M. M. I. Jim, N. Nishat, et al., (2024) \u201cOptimizing SQL databases for big data workloads: techniques and best practices” Academic Journal on Business Administration, Innovation & Sustainability 4(3): 15\u201329.<\/li>\n
  13. [13] V. B. Ramu, (2023) \u201cOptimizing database performance: Strategies for efficient query execution and resource utilization” International Journal of Computer Trends and Technology 71(7): 15\u201321.<\/li>\n
  14. [14] J. Shao, X. Liu, Y. Li, et al., (2015) \u201cDatabase performance optimization for SQL Server based on hierarchical queuing network model” International Journal of Database Theory and Application 8(1): 187\u2013196.<\/li>\n
  15. [15] K. S. Maabreh. \u201cOptimizing Database Query Performance Using Table Partitioning Techniques\u201d. In: 2018 International Arab Conference on Information Technology (ACIT). IEEE, 2018, 1\u20134.<\/li>\n
  16. [16] J. Zhang. \u201cResearch on database application performance optimization method\u201d. In: 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer. Atlantis Press, 2016, 2236\u20132239.<\/li>\n
  17. [17] V. K. Myalapalli, T. P. Totakura, and S. Geloth. \u201cAugmenting database performance via SQL tuning\u201d. In: 2015 International Conference on Energy Systems and Applications. IEEE, 2015, 13\u201318.<\/li>\n
  18. [18] S. J. Kamatkar, A. Kamble, A. Viloria, et al. \u201cDatabase performance tuning and query optimization\u201d. In: International Conference on Data Mining and Big Data. Springer International Publishing, 2018, 3\u201311.<\/li>\n
  19. [19] X. Sun, B. Jiang, and X. He. \u201cDatabase query optimization based on distributed photovoltaic power generation\u201d. In: 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2018, 2382\u20132386.<\/li>\n
  20. [20] C. Anneser, N. Tatbul, D. Cohen, et al., (2023) \u201cAutosteer: Learned query optimization for any sql database” Proceedings of the VLDB Endowment 16(12): 3515\u20133527.<\/li>\n<\/ol>\n<\/div>\n\n\n\n

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