Page 302 - 《软件学报》2026年第1期

何家豪 等: 智能查询优化算法研究综述 299

[93] Woltmann L, Thiessat J, Hartmann C, Habich D, Lehner W. FASTgres: Making learned query optimizer hinting effective. Proc. of the
VLDB Endowment, 2023, 16(11): 3310–3322. [doi: 10.14778/3611479.3611528]
[94] Anneser C, Tatbul N, Cohen D, Xu ZG, Pandian P, Laptev N, Marcus R. AutoSteer: Learned query optimization for any SQL database.
Proc. of the VLDB Endowment, 2023, 16(12): 3515–3527. [doi: 10.14778/3611540.3611544]
[95] Zhong K, Sun LM, Ji T, Li CP, Chen H. FOSS: A self-learned doctor for query optimizer. In: Proc. of the 40th IEEE Int’l Conf. on Data
Engineering. Utrecht: IEEE, 2024. 4329–4342. [doi: 10.1109/ICDE60146.2024.00330]
[96] Pirahesh H, Hellerstein JM, Hasan W. Extensible/rule based query rewrite optimization in starburst. ACM SIGMOD Record, 1992,
21(2): 39–48. [doi: 10.1145/141484.130294]
2
[97] Li ZDH, Yuan HT, Wang HM, Cong G, Bing LD. LLM-R : A large language model enhanced rule-based rewrite system for boosting
query efficiency. Proc. of the VLDB Endowment, 2024, 18(1): 53–65. [doi: 10.14778/3696435.3696440]
[98] Zhou XH, Li GL, Chai CL, Feng JH. A learned query rewrite system using Monte Carlo tree search. Proc. of the VLDB Endowment,
2021, 15(1): 46–58. [doi: 10.14778/3485450.3485456]
[99] Sun ZY, Zhou XH, Li GL. R-Bot: An LLM-based query rewrite system. arXiv:2412.01661, 2024.
[100] Zhou Q, Arulraj J, Navathe S, Harris W, Wu JP. SIA: Optimizing queries using learned predicates. In: Proc. of the 2021 Int’l Conf. on
Management of Data. ACM, 2021. 2169–2181. [doi: 10.1145/3448016.3457262]
[101] Wang ZG, Zhou Z, Yang YC, Ding HR, Hu GS, Ding D, Tang CZ, Chen HB, Li JY. WeTune: Automatic discovery and verification of
query rewrite rules. In: Proc. of the 2022 Int’l Conf. on Management of Data. Philadelphia: ACM, 2022. 94–107. [doi: 10.1145/3514221.
3526125]
[102] Liu J, Mozafari B. Query rewriting via large language models. arXiv:2403.09060, 2024.
[103] Huang XM, Li HY, Zhang J, Zhao XX, Yao ZM, Li YY, Yu ZH, Zhang TY, Chen H, Li CP. LLMTune: Accelerate database knob
tuning with large language models. arXiv:2404.11581v1, 2024.
[104] Lao JL, Wang YB, Li YF, Wang JP, Zhang YJ, Cheng ZY, Chen WH, Tang MJ, Wang JG. GPTuner: A manual-reading database tuning
system via GPT-guided Bayesian optimization. Proc. of the VLDB Endowment, 2024, 17(8): 1939–1952. [doi: 10.14778/3659437.
3659449]
[105] Zhou XH, Li GL, Sun ZY, Liu ZY, Chen WZ, Wu JM, Liu JS, Feng RH, Zeng GY. D-bot: Database diagnosis system using large
language models. Proc. of the VLDB Endowment, 2024, 17(10): 2514–2527. [doi: 10.14778/3675034.3675043]
[106] Xue SQ, Qi DR, Jiang CG, Cheng FY, Chen KT, Zhang ZP, Zhang HY, Wei GL, Zhao W, Zhou F, Yi H, Liu SD, Yang HJ, Chen FQ.
Demonstration of DB-GPT: Next generation data interaction system empowered by large language models. Proc. of the VLDB
Endowment, 2024, 17(12): 4365–4368. [doi: 10.14778/3685800.3685876]
[107] Fredrikson M, Jha S, Ristenpart T. Model inversion attacks that exploit confidence information and basic countermeasures. In: Proc. of
the 22nd ACM SIGSAC Conf. on Computer and Communications Security. Denver: ACM, 2015. 1322–1333. [doi: 10.1145/2810103.
2813677]
[108] Liu XM, Xie LH, Wang YP, Zou J, Xiong JB, Ying ZB, Vasilakos AV. Privacy and security issues in deep learning: A survey. IEEE
Access, 2021, 9: 4566–4593. [doi: 10.1109/ACCESS.2020.3045078]
[109] Dwork C, Roth A. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science,
2014, 9(3–4): 211–407. [doi: 10.1561/0400000042]

附中文参考文献
[7] 孙路明, 张少敏, 姬涛, 李翠平, 陈红. 人工智能赋能的数据管理技术研究. 软件学报, 2020, 31(3): 600–619. http://www.jos.org.cn/
1000-9825/5909.htm [doi: 10.13328/j.cnki.jos.005909]
[9] 柴茗珂, 范举, 杜小勇. 学习式数据库系统: 挑战与机遇. 软件学报, 2020, 31(3): 806–830. http://www.jos.org.cn/1000-9825/5908.htm
[doi: 10.13328/j.cnki.jos.005908]
[10] 孟小峰, 马超红, 杨晨. 机器学习化数据库系统研究综述. 计算机研究与发展, 2019, 56(9): 1803–1820. [doi: 10.7544/issn1000-
1239.2019.20190446]
[11] 李国良, 周煊赫, 孙佶, 余翔, 袁海涛, 刘佳斌, 韩越. 基于机器学习的数据库技术综述. 计算机学报, 2020, 43(11): 2019–2049. [doi:
10.11897/SP.J.1016.2020.02019]
[12] 宋雨萌, 谷峪, 李芳芳, 于戈. 人工智能赋能的查询处理与优化新技术研究综述. 计算机科学与探索, 2020, 14(7): 1081–1103. [doi:
10.3778/j.issn.1673-9418.1911063]