Page 300 - 《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 297

ICDE.2009.130]
[46] Li JX, König AC, Narasayya V, Chaudhuri S. Robust estimation of resource consumption for SQL queries using statistical techniques.
Proc. of the VLDB Endowment, 2012, 5(11): 1555–1566. [doi: 10.14778/2350229.2350269]
[47] Marcus R, Papaemmanouil O. Plan-structured deep neural network models for query performance prediction. Proc. of the VLDB
Endowment, 2019, 12(11): 1733–1746. [doi: 10.14778/3342263.3342646]
[48] Zhu XD, Sobhani P, Guo HY. Long short-term memory over recursive structures. In: Proc. of the 32nd Int’l Conf. on Machine Learning.
Lille: JMLR.org, 2015. 1604–1612.
[49] Mou LL, Li G, Zhang L, Wang T, Jin Z. Convolutional neural networks over tree structures for programming language processing. In:
Proc. of the 30th AAAI Conf. on Artificial Intelligence. Phoenix: AAAI, 2016. 1287–1293. [doi: 10.1609/aaai.v30i1.10139]
[50] Li Y, Wang LW, Wang S, Sun Y, Peng ZY. A resource-aware deep cost model for big data query processing. In: Proc. of the 38th IEEE
Int’l Conf. on Data Engineering. Kuala: IEEE, 2022. 885–897. [doi: 10.1109/ICDE53745.2022.00071]
[51] Elman JL. Finding structure in time. Cognitive Science, 1990, 14(2): 179–211. [doi: 10.1207/s15516709cog1402_1]
[52] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8): 1735–1780. [doi: 10.1162/neco.1997.9.8.
1735]
[53] Tai KS, Socher R, Manning CD. Improved semantic representations from tree-structured long short-term memory networks. In: Proc. of
the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th Int’l Joint Conf. on Natural Language Processing
(Vol. 1: Long Papers). Beijing: Association for Computational Linguistics, 2015. 1556–1566. [doi: 10.3115/v1/P15-1150]
[54] LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc. of the IEEE, 1998, 86(11):
2278–2324. [doi: 10.1109/5.726791]
[55] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. In: Proc. of the
31st Int’l Conf. on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017. 6000–6010.
[56] Yuan HT, Li GL, Feng L, Sun J, Han Y. Automatic view generation with deep learning and reinforcement learning. In: Proc. of the 36th
IEEE Int’l Conf. on Data Engineering. Dallas: IEEE, 2020. 1501–1512. [doi: 10.1109/ICDE48307.2020.00133]
[57] Liu J, Dong WQ, Zhou QQ, Li D. Fauce: Fast and accurate deep ensembles with uncertainty for cardinality estimation. Proc. of the
VLDB Endowment, 2021, 14(11): 1950–1963. [doi: 10.14778/3476249.3476254]
[58] Ding BL, Das S, Marcus R, Wu WT, Chaudhuri S, Narasayya VR. AI meets AI: Leveraging query executions to improve index
recommendations. In: Proc. of the 2019 Int’l Conf. on Management of Data. Amsterdam: ACM, 2019. 1241–1258. [doi: 10.1145/
3299869.3324957]
[59] Marcus R, Papaemmanouil O. Deep reinforcement learning for join order enumeration. In: Proc. of the 1st Int’l Workshop on Exploiting
Artificial Intelligence Techniques for Data Management. Houston: ACM, 2018. 3. [doi: 10.1145/3211954.3211957]
[60] Yu X, Li GL, Chai CL, Tang N. Reinforcement learning with tree-LSTM for join order selection. In: Proc. of the 36th IEEE Int’l Conf.
on Data Engineering. Dallas: IEEE, 2020. 1297–1308. [doi: 10.1109/ICDE48307.2020.00116]
[61] Marcus R, Negi P, Mao HZ, Tatbul N, Alizadeh M, Kraska T. Bao: Making learned query optimization practical. In: Proc. of the 2021
Int’l Conf. on Management of Data. ACM, 2021. 1275–1288. [doi: 10.1145/3448016.3452838]
[62] Zeng TJ, Lan JW, Ma JH, Wei WQ, Zhu R, Zhou YL, Li PF, Ding BL, Lian DF, Wei ZW, Zhou JR. PRICE: A pretrained model for
cross-database cardinality estimation. Proc. of the VLDB Endowment, 2024, 18(3): 637–650. [doi: 10.14778/3712221.3712231]
[63] Han YX, Wu ZN, Wu PZ, Zhu R, Yang JY, Tan LW, Zeng K, Cong G, Qin YZ, Pfadler A, Qian ZP, Zhou JR, Li JN, Cui B. Cardinality
estimation in DBMS: A comprehensive benchmark evaluation. Proc. of the VLDB Endowment, 2021, 15(4): 752–765. [doi: 10.14778/
3503585.3503586]
[64] Transaction processing performance council (TPC). TPC-H Version 2 and Version 3. 2021. http://www.tpc.org/tpch/
[65] Zhu R, Chen W, Ding BL, Chen XG, Pfadler A, Wu ZN, Zhou JR. Lero: A learning-to-rank query optimizer. Proc. of the VLDB
Endowment, 2023, 16(6): 1466–1479. [doi: 10.14778/3583140.3583160]
[66] Chen XG, Zhu R, Ding BL, Wang SB, Zhou JR. Lero: Applying learning-to-rank in query optimizer. The VLDB Journal, 2024, 33(5):
1307–1331. [doi: 10.1007/s00778-024-00850-3]
[67] Xu XH, Zhao ZB, Zhang TY, Kang R, Sun LM, Chen JJ. COOOL: A learning-to-rank approach for SQL hint recommendations.
arXiv:2304.04407, 2023.
[68] Lim WS, Ma L, Zhang W, Butrovich M, Arch S, Pavlo A. Hit the gym: Accelerating query execution to efficiently bootstrap behavior
models for self-driving database management systems. Proc. of the VLDB Endowment, 2024, 17(11): 3680–3693. [doi: 10.14778/