Page 378 - 《软件学报》2026年第2期

姬涛 等: AI 赋能的关系型数据库系统研究: 标准化、技术与挑战 857

[212] Wang F, Yan X, Yiu ML, Li S, Mao ZY, Tang B. Speeding up end-to-end query execution via learning-based progressive cardinality
estimation. Proc. of the ACM on Management of Data, 2023, 1(1): 28. [doi: 10.1145/3588708]
[213] Li BB, Lu Y, Kandula S. Warper: Efficiently adapting learned cardinality estimators to data and workload drifts. In: Proc. of the 2022
Int’l Conf. on Management of Data. Philadelphia: ACM, 2022. 1920–1933. [doi: 10.1145/3514221.3526179]
[214] Negi P, Wu ZN, Kipf A, Tatbul N, Marcus R, Madden S, Kraska T, Alizadeh M. Robust query driven cardinality estimation under
changing workloads. Proc. of the VLDB Endowment, 2023, 16(6): 1520–1533. [doi: 10.14778/3583140.3583164]
[215] Wu PZ, Cong G. A unified deep model of learning from both data and queries for cardinality estimation. In: Proc. of the 2021 Int’l Conf.
on Management of Data. ACM, 2021. 2009–2022. [doi: 10.1145/3448016.3452830]
[216] Li PF, Wei WQ, Zhu R, Ding BL, Zhou JR, Lu H. ALECE: An attention-based learned cardinality estimator for SPJ queries on dynamic
workloads. Proc. of the VLDB Endowment, 2023, 17(2): 197–210. [doi: 10.14778/3626292.3626302]
[217] 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]
[218] Marcus R, Negi P, Mao HZ, Zhang C, Alizadeh M, Kraska T, Papaemmanouil O, Tatbul N. Neo: A learned query optimizer. Proc. of the
VLDB Endowment, 2019, 12(11): 1705–1718. [doi: 10.14778/3342263.3342644]
[219] Sun J, Li GL. An end-to-end learning-based cost estimator. Proc. of the VLDB Endowment, 2019, 13(3): 307–319. [doi: 10.14778/
3368289.3368296]
[220] Zhao Y, Cong G, Shi JC, Miao CY. QueryFormer: A tree Transformer model for query plan representation. Proc. of the VLDB
Endowment, 2022, 15(8): 1658–1670. [doi: 10.14778/3529337.3529349]
[221] Hilprecht B, Binnig C. Zero-shot cost models for out-of-the-box learned cost prediction. Proc. of the VLDB Endowment, 2022, 15(11):
2361–2374. [doi: 10.14778/3551793.3551799]
[222] Yang JN, Wu S, Zhang DX, Dai J, Li FF, Chen G. Rethinking learned cost models: Why start from scratch? Proc. of the ACM on
Management of Data, 2023, 1(4): 255. [doi: 10.1145/3626769]
[223] 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]
[224] Ortiz J, Balazinska M, Gehrke J, Keerthi SS. Learning state representations for query optimization with deep reinforcement learning. In:
Proc. of the 2nd Workshop on Data Management for End-to-end Machine Learning. Houston: ACM, 2018. 4. [doi: 10.1145/3209889.
3209890]
[225] Krishnan S, Yang ZH, Goldberg K, Hellerstein J, Stoica I. Learning to optimize join queries with deep reinforcement learning.
arXiv:1808.03196, 2019.
[226] Guo RB, Daudjee K. Research challenges in deep reinforcement learning-based join query optimization. In: Proc. of the 3rd Int’l
Workshop on Exploiting Artificial Intelligence Techniques for Data Management. Portland: ACM, 2020. 3. [doi: 10.1145/3401071.
3401657]
[227] 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]
[228] Chen J, Ye GY, Zhao Y, Liu SC, Deng LW, Chen X, Zhou R, Zheng K. Efficient join order selection learning with graph-based
representation. In: Proc. of the 28th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining. Washington: ACM, 2022.
97–107. [doi: 10.1145/3534678.3539303]
[229] Yang ZH, Chiang WL, Luan SF, Mittal G, Luo M, Stoica I. Balsa: Learning a query optimizer without expert demonstrations. In: Proc.
of the 2022 Int’l Conf. on Management of Data. Philadelphia: ACM, 2022. 931–944. [doi: 10.1145/3514221.3517885]
[230] Chen TY, Gao J, Chen HD, Tu YF. LOGER: A learned optimizer towards generating efficient and robust query execution plans. Proc.
of the VLDB Endowment, 2023, 16(7): 1777–1789. [doi: 10.14778/3587136.3587150]
[231] 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]
[232] Yu X, Chai CL, Li GL, Liu JB. Cost-based or learning-based?: A hybrid query optimizer for query plan selection. Proc. of the VLDB
Endowment, 2022, 15(13): 3924–3936. [doi: 10.14778/3565838.3565846]
[233] 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]
[234] 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]
[235] Woltmann L, Thiessat J, Hartmann C, Habich D, Lehner W. FASTgres: Making learned query optimizer hinting effective. Proc. of the