Page 299 - 《软件学报》2026年第1期
P. 299
296 软件学报 2026 年第 37 卷第 1 期
Conf. on Management of Data. Portland: ACM, 2020. 1017–1033. [doi: 10.1145/3318464.3389727]
[23] Hasan S, Thirumuruganathan S, Augustine J, Koudas N, Das G. Deep learning models for selectivity estimation of multi-attribute
queries. In: Proc. of the 2020 ACM SIGMOD Int’l Conf. on Management of Data. Portland: ACM, 2020. 1035–1050. [doi: 10.1145/
3318464.3389741]
[24] Yang ZH, Liang E, Kamsetty A, Wu CG, Duan Y, Chen X, Abbeel P, Hellerstein JM, Krishnan S, Stoica I. Deep unsupervised
cardinality estimation. Proc. of the VLDB Endowment, 2019, 13(3): 279–292. [doi: 10.14778/3368289.3368294]
[25] Yang ZH, Kamsetty A, Luan SF, Liang E, Duan Y, Chen X, Stoica I. NeuroCard: One cardinality estimator for all tables. Proc. of the
VLDB Endowment, 2020, 14(1): 61–73. [doi: 10.14778/3421424.3421432]
[26] Zhu R, Wu ZN, Han YX, Zeng K, Pfadler A, Qian ZP, Zhou JR, Cui B. FLAT: Fast, lightweight and accurate method for cardinality
estimation. Proc. of the VLDB Endowment, 2021, 14(9): 1489–1502. [doi: 10.14778/3461535.3461539]
[27] Kim K, Lee S, Kim I, Han WS. ASM: Harmonizing autoregressive model, sampling, and multi-dimensional statistics merging for
cardinality estimation. Proc. of the ACM on Management of Data, 2024, 2(1): 45. [doi: 10.1145/3639300]
[28] Lee S, Kim K, Han WS. ASM in action: Fast and practical learned cardinality estimation. In: Companion of the 2024 Int’l Conf. on
Management of Data. Santiago: ACM, 2024. 460–463. [doi: 10.1145/3626246.3654728]
[29] Gjurovski D, Davitkova A, Michel S. Grid-AR: A grid-based booster for learned cardinality estimation and range joins. arXiv:2410.
07895, 2024.
[30] Li YZ, Liu XL, Wang HZ, Zhang KX, Wang ZX. Updateable data-driven cardinality estimator with bounded Q-error. arXiv:2408.
17209, 2024.
[31] Liu QY, Shen YY, Chen L. LHist: Towards learning multi-dimensional histogram for massive spatial data. In: Proc. of the 37th IEEE Int’l
Conf. on Data Engineering. Chania: IEEE, 2021. 1188–1199. [doi: 10.1109/ICDE51399.2021.00107]
[32] Halford M, Saint-Pierre P, Morvan F. An approach based on Bayesian networks for query selectivity estimation. In: Proc. of the 24th Int’l
Conf. on Database Systems for Advanced Applications. Chiang Mai: Springer, 2019. 3–19. [doi: 10.1007/978-3-030-18579-4_1]
[33] Kipf A, Freitag M, Vorona D, Boncz P, Neumann T, Kemper A. Estimating filtered group-by queries is hard: Deep learning to the
rescue. In: Proc. of the 1st Int’l Workshop on Applied AI for Database Systems and Applications. Los Angeles, 2019.
[34] Akdere M, Çetintemel U, Riondato M, Upfal E, Zdonik SB. Learning-based query performance modeling and prediction. In: Proc. of the
28th IEEE Int’l Conf. on Data Engineering. Arlington: IEEE, 2012. 390–401. [doi: 10.1109/ICDE.2012.64]
[35] Dutt A, Wang C, Nazi A, Kandula S, Narasayya V, Chaudhuri S. Selectivity estimation for range predicates using lightweight models.
Proc. of the VLDB Endowment, 2019, 12(9): 1044–1057. [doi: 10.14778/3329772.3329780]
[36] 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]
[37] 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]
[38] 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]
[39] 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]
[40] Sun LM, Ji T, Li CP, Chen H. DeepO: A learned query optimizer. In: Proc. of the 2022 Int’l Conf. on Management of Data.
Philadelphia: ACM, 2022. 2421–2424. [doi: 10.1145/3514221.3520167]
[41] Kang JKZ, Gaurav, Tan SY, Cheng F, Sun SX, He BS. Efficient deep learning pipelines for accurate cost estimations over large scale
query workload. In: Proc. of the 2021 Int’l Conf. on Management of Data. ACM, 2021. 1014–1022. [doi: 10.1145/3448016.3457546]
[42] 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]
[43] Wang ZL, Zeng QX, Wang N, Lu HW, Zhang Y. CEDA: Learned cardinality estimation with domain adaptation. Proc. of the VLDB
Endowment, 2023, 16(12): 3934–3937. [doi: 10.14778/3611540.3611589]
[44] Akdere M, Cetintemel U, Riondato M, Upfal E, Zdonik S. The case for predictive database systems: Opportunities and challenges.
CIDR. In: Proc. of the 5th Biennial Conf. on Innovative Data Systems Research. Asilomar, 2011. 167–174.
[45] Ganapathi A, Kuno H, Dayal U, Wiener JL, Fox A, Jordan M, Patterson D. Predicting multiple metrics for queries: Better decisions
enabled by machine learning. In: Proc. of the 25th IEEE Int’l Conf. on Data Engineering. Shanghai: IEEE, 2009. 592–603. [doi: 10.1109/

