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何家豪 等: 智能查询优化算法研究综述 295
检测异常优化决策, 确保智能优化算法在有安全约束下的可靠性.
References
[1] Nambiar RO, Poess M. The making of TPC-DS. In: Proc. of the 32nd Int’l Conf. on Very Large Data Bases. Seoul: VLDB Endowment,
2006. 1049–1058.
[2] Horng JT, Kao CY, Liu BJ. A genetic algorithm for database query optimization. In: Proc. of the 1st IEEE Conf. on Evolutionary
Computation. Orlando: IEEE, 1994. 350–355. [doi: 10.1109/ICEC.1994.349926]
[3] Leis V, Gubichev A, Mirchev A, Boncz P, Kemper A, Neumann T. How good are query optimizers, really? Proc. of the VLDB
Endowment, 2015, 9(3): 204–215. [doi: 10.14778/2850583.2850594]
[4] Chen X, Chen HT, Liang ZB, Liu SC, Wang JH, Zeng K, Su H, Zheng K. Leon: A new framework for ML-aided query optimization.
Proc. of the VLDB Endowment, 2023, 16(9): 2261–2273. [doi: 10.14778/3598581.3598597]
[5] Negi P, Interlandi M, Marcus R, Alizadeh M, Kraska T, Friedman M, Jindal A. Steering query optimizers: A practical take on big data
workloads. In: Proc. of the 2021 Int’l Conf. on Management of Data. ACM, 2021. 2557–2569. [doi: 10.1145/3448016.3457568]
[6] Ahmed R, Bello R, Witkowski A, Kumar P. Automated generation of materialized views in Oracle. Proc. of the VLDB Endowment,
2020, 13(12): 3046–3058. [doi: 10.14778/3415478.3415533]
[7] Sun LM, Zhang SM, Ji T, Li CP, Chen H. Survey of data management techniques powered by artificial intelligence. Ruan Jian Xue
Bao/Journal of Software, 2020, 31(3): 600–619 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5909.htm [doi: 10.
13328/j.cnki.jos.005909]
[8] Li GL, Zhou XH, Cao L. AI meets database: AI4DB and DB4AI. In: Proc. of the 2021 Int’l Conf. on Management of Data. ACM, 2021.
2859–2866. [doi: 10.1145/3448016.3457542]
[9] Chai MK, Fan J, Du XY. Learnable database systems: Challenges and opportunities. Ruan Jian Xue Bao/Journal of Software, 2020,
31(3): 806–830 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5908.htm [doi: 10.13328/j.cnki.jos.005908]
[10] Meng XF, Ma CH, Yang C. Survey on machine learning for database systems. Journal of Computer Research and Development, 2019,
56(9): 1803–1820 (in Chinese with English abstract). [doi: 10.7544/issn1000-1239.2019.20190446]
[11] Li GL, Zhou XH, Sun J, Yu X, Yuan HT, Liu JB, Han Y. A survey of machine learning based database techniques. Chinese Journal of
Computers, 2020, 43(11): 2019–2049 (in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2020.02019]
[12] Song YM, Gu Y, Li FF, Yu G. Survey on AI powered new techniques for query processing and optimization. Journal of Frontiers of
Computer Science and Technology, 2020, 14(7): 1081–1103 (in Chinese with English abstract). [doi: 10.3778/j.issn.1673-9418.
1911063]
[13] Lan H, Bao ZF, Peng YW. A survey on advancing the DBMS query optimizer: Cardinality estimation, cost model, and plan
enumeration. Data Science and Engineering, 2021, 6(1): 86–101. [doi: 10.1007/s41019-020-00149-7]
[14] Zhao Y, Li ZDH, Cong G. A comparative study and component analysis of query plan representation techniques in ML4DB studies.
Proc. of the VLDB Endowment, 2023, 17(4): 823–835. [doi: 10.14778/3636218.3636235]
[15] Wang H, Sevcik KC. A multi-dimensional histogram for selectivity estimation and fast approximate query answering. In: Proc. of the
2003 Conf. of the Centre for Advanced Studies on Collaborative Research. Toronto: IBM Press, 2003. 328–342.
[16] Gunopulos D, Kollios G, Tsotras VJ, Domeniconi C. Selectivity estimators for multidimensional range queries over real attributes. The
VLDB Journal, 2005, 14(2): 137–154. [doi: 10.1007/s00778-003-0090-4]
[17] Zhao ZY, Christensen R, Li FF, Hu X, Yi K. Random sampling over joins revisited. In: Proc. of the 2018 Int’l Conf. on Management of
Data. Houston: ACM, 2018. 1525–1539. [doi: 10.1145/3183713.3183739]
[18] Nash C, Durkan C. Autoregressive energy machines. In: Proc. of the 36th Int’l Conf. on Machine Learning. Long Beach: PMLR, 2019.
1735–1744.
[19] Hilprecht B, Schmidt A, Kulessa M, Molina A, Kersting K, Binnig C. DeepDB: Learn from data, not from queries! Proc. of the VLDB
Endowment, 2020, 13(7): 992–1005. [doi: 10.14778/3384345.3384349]
[20] Heimel M, Kiefer M, Markl V. Self-tuning, GPU-accelerated kernel density models for multidimensional selectivity estimation. In:
Proc. of the 2015 Int’l Conf. on Management of Data. Melbourne: ACM, 2015. 1477–1492. [doi: 10.1145/2723372.2749438]
[21] Sun LM, Li CP, Ji T, Chen H. MOSE: A monotonic selectivity estimator using learned CDF. IEEE Trans. on Knowledge and Data
Engineering, 2023, 35(3): 2823–2836. [doi: 10.1109/TKDE.2021.3112753]
[22] Park Y, Zhong SC, Mozafari B. QuickSel: Quick selectivity learning with mixture models. In: Proc. of the 2020 ACM SIGMOD Int’l

