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Management of Data. Seattle: ACM, 1998. 367–378. [doi: 10.1145/276304.276337]
[99] Chaudhuri S, Datar M, Narasayya V. Index selection for databases: A hardness study and a principled heuristic solution. IEEE Trans. on
Knowledge and Data Engineering, 2004, 16(11): 1313–1323. [doi: 10.1109/TKDE.2004.75]
[100] Schlosser R, Kossmann J, Boissier M. Efficient scalable multi-attribute index selection using recursive strategies. In: Proc of the 35th
IEEE Int’l Conf. on Data Engineering. Macao: IEEE, 2019. 1238–1249. [doi: 10.1109/ICDE.2019.00113]
[101] Dash D, Polyzotis N, Ailamaki A. CoPhy: A scalable, portable, and interactive index advisor for large workloads. Proc. of the VLDB
Endowment, 2011, 4(6): 362–372. [doi: 10.14778/1978665.1978668]
[102] Siddiqui T, Wu WT, Narasayya V, Chaudhuri S. DISTILL: Low-overhead data-driven techniques for filtering and costing indexes for
scalable index tuning. Proc. of the VLDB Endowment, 2022, 15(10): 2019–2031. [doi: 10.14778/3547305.3547309]
[103] Siddiqui T, Jo S, Wu WT, Wang C, Narasayya V, Chaudhuri S. ISUM: Efficiently compressing large and complex workloads for
scalable index tuning. In: Proc. of the 2022 Int’l Conf. on Management of Data. Philadelphia: ACM, 2022. 660–673. [doi: 10.1145/
3514221.3526152]
[104] Wu WT, Wang C, Siddiqui T, Wang JX, Narasayya V, Chaudhuri S, Bernstein PA. Budget-aware index tuning with reinforcement
learning. In: Proc. of the 2022 Int’l Conf. on Management of Data. Philadelphia: ACM, 2022. 1528–1541. [doi: 10.1145/3514221.
3526128]
[105] Sharma A, Schuhknecht FM, Dittrich J. The case for automatic database administration using deep reinforcement learning.
arXiv:1801.05643, 2018.
[106] Lan H, Bao ZF, Peng YW. An index advisor using deep reinforcement learning. In: Proc. of the 29th ACM Int’l Conf. on Information &
Knowledge Management. ACM, 2020. 2105–2108. [doi: 10.1145/3340531.3412106]
[107] Schnaitter K, Abiteboul S, Milo T, Polyzotis N. On-line index selection for shifting workloads. In: Proc. of the 23rd IEEE Int’l Conf. on
Data Engineering Workshop. Istanbul: IEEE, 2007. 459–468. [doi: 10.1109/ICDEW.2007.4401029]
[108] Ma L, van Aken D, Hefny A, Mezerhane G, Pavlo A, Gordon GJ. Query-based workload forecasting for self-driving database
management systems. In: Proc. of the 2018 Int’l Conf. on Management of Data. Houston: ACM, 2018. 631–645. [doi: 10.1145/3183713.
3196908]
[109] Bruno N, Chaudhuri S. To tune or not to tune?: A lightweight physical design alerter. In: Proc. of the 32nd Int’l Conf. on Very Large
Data Bases. Seoul: VLDB Endowment, 2006. 499–510.
[110] Bruno N, Chaudhuri S. An online approach to physical design tuning. In: Proc. of the 23rd IEEE Int’l Conf. on Data Engineering.
Istanbul: IEEE, 2007. 826–835. [doi: 10.1109/ICDE.2007.367928]
[111] Schnaitter K, Polyzotis N. Semi-automatic index tuning: Keeping DBAs in the loop. Proc. of the VLDB Endowment, 2012, 5(5):
478–489. [doi: 10.14778/2140436.2140444]
[112] Yadav R, Valluri SR, Zaït M. AIM: A practical approach to automated index management for SQL databases. In: Proc. of the 39th IEEE
Int’l Conf. on Data Engineering. Anaheim: IEEE, 2023. 3349–3362. [doi: 10.1109/ICDE55515.2023.00257]
[113] Shi JC, Cong G, Li XL. Learned index benefits: Machine learning based index performance estimation. Proc. of the VLDB Endowment,
2022, 15(13): 3950–3962. [doi: 10.14778/3565838.3565848]
[114] Perera RM, Oetomo B, Rubinstein BIP, Borovica-Gajic R. DBA Bandits: Self-driving index tuning under ad-hoc, analytical workloads
with safety guarantees. In: Proc. of the 37th IEEE Int’l Conf. on Data Engineering. Chania: IEEE, 2021. 600–611. [doi: 10.1109/
ICDE51399.2021.00058]
[115] Kossmann J, Kastius A, Schlosser R. SWIRL: Selection of workload-aware indexes using reinforcement learning. In: Proc. of the 25th
Int’l Conf. on Extending Database Technology. Edinburgh: OpenProceedings.org, 2022. 2:155–2:168.
[116] Sadri Z, Gruenwald L, Lead E. DRLindex: Deep reinforcement learning index advisor for a cluster database. In: Proc. of the 24th Symp.
on Int’l Database Engineering & Applications. Seoul: ACM, 2020. 11. [doi: 10.1145/3410566.3410603]
[117] Perera RM, Oetomo B, Rubinstein BIP, Borovica-Gajic R. HMAB: Self-driving hierarchy of bandits for integrated physical database
design tuning. Proc. of the VLDB Endowment, 2022, 16(2): 216–229. [doi: 10.14778/3565816.3565824]
[118] Zhou XH, Liu LY, Li WB, Jin LY, Li SF, Wang TQ, Feng JH. AutoIndex: An incremental index management system for dynamic
workloads. In: Proc. of the 38th IEEE Int’l Conf. on Data Engineering. Kuala Lumpur: IEEE, 2022. 2196–2208. [DOI: 10.1109/
ICDE53745.2022.00210]
[119] Chaudhuri S, Narasayya V. Anytime algorithm of database tuning advisor for Microsoft SQL server. 2020. https://www.microsoft.com/
en-us/research/wp-content/uploads/2020/06/Anytime-Algorithm-of-Database-Tuning-Advisor-for-Microsoft-SQL-Server.pdf
[120] Valentin G, Zuliani M, Zilio DC, Lohman G, Skelley A. DB2 Advisor: An optimizer smart enough to recommend its own indexes. In:
Proc. of the 16th Int’l Conf. on Data Engineering. San Diego: IEEE, 2000. 101–110. [doi: 10.1109/ICDE.2000.839397]