Page 375 - 《软件学报》2026年第2期

854 软件学报 2026 年第 37 卷第 2 期

[142] Saxena G, Rahman M, Chainani N, Lin CB, Caragea G, Chowdhury F, Marcus R, Kraska T, Pandis I, Narayanaswamy B. Auto-WLM:
Machine learning enhanced workload management in Amazon Redshift. In: Proc. of Companion of the 2023 Int’l Conf. on Management
of Data. Seattle: ACM, 2023. 225–237. [doi: 10.1145/3555041.3589677]
[143] Zhang LX, Chai CL, Zhou XH, Li GL. LearnedSQLGen: Constraint-aware SQL generation using reinforcement learning. In: Proc. of
the 2022 Int’l Conf. on Management of Data. Philadelphia: ACM, 2022. 945–958. [doi: 10.1145/3514221.3526155]
[144] Sun Z, Cheung A. Query aware synthetic data generation. Technical Report, UCB/EECS-2023-124, Berkeley: University of California,
2023.
[145] Weng SY, Wang QS, Qu LY, Zhang R, Cai P, Qian WN, Zhou AY. Lauca: A workload duplicator for benchmarking transactional
database performance. IEEE Trans. on Knowledge and Data Engineering, 2024, 36(7): 3180–3194. [doi: 10.1109/TKDE.2024.3360116]
[146] Qu LY, Li YM, Zhang R, Chen T, Shu K, Qian WN, Zhou AY. Application-oriented workload generation for transactional database
performance evaluation. In: Proc. of the 38th IEEE Int’l Conf. on Data Engineering. Kuala Lumpur: IEEE, 2022. 420–432. [doi: 10.1109/
ICDE53745.2022.00036]
[147] Lerner A, Jasny M, Jepsen T, Binnig C, Cudré-Mauroux P. DBMS annihilator: A high-performance database workload generator in
action. Proc. of the VLDB Endowment, 2022, 15(12): 3682–3685. [doi: 10.14778/3554821.3554874]
[148] Holze M, Gaidies C, Ritter N. Consistent on-line classification of DBs workload events. In: Proc. of the 18th ACM Conf. on Information
and Knowledge Management. Hong Kong: ACM, 2009. 1641–1644. [doi: 10.1145/1645953.1646193]
[149] Holze M, Ritter N. Towards workload shift detection and prediction for autonomic databases. In: Proc of the 1st ACM Ph.D. Workshop
in CIKM. Lisbon: ACM, 2007. 109–116. [doi: 10.1145/1316874.1316892]
[150] Wu PZ, Ives ZG. Modeling shifting workloads for learned database systems. Proc. of the ACM on Management of Data, 2024, 2(1): 38.
[doi: 10.1145/3639293]
[151] Gao YN, Huang XQ, Zhou XH, Gao XF, Li GL, Chen GH. DBAugur: An adversarial-based trend forecasting system for diversified
workloads. In: Proc. of the 39th IEEE Int’l Conf. on Data Engineering. Anaheim: IEEE, 2023. 27–39. [doi: 10.1109/ICDE55515.2023.
00385]
[152] Wang JQ, Li TY, Wang AN, Liu XZ, Chen L, Chen J, Liu JY, Wu JY, Li FF, Gao YJ. Real-time workload pattern analysis for large-
scale cloud databases. Proc. of the VLDB Endowment, 2023, 16(12): 3689–3701. [doi: 10.14778/3611540.3611557]
[153] Zhao XY, Zhou XH, Li GL. Automatic database knob tuning: A survey. IEEE Trans. on Knowledge and Data Engineering, 2023,
35(12): 12470–12490. [doi: 10.1109/TKDE.2023.3266893]
[154] Wang JH, Liu LQ, Li CX. A survey of database knob tuning with machine learning. In: Proc. of the 2nd IEEE Int’l Conf. on Electrical
Engineering, Big Data and Algorithms. Changchun: IEEE, 2023. 1545–1550. [doi: 10.1109/EEBDA56825.2023.10090597]
[155] Li YY, Tian JK, Pu Z, Li CP, Chen H. Research survey on intelligent tuning of database knobs configuration. Chinese Journal of
Computers, 2024, 47(8): 1901–1921 (in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2024.01901]
[156] Wu Y, Zhou XH, Zhang Y, Li GL. Automatic index tuning: A survey. IEEE Trans. on Knowledge and Data Engineering, 2024, 36(12):
7657–7676. [doi: 10.1109/TKDE.2024.3422006]
[157] Zhou W, Lin C, Zhou XH, Li GL. Breaking it down: An in-depth study of index advisors. Proc. of the VLDB Endowment, 2024, 17(10):
2405–2418. [doi: 10.14778/3675034.3675035]
[158] Comer D. Ubiquitous B-tree. ACM Computing Surveys (CSUR), 1979, 11(2): 121–137. [doi: 10.1145/356770.356776]
[159] Kraska T, Beutel A, Chi EH, Dean J, Polyzotis N. The case for learned index structures. In: Proc. of the 2018 Int’l Conf. on
Management of Data. Houston: ACM, 2018. 489–504. [doi: 10.1145/3183713.3196909]
[160] Tang CZ, Wang YY, Dong ZY, Hu GS, Wang ZG, Wang MJ, Chen HB. XIndex: A scalable learned index for multicore data storage.
In: Proc. of the 25th ACM SIGPLAN Symp. on Principles and Practice of Parallel Programming. San Diego: ACM, 2020. 308–320.
[doi: 10.1145/3332466.3374547]
[161] Li PF, Hua Y, Jia JN, Zuo PF. FINEdex: A fine-grained learned index scheme for scalable and concurrent memory systems. Proc. of the
VLDB Endowment, 2021, 15(2): 321–334. [doi: 10.14778/3489496.3489512]
[162] Wang YY, Tang CZ, Wang ZG, Chen HB. SIndex: A scalable learned index for string keys. In: Proc. of the 11th ACM SIGOPS Asia-
Pacific Workshop on Systems. Tsukuba: ACM, 2020. 17–24. [doi: 10.1145/3409963.3410496]
[163] Ding JL, Minhas UF, Yu J, Wang C, Do J, Li YN, Zhang HT, Chandramouli B, Gehrke J, Kossmann D, Lomet D, Kraska T. ALEX: An
updatable adaptive learned index. In: Proc. of the 2020 ACM SIGMOD Int’l Conf. on Management of Data. Portland: ACM, 2020.
969–984. [doi: 10.1145/3318464.3389711]
[164] Hadian A, Heinis T. MADEX: Learning-augmented algorithmic index structures. In: Proc. of the 2nd Int’l Workshop on Applied AI for
Database Systems and Applications. Tokyo, 2020.