Page 363 - 《软件学报》2025年第8期
P. 363
3786 软件学报 2025 年第 36 卷第 8 期
Conf. on Data Engineering (ICDE). Brisbane: IEEE, 2013. 38–49. [doi: 10.1109/ICDE.2013.6544812]
[22] 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]
[23] Li YL, Chen DY, Ding BL, Zeng K, Zhou JR. A pluggable learned index method via sampling and gap insertion. arXiv:2101.00808,
2021.
[24] Guo N, Wang YQ, Jiang HN, Xia XF, Gu Y. TALI: An update-distribution-aware learned index for social media data. Mathematics,
2022, 10(23): 4507. [doi: 10.3390/math10234507]
[25] Galakatos A, Markovitch M, Binnig C, Fonseca R, Kraska T. FITing-tree: A data-aware index structure. In: Proc. of the 2019 Int’l Conf.
on Management of Data. Amsterdam: ACM, 2019. 1189–1206. [doi: 10.1145/3299869.3319860]
[26] Bingmann T. STX B+ Tree C++ Template Classes. 2013. https://panthema.net/2007/stx-btree/
[27] Li X, Li JD, Wang XL. ASLM: Adaptive single layer model for learned index. In: Proc. of the 24th Int’l Conf. on Database System for
Advanced Applications (DASFAA 2019). Chiang Mai: Springer, 2019. 80–95. [doi: 10.1007/978-3-030-18590-9_6]
[28] Gao YN, Ye JB, Yang NZ, Gao XF, Chen GH. Middle layer based scalable learned index scheme. Ruan Jian Xue Bao/Journal of
Software, 2020, 31(3): 620–633 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5910.htm [doi: 10.13328/j.cnki.jos.
005910]
[29] Mishra M, Singhal R. RUSLI: Real-time updatable spline learned index. In: Proc. of the 4th Workshop on Exploiting Artificial
Intelligence Techniques for Data Management. New York: ACM, 2021. 1–8. [doi: 10.1145/3464509.3464886]
[30] Sheng YF, Cao X, Fang YX, Zhao KQ, Qi JZ, Cong G, Zhang WJ. WISK: A workload-aware learned index for spatial keyword queries.
Proc. of the ACM on Management of Data, 2023, 1(2): 187. [doi: 10.1145/3589332]
[31] Huang S, Wang Y, Li GL. ACR-tree: Constructing R-trees using deep reinforcement learning. In: Proc. of the 28th Int’l Conf. on
Database Systems for Advanced Applications. Tianjin: Springer, 2023. 80–96. [doi: 10.1007/978-3-031-30637-2_6]
附中文参考文献:
[1] 蔡盼, 张少敏, 刘沛然, 孙路明, 李翠平, 陈红. 智能数据库学习型索引研究综述. 计算机学报, 2023, 46(1): 51–69. [doi: 10.11897/
SP.J.1016.2023.00051]
[28] 高远宁, 叶金标, 杨念祖, 高晓沨, 陈贵海. 基于中间层的可扩展学习索引技术. 软件学报, 2020, 31(3): 620–633. http://www.jos.org.
cn/1000-9825/5910.htm [doi: 10.13328/j.cnki.jos.005910]
郭娜(1985-), 女, 博士生, 主要研究领域为空间 谷峪(1981-), 男, 博士, 教授, 博士生导师, CCF
数据管理, 学习型索引. 杰出会员, 主要研究领域为图数据管理, 空间数
据管理, 大数据分析.
王雅琪(1999-), 女, 硕士生, CCF 学生会员, 主 夏秀峰(1964-), 男, 博士, 教授, 博士生导师,
要研究领域为空间数据管理, 学习型索引. CCF 高级会员, 主要研究领域为算法设计和分
析, 数据库, 数据质量, 分布式系统.
姜皓南(1998-), 男, 硕士生, 主要研究领域为空
间数据管理, 学习型索引.

