Page 28 - 《软件学报》2026年第2期

刘佳伟 等: LA-tree: 查询感知的自适应学习型多维索引 507

[16] Graefe G. B-tree indexes, interpolation search, and skew. In: Proc. of the 2nd Int’l Workshop on Data Management on New Hardware.
Chicago: ACM, 2006. 5. [doi: 10.1145/1140402.1140409]
[17] Chierichetti F, Kumar R. LSH-preserving functions and their applications. Journal of the ACM, 2015, 62(5): 33. [doi: 10.1145/2816813]
[18] Fox EA, Chen QF, Daoud AM, Heath LS. Order-preserving minimal perfect hash functions and information retrieval. ACM Trans. on
Information Systems, 1991, 9(3): 281–308. [doi: 10.1145/125187.125200]
[19] 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]
[20] 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]
[21] Kipf A, Marcus R, van Renen A, Stoian M, Kemper A, Kraska T, Neumann T. RadixSpline: A single-pass learned index. In: Proc. of the
3rd Int’l Workshop on Exploiting Artificial Intelligence Techniques for Data Management. Portland: ACM, 2020. 5. [doi: 10.1145/
3401071.3401659]
[22] 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]
[23] Lamb A, Fuller M, Varadarajan R, Tran N, Vandiver B, Doshi L, Bear C. The vertica analytic database: C-store 7 years later. Proc. of the
VLDB Endowment, 2012, 5(12): 1790–1801. [doi: 10.14778/2367502.2367518]
[24] Samet H. The quadtree and related hierarchical data structures. ACM Computing Surveys, 1984, 16(2): 187–260. [doi: 10.1145/356924.
356930]
[25] Gaede V, Günther O. Multidimensional access methods. ACM Computing Surveys, 1998, 30(2): 170–231. [doi: 10.1145/280277.280279]
[26] Guttman A. R-trees: A dynamic index structure for spatial searching. In: Proc. of the 1984 ACM SIGMOD Int’l Conf. on Management of
Data. Boston: ACM, 1984. 47–57. [doi: 10.1145/602259.602266]
[27] IBM. The spatial index. 2021. https://www.ibm.com/docs/en/informix-servers/12.10.0?topic=data-spatial-index
[28] Wang HX, Fu XY, Xu JL, Lu H. Learned index for spatial queries. In: Proc. of the 20th IEEE Int’l Conf. on Mobile Data Management.
Hong Kong: IEEE, 2019. 569–574. [doi: 10.1109/MDM.2019.00121]
[29] Qi JZ, Liu GL, Jensen CS, Kulik L. Effectively learning spatial indices. Proc. of the VLDB Endowment, 2020, 13(12): 2341–2354. [doi:
10.14778/3407790.3407829]
[30] Wang XL, Chen HH. ZFT INDEX: Learning multi-dimensional index based on piecewise linear regression. Computer Applications and
Software, 2024, 41(10): 24–31 (in Chinese with English abstract). [doi: 10.3969/j.issn.1000-386x.2024.10.005]
[31] Idreos S, Kersten ML, Manegold S. Database cracking. In: Proc. of the 3rd Biennial Conf. on Innovative Data Systems Research.
Asilomar, 2007. 68–78.
[32] Davitkova A, Milchevski E, Michel S. The ML-index: A multidimensional, learned index for point, range, and nearest-neighbor queries.
In: Proc. of the 23rd Int’l Conf. on Extending Database Technology. Copenhagen: OpenProceedings, 2020. 407–410. [doi: 10.5441/002/
EDBT.2020.44]
[33] Gao J, Cao X, Yao X, Zhang G, Wang W. LMSFC: A novel multidimensional index based on learned monotonic space filling curves.
Proc. of the VLDB Endowment, 2023, 16(10): 2605–2617. [doi: 10.14778/3603581.3603598]
[34] Pai S, Mathioudakis M, Wang YH. WaZI: A learned and workload-aware Z-index. In: Proc. of the 27th Int’l Conf. on Extending
Database Technology. Paestum: OpenProceedings, 2024. 559–571. [doi: 10.48786/EDBT.2024.48]
[35] Li PF, Lu H, Zheng Q, Yang L, Pan G. LISA: A learned index structure for spatial data. In: Proc. of the 2020 ACM SIGMOD Int’l Conf.
on Management of Data. Portland: ACM, 2020. 2119–2133. [doi: 10.1145/3318464.3389703]

附中文参考文献
[30] 王小丽, 陈华辉. ZFT 索引: 基于分段线性回归的学习型多维索引. 计算机应用与软件, 2024, 41(10): 24–31. [doi: 10.3969/j.issn.1000-
386x.2024.10.005]

作者简介
刘佳伟, 博士生, 主要研究领域为智能数据库技术, 机器学习.
范举, 博士, 教授, 博士生导师, CCF 专业会员, 主要研究领域为智能数据库技术, 人在回路的数据准备, 大数据管理与分析.
张超, 博士, 副教授, CCF 高级会员, 主要研究领域为云原生 HTAP 数据库系统, 智能数据库技术.
杜小勇, 博士, 教授, 博士生导师, CCF 会士, 主要研究领域为数据管理技术, 语义网技术, 智能信息检索技术.