Page 119 - 《软件学报》2026年第3期

1082 软件学报 2026 年第 37 卷第 3 期

index framework for high-dimensional vector similarity search on data segment. Proc. of the ACM on Management of Data, 2024, 2(1):
14. [doi: 10.1145/3639269]
[27] Pan JJ, Wang JG, Li GL. Survey of vector database management systems. The VLDB Journal, 2024, 33(1): 1591–1615.
[28] Aumüller M, Bernhardsson E, Faithfull A. ANN-benchmarks: A benchmarking tool for approximate nearest neighbor algorithms.
Information Systems, 2020, 87: 101374. [doi: 10.1016/j.is.2019.02.006]
[29] Li W, Zhang Y, Sun YF, Wang W, Li MJ, Zhang WJ, Lin XM. Approximate nearest neighbor search on high dimensional
data—Experiments, analyses, and improvement. IEEE Trans. on Knowledge and Data Engineering, 2020, 32(8): 1475–1488. [doi: 10.
1109/TKDE.2019.2909204]
[30] Sun YS, Zeng JH. Research on vector database and its application. Scientific Information Research, 2024, 6(4): 11–24 (in Chinese with
English abstract). [doi: 10.19809/j.cnki.kjqbyj.2024.04.002]
[31] Lu KJ, Kudo M, Xiao C, Ishikawa Y. HVS: Hierarchical graph structure based on voronoi diagrams for solving approximate nearest
neighbor search. Proc. of the VLDB Endowment, 2021, 15(2): 246–258. [doi: 10.14778/3489496.3489506]
[32] Fu C, Xiang C, Wang CX, Cai D. Fast approximate nearest neighbor search with the navigating spreading-out graph. Proc. of the VLDB
Endowment, 2019, 12(5): 461–474. [doi: 10.14778/3303753.3303754]
[33] Zhao X, Tian Y, Huang K, Zheng BL, Zhou XF. Towards efficient index construction and approximate nearest neighbor search in high-
dimensional spaces. Proc. of the VLDB Endowment, 2023, 16(8): 1979–1991. [doi: 10.14778/3594512.3594527]
[34] Fu C, Cai D. EFANNA: An extremely fast approximate nearest neighbor search algorithm based on KNN graph. arXiv:1609.07228, 2016.
[35] Dasgupta S, Sinha K. Randomized partition trees for exact nearest neighbor search. In: Proc. of the 26th Annual Conf. on Learning
Theory. 2013. 317–337.
[36] Zheng BL, Xi Z, Weng LG, Hung NQV, Liu H, Jensen CS. PM-LSH: A fast and accurate LSH framework for high-dimensional
approximate NN search. Proc. of the VLDB Endowment, 2020, 13(5): 643–655. [doi: 10.14778/3377369.3377374]
[37] Meng JF, Wang HY, Xu J, Ogihara M. ONe index for all kernels (ONIAK): A zero re-indexing LSH solution to ANNS-ALT (after linear
transformation). Proc. of the VLDB Endowment, 2022, 15(13): 3937–3949. [doi: 10.14778/3565838.3565847]
[38] Ge TZ, He KM, Ke QF, Sun J. Optimized product quantization for approximate nearest neighbor search. In: Proc. of the 2013 IEEE Conf.
on Computer Vision and Pattern Recognition. Portland: IEEE, 2013. 2946–2953. [doi: 10.1109/CVPR.2013.379]
[39] Yandex AB, Lempitsky V. Efficient indexing of billion-scale datasets of deep descriptors. In: Proc. of the 2016 IEEE Conf. on Computer
Vision and Pattern Recognition. Las Vegas: IEEE, 2016. 2055–2063. [doi: 10.1109/CVPR.2016.226]
[40] Srinivasan K, Raman K, Chen JC, Bendersky M, Najork M. WIT: Wikipedia-based image text dataset for multimodal multilingual
machine learning. In: Proc. of the 44th Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM, 2021.
2443–2449. [doi: 10.1145/3404835.3463257]
[41] Microsoft spacev-1b. 2021. https://github.com/microsoft/SPTAG/tree/main/datasets/SPACEV1B
[42] Pan Y, Sun JX, Yu HF. LM-DiskANN: Low memory footprint in disk-native dynamic graph-based ANN indexing. In: Proc. of the 2023
IEEE Int’l Conf. on Big Data (BigData). Sorrento: IEEE, 2023. 5987–5996. [doi: 10.1109/BigData59044.2023.10386517]
[43] Ni JK, Xu XL, Wang YX, Li C, Yao JJ, Xiao SH, Zhang XC. DiskANN++: Efficient page-based search over isomorphic mapped graph
index using query-sensitivity entry vertex. arXiv:2310.00402, 2023.
[44] YouTube. 2025. https://blog.youtube/press/
[45] Li J, Liu HF, Gui CH, Chen JY, Ni ZY, Wang N, Chen Y. The design and implementation of a real time visual search system on JD
e-commerce platform. In: Proc. of the 19th Int’l Middleware Conf. Industry. Rennes: ACM, 2018. 9–16. [doi: 10.1145/3284028.3284030]
[46] Xu HK, Manohar MD, Bernstein PA, Chandramouli B, Wen R, Simhadri HV. In-place updates of a graph index for streaming
approximate nearest neighbor search. arXiv:2502.13826, 2025.
[47] Qader MA, Cheng SW, Hristidis V. A comparative study of secondary indexing techniques in LSM-based NoSQL databases. In: Proc. of
the 2018 Int’l Conf. on Management of Data. Houston: ACM, 2018. 551–566. [doi: 10.1145/3183713.3196900]
[48] Wu LK, Lin WQ, Xiao XK, Xu YB. LSII: An indexing structure for exact real-time search on microblogs. In: Proc. of the 29th IEEE Int’l
Conf. on Data Engineering (ICDE). Brisbane: IEEE, 2013. 482–493. [doi: 10.1109/ICDE.2013.6544849]
[49] Alsubaiee S, Behm A, Borkar V, Heilbron Z, Kim YS, Carey MJ, Dreseler M, Li C. Storage management in AsterixDB. Proc. of the
VLDB Endowment, 2014, 7(10): 841–852. [doi: 10.14778/2732951.2732958]
[50] Shin J, Wang JG, Aref WG. The LSM RUM-tree: A log structured merge R-tree for update-intensive spatial workloads. In: Proc. of the
37th IEEE Int’l Conf. on Data Engineering (ICDE). Chania: IEEE, 2021. 2285–2290. [doi: 10.1109/ICDE51399.2021.00238]
[51] Xu R, Liu ZH, Hu HQ, Qian WN, Zhou AY. An efficient secondary index for spatial data based on LevelDB. In: Proc. of the 25th Int’l
Conf. on Database Systems for Advanced Applications. Jeju: Springer, 2020. 750–754. [doi: 10.1007/978-3-030-59419-0_50]