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                 [30]   Dong HW, Zhang C, Li GL, Feng JH. Survey on cloud-native databases. Ruan Jian Xue Bao/Journal of Software, 2024, 35(2): 899–926
                     (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6952.htm [doi: 10.13328/j.cnki.jos.006952]
                 [31]   Subramanya SJ, Devvrit F, Simhadri HV, Krishnawamy R, Karchmer R. DiskANN: Fast accurate billion-point nearest neighbor search on
                     a single node. In: Proc. of the 33rd Int'l Conf. on Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2019.
                     13766–13776.
                 [32]   Tang B, Qin JB, Mao R. Vector databases: Key technologies, system architecture, and future challenges. Communications of the China
                     Computer Federation, 2023, 19(11): 36–41 (in Chinese with English abstract).
                 [33]   Babenko A, Lempitsky V. Additive quantization for extreme vector compression. In: Proc. of the 2014 IEEE Conf. on Computer Vision
                     and Pattern Recognition. Columbus: IEEE, 2014. 931–938. [doi: 10.1109/CVPR.2014.124]
                 [34]   Zeghidour N, Luebs A, Omran A, Skoglund J, Tagliasacchi M. SoundStream: An end-to-end neural audio codec. IEEE/ACM Trans. on
                     Audio, Speech, and Language Processing, 2022, 30: 495–507. [doi: 10.1109/TASLP.2021.3129994]
                 [35]   Lee D, Kim C, Kim S, Cho M, Han WS. Autoregressive image generation using residual quantization. In: Proc. of the 2022 IEEE/CVF
                     Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 11513–11522. [doi: 10.1109/CVPR52688.2022.01123]
                 [36]   Vali MH, Bäckström T. Stochastic optimization of vector quantization methods in application to speech and image processing. In: Proc.
                     of the 2023 IEEE Int’l Conf. on Acoustics, Speech and Signal Processing (ICASSP 2023). Rhodes Island: IEEE, 2023. 1–5. [doi: 10.1109/
                     ICASSP49357.2023.10096204]
                 [37]   Chen YJ, Guan T, Wang C. Approximate nearest neighbor search by residual vector quantization. Sensors, 2010, 10(12): 11259–11273.
                     [doi: 10.3390/s101211259]
                 [38]   Zheng BL, Yue ZY, Hu Q, Yi XM, Luan XF, Xie C, Zhou XF, Jensen CS. Learned probing cardinality estimation for high-dimensional
                     approximate NN search. In: Proc. of the 39th Int’l Conf. on Data Engineering (ICDE). Anaheim: IEEE, 2023. 3209–3221.
                 [39]   Zhou YK, Yan MY, Yao JJ, Xu G. ProRAG: Towards reliable and proficient AIGC-based digital avatar. In: Proc. of the 30th Int’l Conf.
                     on Database Systems for Advanced Applications (DASFAA 2025). 2025. 408–419.
                 [40]   Tang  X,  Wu  S,  Hou  J,  Chen  G.  Efficient  sample  retrieval  techniques  for  multimodal  model  training.  Ruan  Jian  Xue  Bao/Journal  of
                     Software, 2024, 35(3): 1125–1139 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/7073.htm [doi: 10.13328/j.cnki.
                     jos.007073]

                 附中文参考文献
                 [30]   董昊文, 张超, 李国良, 冯建华. 云原生数据库综述. 软件学报, 2024, 35(2): 899–926. http://www.jos.org.cn/1000-9825/6952.htm [doi:
                     10.13328/j.cnki.jos.006952]
                 [32]   唐博, 秦建斌, 毛睿. 向量数据库: 关键技术、系统架构与未来挑战. 中国计算机学会通讯, 2023, 19(11): 36–41.
                 [40]   唐秀, 伍赛, 侯捷, 陈刚. 面向多模态模型训练的高效样本检索技术. 软件学报, 2024, 35(3): 1125–1139. http://www.jos.org.cn/1000-
                     9825/7073.htm [doi: 10.13328/j.cnki.jos.007073]

                 作者简介
                 江宇轩, 硕士生, 主要研究领域为数据库索引优化.
                 姚俊杰, 博士, 副教授, CCF  专业会员, 主要研究领域为数据库系统, 知识工程.
                 侯宇轩, 硕士生, 主要研究领域为数据库索引优化.
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