{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T15:42:10Z","timestamp":1783784530138,"version":"3.55.0"},"reference-count":82,"publisher":"Association for Computing Machinery (ACM)","issue":"6","funder":[{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2023ZD0503600\/2023ZD0503604"],"award-info":[{"award-number":["2023ZD0503600\/2023ZD0503604"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2025,12,4]]},"abstract":"<jats:p>Given a hybrid dataset where every data object consists of a vector and an attribute value, for each query with a target vector and a range filter, range-filtering approximate nearest neighbor search (RFANNS) aims to retrieve the most similar vectors from the dataset and the corresponding attribute values fall in the query range. It is a fundamental function in vector database management systems and intelligent systems with embedding abilities. Dedicated indices for RFANNS accelerate query speed with an acceptable accuracy loss on nearest neighbors. However, they are still facing the challenges to be constructed incrementally and generalized to achieve superior query performance for arbitrary range filters. In this paper, we introduce a window graph-based RFANNS index. For incremental construction, we propose an insertion algorithm to add new vector-attribute pairs into hierarchical window graphs with varying window size. To handle arbitrary range filters, we optimize relevant window search for attribute filter checks and vector distance computations by range selectivity. Extensive experiments on real-world datasets show that for index construction, the indexing time is on par with the most building-efficient index, and 4.9x faster than the most query-efficient index with 0.4-0.5x smaller size; For RFANNS query, it is 4x faster than the most efficient incremental index, and matches the performance of the best statically-built index.<\/jats:p>","DOI":"10.1145\/3769843","type":"journal-article","created":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T04:32:13Z","timestamp":1764995533000},"page":"1-27","source":"Crossref","is-referenced-by-count":1,"title":["WoW: A Window-to-Window Incremental Index for Range-Filtering Approximate Nearest Neighbor Search"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2027-3524","authenticated-orcid":false,"given":"Ziqi","family":"Wang","sequence":"first","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7062-9096","authenticated-orcid":false,"given":"Jingzhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3635-6335","authenticated-orcid":false,"given":"Wei","family":"Hu","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3725349"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2024.104662"},{"key":"e_1_2_1_3_1","first-page":"29","article-title":"Estimating Local Intrinsic Dimensionality. In SIGKDD. ACM, Sydney, NSW","author":"Amsaleg Laurent","year":"2015","unstructured":"Laurent Amsaleg, Oussama Chelly, Teddy Furon, St\u00e9phane Girard, Michael E. Houle, Ken-ichi Kawarabayashi, and Michael Nett. 2015. Estimating Local Intrinsic Dimensionality. In SIGKDD. ACM, Sydney, NSW, Australia, 29-38.","journal-title":"Australia"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.14778\/2856318.2856324"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2019.02.006"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2021.101807"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3709693"},{"key":"e_1_2_1_8_1","first-page":"2787","volume-title":"NeurIPS","volume":"26","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-relational Data. In NeurIPS, Vol. 26. Curran Associates, Inc., Lake Tahoe, NV, USA, 2787-2795."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3698822"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/3685800.3685805"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/1577069.1755852"},{"key":"e_1_2_1_12_1","volume-title":"FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search. In WWW","author":"Chen Patrick","year":"2023","unstructured":"Patrick Chen, Wei-Cheng Chang, Jyun-Yu Jiang, Hsiang-Fu Yu, Inderjit Dhillon, and Cho-Jui Hsieh. 2023. FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search. In WWW. ACM, Austin, TX, USA, 3225-3235."},{"key":"e_1_2_1_13_1","volume-title":"SPANN: highly-efficient billion-scale approximate nearest neighbor search","author":"Chen Qi","unstructured":"Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zengzhong Li, Mao Yang, and Jingdong Wang. 2021. SPANN: highly-efficient billion-scale approximate nearest neighbor search. In NeurIPS. Curran Associates Inc., Red Hook, NY, USA, 14 pages."},{"key":"e_1_2_1_14_1","unstructured":"Rongxin Cheng Yifan Peng Xingda Wei Hongrui Xie Rong Chen Sijie Shen and Haibo Chen. 2024. Characterizing the Dilemma of Performance and Index Size in Billion-Scale Vector Search and Breaking It with Second-Tier Memory. arXiv:2405.03267 [cs.DC]"},{"key":"e_1_2_1_15_1","volume-title":"Introduction to Algorithms","author":"Cormen Thomas H.","unstructured":"Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. 2022. Introduction to Algorithms (4th ed.). MIT Press, Cambridge, MA, USA, 481-485.","edition":"4"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/1370949"},{"key":"e_1_2_1_17_1","volume-title":"Abdel Rodriguez, Dirk Alexander Kulawiak, Marcin Antas, and Parker Duckworth.","author":"Dilocker Etienne","year":"2025","unstructured":"Etienne Dilocker, Bob van Luijt, Byron Voorbach, Mohd Shukri Hasan, Abdel Rodriguez, Dirk Alexander Kulawiak, Marcin Antas, and Parker Duckworth. 2025. Weaviate. https:\/\/github.com\/weaviate\/weaviate."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.2478\/cait-2024-0035"},{"key":"e_1_2_1_19_1","first-page":"12469","article-title":"Approximate Nearest Neighbor Search with Window Filters. In ICML. JMLR.org, Vienna","author":"Engels Joshua","year":"2024","unstructured":"Joshua Engels, Benjamin Landrum, Shangdi Yu, Laxman Dhulipala, and Julian Shun. 2024. Approximate Nearest Neighbor Search with Window Filters. In ICML. JMLR.org, Vienna, Austria, 12469-12490.","journal-title":"Austria"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3067706"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.14778\/3303753.3303754"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589282"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3654970"},{"key":"e_1_2_1_24_1","unstructured":"Yunfan Gao Yun Xiong Xinyu Gao Kangxiang Jia Jinliu Pan Yuxi Bi Yi Dai Jiawei Sun Meng Wang and Haofen Wang. 2024. Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv:2312.10997 [cs.CL]"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.240"},{"key":"e_1_2_1_26_1","first-page":"3406","article-title":"Filtered-DiskANN: Graph Algorithms for Approximate Nearest Neighbor Search with Filters. In WWW. ACM, Austin","author":"Gollapudi Siddharth","year":"2023","unstructured":"Siddharth Gollapudi, Neel Karia, Varun Sivashankar, Ravishankar Krishnaswamy, Nikit Begwani, Swapnil Raz, Yiyong Lin, Yin Zhang, Neelam Mahapatro, Premkumar Srinivasan, Amit Singh, and Harsha Vardhan Simhadri. 2023. Filtered-DiskANN: Graph Algorithms for Approximate Nearest Neighbor Search with Filters. In WWW. ACM, Austin, TX, USA, 3406-3416.","journal-title":"TX, USA"},{"key":"e_1_2_1_27_1","unstructured":"Yutong Gou Jianyang Gao Yuexuan Xu and Cheng Long. 2024. SymphonyQG: Towards Symphonious Integration of Quantization and Graph for Approximate Nearest Neighbor Search. arXiv:2411.12229 [cs.DB]"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.14778\/3554821.3554843"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.14778\/3503585.3503586"},{"key":"e_1_2_1_30_1","first-page":"604","article-title":"Approximate nearest neighbors: towards removing the curse of dimensionality. In STOC. ACM, Dallas","author":"Indyk Piotr","year":"1998","unstructured":"Piotr Indyk and Rajeev Motwani. 1998. Approximate nearest neighbors: towards removing the curse of dimensionality. In STOC. ACM, Dallas, TX, USA, 604-613.","journal-title":"TX, USA"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.57"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3725399"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2019.2921572"},{"key":"e_1_2_1_34_1","first-page":"2329","article-title":"Locally Optimized Product Quantization for Approximate Nearest Neighbor Search. In CVPR. IEEE, Columbus","author":"Kalantidis Yannis","year":"2014","unstructured":"Yannis Kalantidis and Yannis Avrithis. 2014. Locally Optimized Product Quantization for Approximate Nearest Neighbor Search. In CVPR. IEEE, Columbus, OH, USA, 2329-2336.","journal-title":"OH, USA"},{"key":"e_1_2_1_35_1","first-page":"2589","article-title":"Locality-Sensitive Hashing Scheme based on Longest Circular Co-Substring. In SIGMOD. ACM, Portland","author":"Lei Yifan","year":"2020","unstructured":"Yifan Lei, Qiang Huang, Mohan Kankanhalli, and Anthony K. H. Tung. 2020. Locality-Sensitive Hashing Scheme based on Longest Circular Co-Substring. In SIGMOD. ACM, Portland, OR, USA, 2589-2599.","journal-title":"OR, USA"},{"key":"e_1_2_1_36_1","first-page":"9","article-title":"The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform. In Middleware. ACM, Rennes","author":"Li Jie","year":"2018","unstructured":"Jie Li, Haifeng Liu, Chuanghua Gui, Jianyu Chen, Zhenyuan Ni, Ning Wang, and Yuan Chen. 2018. The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform. In Middleware. ACM, Rennes, France, 9-16.","journal-title":"France"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2909204"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.14778\/3717755.3717770"},{"key":"e_1_2_1_39_1","volume-title":"ICML. JMLR.org","author":"Lu Kejing","unstructured":"Kejing Lu, Chuan Xiao, and Yoshiharu Ishikawa. 2024. Probabilistic routing for graph-based approximate nearest neighbor search. In ICML. JMLR.org, Vienna, Austria, 19 pages."},{"key":"e_1_2_1_40_1","volume-title":"CHASE: A Native Relational Database for Hybrid Queries on Structured and Unstructured Data. arXiv:2501.05006 [cs.DB]","author":"Ma Rui","year":"2025","unstructured":"Rui Ma, Kai Zhang, Zhenying He, Yinan Jing, X. Sean Wang, and Zhenqiang Chen. 2025a. CHASE: A Native Relational Database for Hybrid Queries on Structured and Unstructured Data. arXiv:2501.05006 [cs.DB]"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.14778\/3717755.3717760"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2013.10.006"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2889473"},{"key":"e_1_2_1_44_1","first-page":"270","article-title":"ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms. In PPoPP. ACM, Edinburgh","author":"Manohar Magdalen Dobson","year":"2024","unstructured":"Magdalen Dobson Manohar, Zheqi Shen, Guy Blelloch, Laxman Dhulipala, Yan Gu, Harsha Vardhan Simhadri, and Yihan Sun. 2024. ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms. In PPoPP. ACM, Edinburgh, UK, 270-285.","journal-title":"UK"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589777"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.eacl-main.148"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2321376"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.14778\/3583140.3583164"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1137\/0202005"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-024-00864-x"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3654923"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588908"},{"key":"e_1_2_1_53_1","first-page":"1532","article-title":"GloVe: Global Vectors for Word Representation. In EMNLP. ACM, Doha","author":"Pennington Jeffrey","year":"2014","unstructured":"Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. GloVe: Global Vectors for Word Representation. In EMNLP. ACM, Doha, Qatar, 1532-1543.","journal-title":"Qatar"},{"key":"e_1_2_1_54_1","volume-title":"Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever.","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning Transferable Visual Models From Natural Language Supervision. arXiv:2103.00020 [cs.CV]"},{"key":"e_1_2_1_55_1","first-page":"71","article-title":"Nearest neighbor queries. In SIGMOD. ACM, San Jose","author":"Roussopoulos Nick","year":"1995","unstructured":"Nick Roussopoulos, Stephen Kelley, and Fr\u00e9d\u00e9ric Vincent. 1995. Nearest neighbor queries. In SIGMOD. ACM, San Jose, CA, USA, 71-79.","journal-title":"CA, USA"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.3724\/2096-7004.di.2025.0001"},{"key":"e_1_2_1_57_1","volume-title":"Ravishankar Krishnaswamy, and Harsha Vardhan Simhadri.","author":"Singh Aditi","year":"2021","unstructured":"Aditi Singh, Suhas Jayaram Subramanya, Ravishankar Krishnaswamy, and Harsha Vardhan Simhadri. 2021. FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Search. arXiv:2105.09613 [cs.IR]"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-024-00894-5"},{"key":"e_1_2_1_59_1","first-page":"13766","volume-title":"NeurIPS","volume":"32","author":"Subramanya Suhas Jayaram","year":"2019","unstructured":"Suhas Jayaram Subramanya, Fnu Devvrit, Harsha Vardhan Simhadri, Ravishankar Krishnawamy, and Rohan Kadekodi. 2019. DiskANN: Fast Accurate Billion-Point Nearest Neighbor Search on a Single Node. In NeurIPS, Vol. 32. Curran Associates, Inc., Vancouver, Canada, 13766-13776."},{"key":"e_1_2_1_60_1","first-page":"563","article-title":"Quality and Efficiency in High Dimensional Nearest Neighbor Search. In SIGMOD. ACM, Providence","author":"Tao Yufei","year":"2009","unstructured":"Yufei Tao, Ke Yi, Cheng Sheng, and Panos Kalnis. 2009. Quality and Efficiency in High Dimensional Nearest Neighbor Search. In SIGMOD. ACM, Providence, RI, USA, 563-576.","journal-title":"RI, USA"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3295831"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2487976"},{"key":"e_1_2_1_63_1","first-page":"2614","article-title":"Milvus: A Purpose-Built Vector Data Management System. In SIGMOD. ACM, Xi'an","author":"Wang Jianguo","year":"2021","unstructured":"Jianguo Wang, Xiaomeng Yi, Rentong Guo, Hai Jin, Peng Xu, Shengjun Li, Xiangyu Wang, Xiangzhou Guo, Chengming Li, Xiaohai Xu, Kun Yu, Yuxing Yuan, Yinghao Zou, Jiquan Long, Yudong Cai, Zhenxiang Li, Zhifeng Zhang, Yihua Mo, Jun Gu, Ruiyi Jiang, Yi Wei, and Charles Xie. 2021b. Milvus: A Purpose-Built Vector Data Management System. In SIGMOD. ACM, Xi'an, China, 2614-2627.","journal-title":"China"},{"key":"e_1_2_1_64_1","first-page":"15738","volume-title":"NeurIPS","volume":"36","author":"Wang Mengzhao","year":"2023","unstructured":"Mengzhao Wang, Lingwei Lv, Xiaoliang Xu, Yuxiang Wang, Qiang Yue, and Jiongkang Ni. 2023. An Efficient and Robust Framework for Approximate Nearest Neighbor Search with Attribute Constraint. In NeurIPS, Vol. 36. Curran Associates, Inc., New Orleans, LA, USA, 15738-15751."},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476255"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-025-00285-y"},{"key":"e_1_2_1_67_1","first-page":"4668","article-title":"Steiner-Hardness","volume":"17","author":"Wang Zeyu","year":"2025","unstructured":"Zeyu Wang, Qitong Wang, Xiaoxing Cheng, Peng Wang, Themis Palpanas, and Wei Wang. 2025b. Steiner-Hardness: A Query Hardness Measure for Graph-Based ANN Indexes. Proc. VLDB Endow., Vol. 17, 13 (2025), 4668-4682.","journal-title":"A Query Hardness Measure for Graph-Based ANN Indexes. Proc. VLDB Endow."},{"key":"e_1_2_1_68_1","volume-title":"CORAG: A Cost-Constrained Retrieval Optimization System for Retrieval-Augmented Generation. arXiv:2411.00744 [cs.DB]","author":"Wang Ziting","year":"2024","unstructured":"Ziting Wang, Haitao Yuan, Wei Dong, Gao Cong, and Feifei Li. 2024. CORAG: A Cost-Constrained Retrieval Optimization System for Retrieval-Augmented Generation. arXiv:2411.00744 [cs.DB]"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415541"},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.14778\/3665844.3665854"},{"key":"e_1_2_1_71_1","volume-title":"HQANN: Efficient and Robust Similarity Search for Hybrid Queries with Structured and Unstructured Constraints. In CIKM","author":"Wu Wei","year":"2022","unstructured":"Wei Wu, Junlin He, Yu Qiao, Guoheng Fu, Li Liu, and Jin Yu. 2022. HQANN: Efficient and Robust Similarity Search for Hybrid Queries with Structured and Unstructured Constraints. In CIKM. ACM, Atlanta, GA, USA, 4580-4584."},{"key":"e_1_2_1_72_1","volume-title":"Proc. ACM Manag. Data","volume":"2","author":"Xu Yuexuan","year":"2024","unstructured":"Yuexuan Xu, Jianyang Gao, Yutong Gou, Cheng Long, and Christian S. Jensen. 2024. iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search. Proc. ACM Manag. Data, Vol. 2, 6 (2024), 26 pages."},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.226"},{"key":"e_1_2_1_74_1","volume-title":"Xiyue Gao, Qianru Wang, Yanguo Peng, and Jiangtao Cui.","author":"Yang Shuo","year":"2024","unstructured":"Shuo Yang, Jiadong Xie, Yingfan Liu, Jeffrey Xu Yu, Xiyue Gao, Qianru Wang, Yanguo Peng, and Jiangtao Cui. 2024. Revisiting the Index Construction of Proximity Graph-Based Approximate Nearest Neighbor Search. arXiv:2410.01231 [cs.DB]"},{"key":"e_1_2_1_75_1","first-page":"2241","article-title":"PASE: PostgreSQL Ultra-High-Dimensional Approximate Nearest Neighbor Search Extension. In SIGMOD. ACM, Portland","author":"Yang Wen","year":"2020","unstructured":"Wen Yang, Tao Li, Gai Fang, and Hong Wei. 2020. PASE: PostgreSQL Ultra-High-Dimensional Approximate Nearest Neighbor Search Extension. In SIGMOD. ACM, Portland, OR, USA, 2241-2253.","journal-title":"OR, USA"},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/3709679"},{"key":"e_1_2_1_77_1","unstructured":"Song Yu Shengyuan Lin Shufeng Gong Yongqing Xie Ruicheng Liu Yijie Zhou Ji Sun Yanfeng Zhang Guoliang Li and Ge Yu. 2025. A Topology-Aware Localized Update Strategy for Graph-Based ANN Index. arXiv:2503.00402 [cs.DB]"},{"key":"e_1_2_1_78_1","first-page":"4870","article-title":"Routing-Guided Learned Product Quantization for Graph-Based Approximate Nearest Neighbor Search. In ICDE. IEEE, Utrecht","author":"Yue Qiang","year":"2024","unstructured":"Qiang Yue, Xiaoliang Xu, Yuxiang Wang, Yikun Tao, and Xuliyuan Luo. 2024. Routing-Guided Learned Product Quantization for Graph-Based Approximate Nearest Neighbor Search. In ICDE. IEEE, Utrecht, Netherlands, 4870-4883.","journal-title":"Netherlands"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3725401"},{"key":"e_1_2_1_80_1","volume-title":"VBASE: Unifying Online Vector Similarity Search and Relational Queries via Relaxed Monotonicity","author":"Zhang Qianxi","year":"2023","unstructured":"Qianxi Zhang, Shuotao Xu, Qi Chen, Guoxin Sui, Jiadong Xie, Zhizhen Cai, Yaoqi Chen, Yinxuan He, Yuqing Yang, Fan Yang, Mao Yang, and Lidong Zhou. 2023. VBASE: Unifying Online Vector Similarity Search and Relational Queries via Relaxed Monotonicity. In OSDI. USENIX Association, Boston, MA, USA, 377-395."},{"key":"e_1_2_1_81_1","first-page":"2645","article-title":"ARKGraph","volume":"16","author":"Zuo Chaoji","year":"2023","unstructured":"Chaoji Zuo and Dong Deng. 2023. ARKGraph: All-Range Approximate K-Nearest-Neighbor Graph. Proc. VLDB Endow., Vol. 16, 10 (2023), 2645-2658.","journal-title":"All-Range Approximate K-Nearest-Neighbor Graph. Proc. VLDB Endow."},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1145\/3639324"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3769843","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T04:42:11Z","timestamp":1781325731000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3769843"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,4]]},"references-count":82,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,12,4]]}},"alternative-id":["10.1145\/3769843"],"URL":"https:\/\/doi.org\/10.1145\/3769843","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,4]]}}}