{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T20:52:27Z","timestamp":1781211147039,"version":"3.54.1"},"reference-count":95,"publisher":"Association for Computing Machinery (ACM)","issue":"6","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:p>Vector search is widely employed in recommendation systems, search engines, etc. With the explosive growth of online data and streaming processing engines, streaming vector search has attracted increasing research attention. However, prevailing vector search systems like Vearch, Vespa, and Milvus typically operate as external batch services for streaming processing requirements, resulting in sub-optimal performance for streaming processing scenarios.<\/jats:p>\n          <jats:p>In this paper, we propose VStream, a distributed streaming vector search system. Implementing such a system is non-trivial, raising three technical challenges in streaming adaptability, system scalability, and real-time response. Specifically, VStream offers a dynamic partitioner that adapts to data distribution changes in vector streams. Additionally, VStream features an effective hierarchical storage architecture facilitated by streaming state management, enabling a hybrid of four-level storage media with diverse access speeds and targets. Furthermore, VStream utilizes dynamic hot-cold patterns, such as access frequency, in the streaming vector data, incorporating a specialized hot-cold separation mechanism to enhance query efficiency. Extensive experiments prove that VStream outperforms existing vector search systems, e.g., achieving 251\u2013373\u00d7 improvements in query efficiency, 2.2\u20132.5\u00d7 savings in CPU usage, and 1.5\u20132.0\u00d7 reductions in memory overhead.<\/jats:p>","DOI":"10.14778\/3725688.3725692","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T14:19:21Z","timestamp":1756477161000},"page":"1593-1606","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["VStream: A Distributed Streaming Vector Search System"],"prefix":"10.14778","volume":"18","author":[{"given":"Shenghao","family":"Gong","sequence":"first","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haobo","family":"Sun","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziquan","family":"Fang","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunjun","family":"Gao","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2024. Annoy: Approximate Nearest Neighbors Oh Yeah. https:\/\/github.com\/spotify\/annoy"},{"key":"e_1_2_1_2_1","unstructured":"2024. Benchmarks for Billion-Scale Similarity Search. https:\/\/research.yandex.com\/datasets\/biganns"},{"key":"e_1_2_1_3_1","unstructured":"2024. ChromaDB. https:\/\/www.trychroma.com\/"},{"key":"e_1_2_1_4_1","unstructured":"2024. Datasets for approximate nearest neighbor search. http:\/\/corpus-texmex.irisa.fr\/"},{"key":"e_1_2_1_5_1","unstructured":"2024. DB Engines Ranking. https:\/\/db-engines.com\/en\/ranking\/vector+dbms"},{"key":"e_1_2_1_6_1","unstructured":"2024. ElasticSearch: Open Source Distributed RESTful Search Engine. https:\/\/github.com\/elastic\/elasticsearch"},{"key":"e_1_2_1_7_1","unstructured":"2024. Flink Guide. https:\/\/nightlies.apache.org\/flink\/flink-docs-release-1.18\/"},{"key":"e_1_2_1_8_1","unstructured":"2024. Flink Milvus Connector. https:\/\/github.com\/CuitingChen\/flink-connector-milvus"},{"key":"e_1_2_1_9_1","unstructured":"2024. Milvus Sizing Tool. https:\/\/milvus.io\/tools\/sizing\/"},{"key":"e_1_2_1_10_1","unstructured":"2024. Pinecone. https:\/\/www.pinecone.io\/"},{"key":"e_1_2_1_11_1","unstructured":"2024. PostgreSQL: The World's Most Advanced Open Source Relational Database. https:\/\/www.postgresql.org\/"},{"key":"e_1_2_1_12_1","unstructured":"2024. Qdrant. https:\/\/qdrant.tech\/"},{"key":"e_1_2_1_13_1","unstructured":"2024. SPTAG: A library for fast approximate nearest neighbor search. https:\/\/github.com\/microsoft\/SPTAG"},{"key":"e_1_2_1_14_1","unstructured":"2024. Twitter Dataset. https:\/\/snap.stanford.edu\/data\/twitter7.html"},{"key":"e_1_2_1_15_1","unstructured":"2024. Vald. https:\/\/github.com\/vdaas\/vald"},{"key":"e_1_2_1_16_1","unstructured":"2024. Vespa. https:\/\/vespa.ai\/"},{"key":"e_1_2_1_17_1","unstructured":"2024. Weaviate. https:\/\/github.com\/semi-technologies\/weaviate"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2402.02044"},{"key":"e_1_2_1_19_1","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.14778\/2824032.2824076","article-title":"The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded","volume":"8","author":"Akidau Tyler","year":"2015","unstructured":"Tyler Akidau, Robert Bradshaw, Craig Chambers, Slava Chernyak, Rafael Fern\u00e1ndez-Moctezuma, Reuven Lax, Sam McVeety, Daniel Mills, Frances Perry, Eric Schmidt, and Sam Whittle. 2015. The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing. Proc. VLDB Endow. 8, 12 (2015), 1792\u20131803.","journal-title":"Out-of-Order Data Processing. Proc. VLDB Endow."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-023-04159-8"},{"key":"e_1_2_1_21_1","volume-title":"Mount","author":"Arya Sunil","year":"1993","unstructured":"Sunil Arya and David M. Mount. 1993. Approximate Nearest Neighbor Queries in Fixed Dimensions. In ACM\/SIGACT-SIAM. 271\u2013280."},{"key":"e_1_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Dmitry Baranchuk Artem Babenko and Yury Malkov. 2018. Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors. In ECCV. 209\u2013224.","DOI":"10.1007\/978-3-030-01258-8_13"},{"key":"e_1_2_1_23_1","volume-title":"Blelloch and Magdalen Dobson","author":"Guy","year":"2022","unstructured":"Guy E. Blelloch and Magdalen Dobson. 2022. Parallel Nearest Neighbors in Low Dimensions with Batch Updates. In ALENEX, Cynthia A. Phillips and Bettina Speckmann (Eds.). 195\u2013208."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00051"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-7552(98)00110-X"},{"key":"e_1_2_1_26_1","volume-title":"Amir Ingber, and Edo Liberty.","author":"Bruch Sebastian","year":"2023","unstructured":"Sebastian Bruch, Franco Maria Nardini, Amir Ingber, and Edo Liberty. 2023. An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors. CoRR abs\/2301.10622 (2023)."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3609797"},{"key":"e_1_2_1_28_1","volume-title":"Yuanzhi Li, Scott M. Lundberg, Harsha Nori, Hamid Palangi, Marco T\u00falio Ribeiro, and Yi Zhang.","author":"Bubeck S\u00e9bastien","year":"2023","unstructured":"S\u00e9bastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott M. Lundberg, Harsha Nori, Hamid Palangi, Marco T\u00falio Ribeiro, and Yi Zhang. 2023. Sparks of Artificial General Intelligence: Early experiments with GPT-4. CoRR abs\/2303.12712 (2023)."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137765.3137777"},{"key":"e_1_2_1_30_1","volume-title":"Apache flink: Stream and batch processing in a single engine. The Bulletin of the Technical Committee on Data Engineering 38, 4","author":"Carbone Paris","year":"2015","unstructured":"Paris Carbone, Asterios Katsifodimos, Stephan Ewen, Volker Markl, Seif Haridi, and Kostas Tzoumas. 2015. Apache flink: Stream and batch processing in a single engine. The Bulletin of the Technical Committee on Data Engineering 38, 4 (2015)."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/214451.214456"},{"key":"e_1_2_1_32_1","volume-title":"SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search. In NeurIPS. 5199\u20135212.","author":"Chen Qi","year":"2021","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 Neighborhood Search. In NeurIPS. 5199\u20135212."},{"key":"e_1_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Heng-Tze Cheng Levent Koc Jeremiah Harmsen Tal Shaked Tushar Chandra Hrishi Aradhye Glen Anderson Greg Corrado Wei Chai Mustafa Ispir Rohan Anil Zakaria Haque Lichan Hong Vihan Jain Xiaobing Liu and Hemal Shah. 2016. Wide & Deep Learning for Recommender Systems. In DLRS@RecSys. 7\u201310.","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_2_1_34_1","volume-title":"M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. In VLDB. 426\u2013435.","author":"Ciaccia Paolo","year":"1997","unstructured":"Paolo Ciaccia, Marco Patella, and Pavel Zezula. 1997. M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. In VLDB. 426\u2013435."},{"key":"e_1_2_1_35_1","unstructured":"Open Geospatial Consortium et al. 2017. Discrete Global Grid Systems Abstract Specification."},{"key":"e_1_2_1_36_1","volume-title":"Deep Neural Networks for YouTube Recommendations. In ACM Conference on Recommender Systems. 191\u2013198","author":"Covington Paul","year":"2016","unstructured":"Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In ACM Conference on Recommender Systems. 191\u2013198."},{"key":"e_1_2_1_37_1","volume-title":"Haruna Chiroma, Shafi'i Muhammad Abdulhamid, Adebayo Olusola Adetunmbi, and Opeyemi Emmanuel Ajibuwa.","author":"Dada Emmanuel Gbenga","year":"2019","unstructured":"Emmanuel Gbenga Dada, Joseph Stephen Bassi, Haruna Chiroma, Shafi'i Muhammad Abdulhamid, Adebayo Olusola Adetunmbi, and Opeyemi Emmanuel Ajibuwa. 2019. Machine learning for email spam filtering: review, approaches and open research problems. Heliyon 5 (2019)."},{"key":"e_1_2_1_38_1","volume-title":"ACM Symposium on Computational Geometry. 253\u2013262","author":"Datar Mayur","unstructured":"Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S. Mirrokni. 2004. Locality-sensitive hashing scheme based on p-stable distributions. In ACM Symposium on Computational Geometry. 253\u2013262."},{"key":"e_1_2_1_39_1","doi-asserted-by":"crossref","first-page":"112","DOI":"10.14778\/3282495.3282498","article-title":"The Lernaean Hydra of Data Series Similarity Search: An Experimental Evaluation of the State of the Art","volume":"12","author":"Echihabi Karima","year":"2018","unstructured":"Karima Echihabi, Kostas Zoumpatianos, Themis Palpanas, and Houda Benbrahim. 2018. The Lernaean Hydra of Data Series Similarity Search: An Experimental Evaluation of the State of the Art. Proc. VLDB Endow. 12, 2 (2018), 112\u2013127.","journal-title":"Proc. VLDB Endow."},{"key":"e_1_2_1_40_1","doi-asserted-by":"crossref","first-page":"403","DOI":"10.14778\/3368289.3368303","article-title":"Return of the Lernaean Hydra: Experimental Evaluation of Data Series Approximate Similarity Search","volume":"13","author":"Echihabi Karima","year":"2019","unstructured":"Karima Echihabi, Kostas Zoumpatianos, Themis Palpanas, and Houda Benbrahim. 2019. Return of the Lernaean Hydra: Experimental Evaluation of Data Series Approximate Similarity Search. Proc. VLDB Endow. 13, 3 (2019), 403\u2013420.","journal-title":"Proc. VLDB Endow."},{"key":"e_1_2_1_41_1","volume-title":"Paired compressed cover trees guarantee a near linear parametrized complexity for all k-nearest neighbors search in an arbitrary metric space. CoRR abs\/2201.06553","author":"Elkin Yury","year":"2022","unstructured":"Yury Elkin and Vitaliy Kurlin. 2022. Paired compressed cover trees guarantee a near linear parametrized complexity for all k-nearest neighbors search in an arbitrary metric space. CoRR abs\/2201.06553 (2022)."},{"key":"e_1_2_1_42_1","volume-title":"8th International Conference on Learning Representations, ICLR 2020","author":"Esser Steven K.","year":"2020","unstructured":"Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S. Modha. 2020. Learned Step Size quantization. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26\u201330, 2020. OpenReview.net."},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104743"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.14778\/3303753.3303754"},{"key":"e_1_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Junhao Gan Jianlin Feng Qiong Fang and Wilfred Ng. 2012. Locality-sensitive hashing scheme based on dynamic collision counting. In SIGMOD. 541\u2013552.","DOI":"10.1145\/2213836.2213898"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.240"},{"key":"e_1_2_1_47_1","doi-asserted-by":"crossref","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. 3406\u20133416.","DOI":"10.1145\/3543507.3583552"},{"key":"e_1_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Mihajlo Grbovic and Haibin Cheng. 2018. Real-time Personalization using Embeddings for Search Ranking at Airbnb. In SIGKDD. 311\u2013320.","DOI":"10.1145\/3219819.3219885"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.14778\/3554821.3554843"},{"key":"e_1_2_1_50_1","volume-title":"Carterette","author":"Hashemi Helia","year":"2021","unstructured":"Helia Hashemi, Aasish Pappu, Mi Tian, Praveen Chandar, Mounia Lalmas, and Benjamin A. Carterette. 2021. Neural Instant Search for Music and Podcast. In KDD. 2984\u20132992."},{"key":"e_1_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Shilin He Qingwei Lin Jian-Guang Lou Hongyu Zhang Michael R. Lyu and Dongmei Zhang. 2018. Identifying impactful service system problems via log analysis. In ESEC\/SIGSOFT. 60\u201370.","DOI":"10.1145\/3236024.3236083"},{"key":"e_1_2_1_52_1","volume-title":"\u00dcber die stetige Abbildung einer Linie auf ein Fl\u00e4chenst\u00fcck. Dritter Band: Analysis\u00b7 Grundlagen der Mathematik\u00b7 Physik Verschiedenes: Nebst Einer Lebensgeschichte","author":"Hilbert David","year":"1935","unstructured":"David Hilbert and David Hilbert. 1935. \u00dcber die stetige Abbildung einer Linie auf ein Fl\u00e4chenst\u00fcck. Dritter Band: Analysis\u00b7 Grundlagen der Mathematik\u00b7 Physik Verschiedenes: Nebst Einer Lebensgeschichte (1935), 1\u20132."},{"key":"e_1_2_1_53_1","volume-title":"Heck","author":"Huang Po-Sen","year":"2013","unstructured":"Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry P. Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In CIKM. 2333\u20132338."},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.14778\/2850469.2850470"},{"key":"e_1_2_1_55_1","volume-title":"Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality. In ACM Symposium on the Theory of Computing. 604\u2013613","author":"Indyk Piotr","year":"1998","unstructured":"Piotr Indyk and Rajeev Motwani. 1998. Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality. In ACM Symposium on the Theory of Computing. 604\u2013613."},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.57"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2019.2921572"},{"key":"e_1_2_1_58_1","doi-asserted-by":"crossref","unstructured":"Omar Khattab and Matei Zaharia. 2020. ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. In SIGIR. 39\u201348.","DOI":"10.1145\/3397271.3401075"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11010141"},{"key":"e_1_2_1_60_1","first-page":"1","article-title":"Kafka: A distributed messaging system for log processing","volume":"11","author":"Kreps Jay","year":"2011","unstructured":"Jay Kreps, Neha Narkhede, Jun Rao, et al. 2011. Kafka: A distributed messaging system for log processing. In NetDB, Vol. 11. 1\u20137.","journal-title":"NetDB"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-018-0138-8"},{"key":"e_1_2_1_62_1","volume-title":"SIGIR, France, July 21\u201325","author":"Lamkhede Sudarshan","year":"2019","unstructured":"Sudarshan Lamkhede and Sudeep Das. 2019. Challenges in Search on Streaming Services: Netflix Case Study. In SIGIR, France, July 21\u201325, 2019, Benjamin Piwowarski, Max Chevalier, \u00c9ric Gaussier, Yoelle Maarek, Jian-Yun Nie, and Falk Scholer (Eds.). 1371\u20131374."},{"key":"e_1_2_1_63_1","volume-title":"Ullman","author":"Leskovec Jure","year":"2014","unstructured":"Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman. 2014. Mining of Massive Datasets, 2nd Ed.","edition":"2"},{"key":"e_1_2_1_64_1","volume-title":"The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform. CoRR abs\/1908.07389","author":"Li Jie","year":"2019","unstructured":"Jie Li, Haifeng Liu, Chuanghua Gui, Jianyu Chen, Zhenyuan Ni, and Ning Wang. 2019. The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform. CoRR abs\/1908.07389 (2019)."},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-018-7049-5"},{"key":"e_1_2_1_66_1","doi-asserted-by":"crossref","first-page":"181803","DOI":"10.1007\/s11704-022-2401-1","article-title":"Indexing dynamic encrypted database in cloud for efficient secure k-nearest neighbor query","volume":"18","author":"Li Xingxin","year":"2024","unstructured":"Xingxin Li, Youwen Zhu, Rui Xu, Jian Wang, and Yushu Zhang. 2024. Indexing dynamic encrypted database in cloud for efficient secure k-nearest neighbor query. Frontiers Comput. Sci. 18, 1 (2024), 181803.","journal-title":"Frontiers Comput. Sci."},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2013.10.006"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2889473"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920886"},{"key":"e_1_2_1_70_1","volume-title":"Finite Scalar Quantization: VQ-VAE Made Simple. In The Twelfth International Conference on Learning Representations, ICLR 2024","author":"Mentzer Fabian","year":"2024","unstructured":"Fabian Mentzer, David Minnen, Eirikur Agustsson, and Michael Tschannen. 2024. Finite Scalar Quantization: VQ-VAE Made Simple. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7\u201311, 2024."},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2855437"},{"key":"e_1_2_1_72_1","volume-title":"Hybrid Model for Analysis of Social Media Posts for Identification of Depression and Measuring Its Severity. ICDSNS","author":"Nanavati Jay","year":"2023","unstructured":"Jay Nanavati and Unnati Patel. 2023. Hybrid Model for Analysis of Social Media Posts for Identification of Depression and Measuring Its Severity. ICDSNS (2023), 1\u20135."},{"key":"e_1_2_1_73_1","volume-title":"The Web Conference.","author":"Page Lawrence","year":"1999","unstructured":"Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank Citation Ranking : Bringing Order to the Web. In The Web Conference."},{"key":"e_1_2_1_74_1","doi-asserted-by":"crossref","unstructured":"Giuseppe Peano and G Peano. 1990. Sur une courbe qui remplit toute une aire plane.","DOI":"10.1007\/978-3-7091-9537-6_6"},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.14778\/2824032.2824078"},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/76359.76361"},{"key":"e_1_2_1_77_1","volume-title":"Keogh","author":"Shieh Jin","year":"2008","unstructured":"Jin Shieh and Eamonn J. Keogh. 2008. iSAX: indexing and mining terabyte sized time series. In SIGKDD. 623\u2013631."},{"key":"e_1_2_1_78_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 abs\/2105.09613 (2021)."},{"key":"e_1_2_1_79_1","unstructured":"Alan Jay Smith. 1987. Design of CPU cache memories."},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.14778\/2556549.2556574"},{"key":"e_1_2_1_81_1","volume-title":"LLaMA: Open and Efficient Foundation Language Models. CoRR abs\/2302.13971","author":"Touvron Hugo","year":"2023","unstructured":"Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth\u00e9e Lacroix, Baptiste Rozi\u00e8re, Naman Goyal, Eric Hambro, Faisal Azhar, Aur\u00e9lien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. LLaMA: Open and Efficient Foundation Language Models. CoRR abs\/2302.13971 (2023)."},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aap9559"},{"key":"e_1_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457550"},{"key":"e_1_2_1_84_1","unstructured":"Roger Weber Hans-J\u00f6rg Schek and Stephen Blott. 1998. A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces. In VLDB. 194\u2013205."},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415541"},{"key":"e_1_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2011.08.003"},{"key":"e_1_2_1_87_1","doi-asserted-by":"crossref","unstructured":"Yuming Xu Hengyu Liang Jin Li Shuotao Xu Qi Chen Qianxi Zhang Cheng Li Ziyue Yang Fan Yang Yuqing Yang Peng Cheng and Mao Yang. 2023. SPFresh: Incremental In-Place Update for Billion-Scale Vector Search. In SOSP Jason Flinn Margo I. Seltzer Peter Druschel Antoine Kaufmann and Jonathan Mace (Eds.). 545\u2013561.","DOI":"10.1145\/3600006.3613166"},{"key":"e_1_2_1_88_1","volume-title":"Proximity Graph Maintenance for Fast Online Nearest Neighbor Search. CoRR abs\/2206.10839","author":"Xu Zhaozhuo","year":"2022","unstructured":"Zhaozhuo Xu, Weijie Zhao, Shulong Tan, Zhixin Zhou, and Ping Li. 2022. Proximity Graph Maintenance for Fast Online Nearest Neighbor Search. CoRR abs\/2206.10839 (2022)."},{"key":"e_1_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.151"},{"key":"e_1_2_1_90_1","volume-title":"PASE: PostgreSQL Ultra-High-Dimensional Approximate Nearest Neighbor Search Extension. In SIGMOD. 2241\u20132253.","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. 2241\u20132253."},{"key":"e_1_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934664"},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2021.3129994"},{"key":"e_1_2_1_93_1","volume-title":"Fast Orthogonal Projection Based on Kronecker Product. In 2015 IEEE International Conference on Computer Vision, ICCV 2015","author":"Zhang Xu","year":"2015","unstructured":"Xu Zhang, Felix X. Yu, Ruiqi Guo, Sanjiv Kumar, Shengjin Wang, and Shih-Fu Chang. 2015. Fast Orthogonal Projection Based on Kronecker Product. In 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7\u201313, 2015. 2929\u20132937."},{"key":"e_1_2_1_94_1","volume-title":"Lyu","author":"Zhu Jieming","year":"2019","unstructured":"Jieming Zhu, Shilin He, Jinyang Liu, Pinjia He, Qi Xie, Zibin Zheng, and Michael R. Lyu. 2019. Tools and benchmarks for automated log parsing. In ICSE, Helen Sharp and Mike Whalen (Eds.). 121\u2013130."},{"key":"e_1_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-016-0442-5"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3725688.3725692","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T14:22:23Z","timestamp":1756477343000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3725688.3725692"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":95,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10.14778\/3725688.3725692"],"URL":"https:\/\/doi.org\/10.14778\/3725688.3725692","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2025,2]]},"assertion":[{"value":"2025-08-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}