{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:27:34Z","timestamp":1778048854400,"version":"3.51.4"},"reference-count":90,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p>With the development of learning-based embedding models, embedding vectors are widely used for analyzing and searching unstructured data. As vector collections exceed billion-scale, fully managed and horizontally scalable vector databases are necessary. In the past three years, through interaction with our 1200+ industry users, we have sketched a vision for the features that next-generation vector databases should have, which include long-term evolvability, tunable consistency, good elasticity, and high performance.<\/jats:p>\n          <jats:p>We present Manu, a cloud native vector database that implements these features. It is difficult to integrate all these features if we follow traditional DBMS design rules. As most vector data applications do not require complex data models and strong data consistency, our design philosophy is to relax the data model and consistency constraints in exchange for the aforementioned features. Specifically, Manu firstly exposes the write-ahead log (WAL) and binlog as backbone services. Secondly, write components are designed as log publishers while all read-only analytic and search components are designed as independent subscribers to the log services. Finally, we utilize multi-version concurrency control (MVCC) and a delta consistency model to simplify the communication and cooperation among the system components. These designs achieve a low coupling among the system components, which is essential for elasticity and evolution. We also extensively optimize Manu for performance and usability with hardware-aware implementations and support for complex search semantics. Manu has been used for many applications, including, but not limited to, recommendation, multimedia, language, medicine and security. We evaluated Manu in three typical application scenarios to demonstrate its efficiency, elasticity, and scalability.<\/jats:p>","DOI":"10.14778\/3554821.3554843","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:28:39Z","timestamp":1664490519000},"page":"3548-3561","source":"Crossref","is-referenced-by-count":76,"title":["Manu"],"prefix":"10.14778","volume":"15","author":[{"given":"Rentong","family":"Guo","sequence":"first","affiliation":[{"name":"Zilliz"}]},{"given":"Xiaofan","family":"Luan","sequence":"additional","affiliation":[{"name":"Zilliz"}]},{"given":"Long","family":"Xiang","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology"}]},{"given":"Xiao","family":"Yan","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology"}]},{"given":"Xiaomeng","family":"Yi","sequence":"additional","affiliation":[{"name":"Zilliz"}]},{"given":"Jigao","family":"Luo","sequence":"additional","affiliation":[{"name":"Zilliz"}]},{"given":"Qianya","family":"Cheng","sequence":"additional","affiliation":[{"name":"Zilliz"}]},{"given":"Weizhi","family":"Xu","sequence":"additional","affiliation":[{"name":"Zilliz"}]},{"given":"Jiarui","family":"Luo","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology and Technical University of Munich"}]},{"given":"Frank","family":"Liu","sequence":"additional","affiliation":[{"name":"Zilliz"}]},{"given":"Zhenshan","family":"Cao","sequence":"additional","affiliation":[{"name":"Zilliz"}]},{"given":"Yanliang","family":"Qiao","sequence":"additional","affiliation":[{"name":"Zilliz"}]},{"given":"Ting","family":"Wang","sequence":"additional","affiliation":[{"name":"Zilliz"}]},{"given":"Bo","family":"Tang","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology"}]},{"given":"Charles","family":"Xie","sequence":"additional","affiliation":[{"name":"Zilliz"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2021. Annoy: Approximate Nearest Neighbors Oh Yeah. https:\/\/github.com\/spotify\/annoy.  2021. Annoy: Approximate Nearest Neighbors Oh Yeah. https:\/\/github.com\/spotify\/annoy."},{"key":"e_1_2_1_2_1","unstructured":"2021. Benchmarks for Billion-Scale Similarity Search. https:\/\/research.yandex.com\/datasets\/biganns.  2021. Benchmarks for Billion-Scale Similarity Search. https:\/\/research.yandex.com\/datasets\/biganns."},{"key":"e_1_2_1_3_1","unstructured":"2021. Billion-Scale Approximate Nearest Neighbor Search Challenge. https:\/\/big-ann-benchmarks.com.  2021. Billion-Scale Approximate Nearest Neighbor Search Challenge. https:\/\/big-ann-benchmarks.com."},{"key":"e_1_2_1_4_1","unstructured":"2021. binlog. https:\/\/hevodata.com\/learn\/using-mysql-binlog\/.  2021. binlog. https:\/\/hevodata.com\/learn\/using-mysql-binlog\/."},{"key":"e_1_2_1_5_1","unstructured":"2021. Datasets for approximate nearest neighbor search. http:\/\/corpus-texmex.irisa.fr\/.  2021. Datasets for approximate nearest neighbor search. http:\/\/corpus-texmex.irisa.fr\/."},{"key":"e_1_2_1_6_1","unstructured":"2021. ElasticSearch: Open Source Distributed RESTful Search Engine. https:\/\/github.com\/elastic\/elasticsearch.  2021. ElasticSearch: Open Source Distributed RESTful Search Engine. https:\/\/github.com\/elastic\/elasticsearch."},{"key":"e_1_2_1_7_1","unstructured":"2021. etcd. https:\/\/etcd.io\/.  2021. etcd. https:\/\/etcd.io\/."},{"key":"e_1_2_1_8_1","unstructured":"2021. MinIO. https:\/\/min.io\/.  2021. MinIO. https:\/\/min.io\/."},{"key":"e_1_2_1_9_1","unstructured":"2021. MySQL. https:\/\/www.mysql.com\/.  2021. MySQL. https:\/\/www.mysql.com\/."},{"key":"e_1_2_1_10_1","unstructured":"2021. Pinecone. https:\/\/www.pinecone.io\/.  2021. Pinecone. https:\/\/www.pinecone.io\/."},{"key":"e_1_2_1_11_1","unstructured":"2021. PostgreSQL: The World's Most Advanced Open Source Relational Database. https:\/\/www.postgresql.org\/.  2021. PostgreSQL: The World's Most Advanced Open Source Relational Database. https:\/\/www.postgresql.org\/."},{"key":"e_1_2_1_12_1","unstructured":"2021. Qdrant. https:\/\/qdrant.tech\/.  2021. Qdrant. https:\/\/qdrant.tech\/."},{"key":"e_1_2_1_13_1","unstructured":"2021. S3. https:\/\/aws.amazon.com\/cn\/s3\/.  2021. S3. https:\/\/aws.amazon.com\/cn\/s3\/."},{"key":"e_1_2_1_14_1","unstructured":"2021. siri. https:\/\/www.apple.com\/siri\/.  2021. siri. https:\/\/www.apple.com\/siri\/."},{"key":"e_1_2_1_15_1","unstructured":"2021. SPTAG: A library for fast approximate nearest neighbor search. https:\/\/github.com\/microsoft\/SPTAG.  2021. SPTAG: A library for fast approximate nearest neighbor search. https:\/\/github.com\/microsoft\/SPTAG."},{"key":"e_1_2_1_16_1","unstructured":"2021. User Behavior Data from Taobao for Recommendation. https:\/\/tianchi.aliyun.com\/dataset\/dataDetail?dataId=649.  2021. User Behavior Data from Taobao for Recommendation. https:\/\/tianchi.aliyun.com\/dataset\/dataDetail?dataId=649."},{"key":"e_1_2_1_17_1","unstructured":"2021. Vald. https:\/\/github.com\/vdaas\/vald.  2021. Vald. https:\/\/github.com\/vdaas\/vald."},{"key":"e_1_2_1_18_1","unstructured":"2021. Vespa. https:\/\/vespa.ai\/.  2021. Vespa. https:\/\/vespa.ai\/."},{"key":"e_1_2_1_19_1","unstructured":"2021. Weaviate. https:\/\/github.com\/semi-technologies\/weaviate.  2021. Weaviate. https:\/\/github.com\/semi-technologies\/weaviate."},{"key":"e_1_2_1_20_1","unstructured":"2021. Xiaoice. https:\/\/en.wikipedia.org\/wiki\/Xiaoice.  2021. Xiaoice. https:\/\/en.wikipedia.org\/wiki\/Xiaoice."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.5555\/1325851.1325909"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.14778\/2856318.2856324"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2361319"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457560"},{"key":"e_1_2_1_25_1","volume-title":"Apache flink: Stream and batch processing in a single engine. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering 36, 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. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering 36, 4 ( 2015 ). Paris Carbone, Asterios Katsifodimos, Stephan Ewen, Volker Markl, Seif Haridi, and Kostas Tzoumas. 2015. Apache flink: Stream and batch processing in a single engine. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering 36, 4 (2015)."},{"key":"e_1_2_1_26_1","volume-title":"SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search. Advances in Neural Information Processing Systems 34","author":"Chen Qi","year":"2021","unstructured":"Qi Chen , Bing Zhao , Haidong Wang , Mingqin Li , Chuanjie Liu , Zhiyong Zheng , Mao Yang , and Jingdong Wang . 2021 . SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search. Advances in Neural Information Processing Systems 34 (2021). Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zhiyong Zheng, Mao Yang, and Jingdong Wang. 2021. SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2903741"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/1374376.1374452"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/n19-1423"},{"key":"e_1_2_1_31_1","volume-title":"NIPS 2017 Bayesian Optimization Workshop (Dec","author":"Falkner Stefan","year":"2017","unstructured":"Stefan Falkner , Aaron Klein , and Frank Hutter . 2017 . Combining hyperband and bayesian optimization . In NIPS 2017 Bayesian Optimization Workshop (Dec 2017). Stefan Falkner, Aaron Klein, and Frank Hutter. 2017. Combining hyperband and bayesian optimization. In NIPS 2017 Bayesian Optimization Workshop (Dec 2017)."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.14778\/3303753.3303754"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.379"},{"key":"e_1_2_1_34_1","first-page":"518","article-title":"Similarity search in high dimensions via hashing","volume":"99","author":"Gionis Aristides","year":"1999","unstructured":"Aristides Gionis , Piotr Indyk , Rajeev Motwani , 1999 . Similarity search in high dimensions via hashing . In Vldb , Vol. 99. 518 -- 529 . Aristides Gionis, Piotr Indyk, Rajeev Motwani, et al. 1999. Similarity search in high dimensions via hashing. In Vldb, Vol. 99. 518--529.","journal-title":"Vldb"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.14778\/3397230.3397243"},{"key":"e_1_2_1_36_1","unstructured":"Ruiqi Guo Sanjiv Kumar Krzysztof Choromanski and David Simcha. 2016. Quantization based fast inner product search. In Artificial Intelligence and Statistics. PMLR 482--490.  Ruiqi Guo Sanjiv Kumar Krzysztof Choromanski and David Simcha. 2016. Quantization based fast inner product search. In Artificial Intelligence and Statistics. PMLR 482--490."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2742795"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-7552(98)00038-5"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415535"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/276698.276876"},{"key":"e_1_2_1_41_1","volume-title":"Optimization of indexing based on k-nearest neighbor graph for proximity search in high-dimensional data. arXiv preprint arXiv:1810.07355","author":"Iwasaki Masajiro","year":"2018","unstructured":"Masajiro Iwasaki and Daisuke Miyazaki . 2018. Optimization of indexing based on k-nearest neighbor graph for proximity search in high-dimensional data. arXiv preprint arXiv:1810.07355 ( 2018 ). Masajiro Iwasaki and Daisuke Miyazaki. 2018. Optimization of indexing based on k-nearest neighbor graph for proximity search in high-dimensional data. arXiv preprint arXiv:1810.07355 (2018)."},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-60936-8_4"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864736"},{"key":"e_1_2_1_44_1","volume-title":"Product quantization for nearest neighbor search","author":"Jegou Herve","year":"2010","unstructured":"Herve Jegou , Matthijs Douze , and Cordelia Schmid . 2010. Product quantization for nearest neighbor search . IEEE transactions on pattern analysis and machine intelligence 33, 1 ( 2010 ), 117--128. Herve Jegou, Matthijs Douze, and Cordelia Schmid. 2010. Product quantization for nearest neighbor search. IEEE transactions on pattern analysis and machine intelligence 33, 1 (2010), 117--128."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2019.2921572"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2619239.2626299"},{"key":"e_1_2_1_47_1","unstructured":"Timothy King. 2019. 80 Percent of Your Data Will Be Unstructured in Five Years. https:\/\/solutionsreview.com\/data-management\/80-percent-of-your-data-will-be-unstructured-in-five-years\/.  Timothy King. 2019. 80 Percent of Your Data Will Be Unstructured in Five Years. https:\/\/solutionsreview.com\/data-management\/80-percent-of-your-data-will-be-unstructured-in-five-years\/."},{"key":"e_1_2_1_48_1","unstructured":"Yann LeCun Yoshua Bengio etal 1995. Convolutional networks for images speech and time series. The handbook of brain theory and neural networks 3361 10 (1995) 1995.  Yann LeCun Yoshua Bengio et al. 1995. Convolutional networks for images speech and time series. The handbook of brain theory and neural networks 3361 10 (1995) 1995."},{"key":"e_1_2_1_49_1","volume-title":"Deep learning. nature 521, 7553","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun , Yoshua Bengio , and Geoffrey Hinton . 2015. Deep learning. nature 521, 7553 ( 2015 ), 436--444. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015), 436--444."},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352141"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3284028.3284030"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00032"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2909204"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/1719970.1719976"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00169"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00095"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.14778\/3397230.3397240"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2889329"},{"key":"e_1_2_1_59_1","volume-title":"Zhe Wang, Moses Charikar, and Kai Li.","author":"Lv Qin","year":"2017","unstructured":"Qin Lv , William Josephson , Zhe Wang, Moses Charikar, and Kai Li. 2017 . Intelligent probing for locality sensitive hashing: Multi-probe LSH and beyond. (2017). Qin Lv, William Josephson, Zhe Wang, Moses Charikar, and Kai Li. 2017. Intelligent probing for locality sensitive hashing: Multi-probe LSH and beyond. (2017)."},{"key":"e_1_2_1_60_1","volume-title":"Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs","author":"Malkov Yu A","year":"2018","unstructured":"Yu A Malkov and Dmitry A Yashunin . 2018. Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs . IEEE transactions on pattern analysis and machine intelligence 42, 4 ( 2018 ), 824--836. Yu A Malkov and Dmitry A Yashunin. 2018. Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE transactions on pattern analysis and machine intelligence 42, 4 (2018), 824--836."},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920886"},{"key":"e_1_2_1_62_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119.  Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119."},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2321376"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJWET.2006.010423"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-02465-9_32"},{"key":"e_1_2_1_66_1","volume-title":"Hm-ann: Efficient billion-point nearest neighbor search on heterogeneous memory. Advances in Neural Information Processing Systems 33","author":"Ren Jie","year":"2020","unstructured":"Jie Ren , Minjia Zhang , and Dong Li . 2020 . Hm-ann: Efficient billion-point nearest neighbor search on heterogeneous memory. Advances in Neural Information Processing Systems 33 (2020). Jie Ren, Minjia Zhang, and Dong Li. 2020. Hm-ann: Efficient billion-point nearest neighbor search on heterogeneous memory. Advances in Neural Information Processing Systems 33 (2020)."},{"key":"e_1_2_1_67_1","volume-title":"Markus Hagenbuchner, and Gabriele Monfardini.","author":"Scarselli Franco","year":"2008","unstructured":"Franco Scarselli , Marco Gori , Ah Chung Tsoi , Markus Hagenbuchner, and Gabriele Monfardini. 2008 . The graph neural network model. IEEE transactions on neural networks 20, 1 (2008), 61--80. Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008. The graph neural network model. IEEE transactions on neural networks 20, 1 (2008), 61--80."},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/564376.564416"},{"key":"e_1_2_1_69_1","volume-title":"Advances in neural information processing systems 27","author":"Shrivastava Anshumali","year":"2014","unstructured":"Anshumali Shrivastava and Ping Li. 2014. Asymmetric LSH (ALSH) for sublinear time maximum inner product search (MIPS). Advances in neural information processing systems 27 ( 2014 ). Anshumali Shrivastava and Ping Li. 2014. Asymmetric LSH (ALSH) for sublinear time maximum inner product search (MIPS). Advances in neural information processing systems 27 (2014)."},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587638"},{"key":"e_1_2_1_71_1","unstructured":"Harsha Vardhan Simhadri George Williams Martin Aum\u00fcller Matthijs Douze Artem Babenko Dmitry Baranchuk Qi Chen Lucas Hosseini Ravishankar Krishnaswamy Gopal Srinivasa etal 2022. Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search. arXiv preprint arXiv:2205.03763 (2022).  Harsha Vardhan Simhadri George Williams Martin Aum\u00fcller Matthijs Douze Artem Babenko Dmitry Baranchuk Qi Chen Lucas Hosseini Ravishankar Krishnaswamy Gopal Srinivasa et al. 2022. Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search. arXiv preprint arXiv:2205.03763 (2022)."},{"key":"e_1_2_1_72_1","volume-title":"Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019","author":"Subramanya Suhas Jayaram","year":"2019","unstructured":"Suhas Jayaram Subramanya , Fnu Devvrit , Harsha Vardhan Simhadri , Ravishankar Krishnaswamy , and Rohan Kadekodi . 2019 . Rand-NSG: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node . In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019 , NeurIPS 2019, December 8--14, 2019, Vancouver, BC, Canada. 13748--13758. Suhas Jayaram Subramanya, Fnu Devvrit, Harsha Vardhan Simhadri, Ravishankar Krishnaswamy, and Rohan Kadekodi. 2019. Rand-NSG: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8--14, 2019, Vancouver, BC, Canada. 13748--13758."},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3386134"},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/2365952.2365972"},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687553.1687609"},{"key":"e_1_2_1_76_1","volume-title":"Neural Information Processing Systems Conference (NIPS","volume":"26","author":"Den Oord A\u00e4ron Van","year":"2013","unstructured":"A\u00e4ron Van Den Oord , Sander Dieleman , and Benjamin Schrauwen . 2013 . Deep content-based music recommendation . In Neural Information Processing Systems Conference (NIPS 2013), Vol. 26 . Neural Information Processing Systems Foundation (NIPS). A\u00e4ron Van Den Oord, Sander Dieleman, and Benjamin Schrauwen. 2013. Deep content-based music recommendation. In Neural Information Processing Systems Conference (NIPS 2013), Vol. 26. Neural Information Processing Systems Foundation (NIPS)."},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3056101"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219869"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.125"},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457550"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415541"},{"key":"e_1_2_1_82_1","first-page":"5745","article-title":"Multiscale quantization for fast similarity search","volume":"30","author":"Wu Xiang","year":"2017","unstructured":"Xiang Wu , Ruiqi Guo , Ananda Theertha Suresh , Sanjiv Kumar , Daniel N Holtmann-Rice , David Simcha , and Felix Yu . 2017 . Multiscale quantization for fast similarity search . Advances in Neural Information Processing Systems 30 (2017), 5745 -- 5755 . Xiang Wu, Ruiqi Guo, Ananda Theertha Suresh, Sanjiv Kumar, Daniel N Holtmann-Rice, David Simcha, and Felix Yu. 2017. Multiscale quantization for fast similarity search. Advances in Neural Information Processing Systems 30 (2017), 5745--5755.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_83_1","unstructured":"SHI Xingjian Zhourong Chen Hao Wang Dit-Yan Yeung Wai-Kin Wong and Wang-chun Woo. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in neural information processing systems. 802--810.  SHI Xingjian Zhourong Chen Hao Wang Dit-Yan Yeung Wai-Kin Wong and Wang-chun Woo. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in neural information processing systems. 802--810."},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3386131"},{"key":"e_1_2_1_85_1","first-page":"10","article-title":"Spark: Cluster computing with working sets","volume":"10","author":"Zaharia Matei","year":"2010","unstructured":"Matei Zaharia , Mosharaf Chowdhury , Michael J Franklin , Scott Shenker , Ion Stoica , 2010 . Spark: Cluster computing with working sets . HotCloud 10 , 10 -- 10 (2010), 95. Matei Zaharia, Mosharaf Chowdhury, Michael J Franklin, Scott Shenker, Ion Stoica, et al. 2010. Spark: Cluster computing with working sets. HotCloud 10, 10--10 (2010), 95.","journal-title":"HotCloud"},{"key":"e_1_2_1_86_1","volume-title":"Recurrent neural network regularization. arXiv preprint arXiv:1409.2329","author":"Zaremba Wojciech","year":"2014","unstructured":"Wojciech Zaremba , Ilya Sutskever , and Oriol Vinyals . 2014. Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 ( 2014 ). Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014. Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)."},{"key":"e_1_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352124"},{"key":"e_1_2_1_88_1","volume-title":"SONG: Approximate Nearest Neighbor Search on GPU. In 36th IEEE International Conference on Data Engineering, ICDE 2020","author":"Zhao Weijie","year":"2020","unstructured":"Weijie Zhao , Shulong Tan , and Ping Li . 2020 . SONG: Approximate Nearest Neighbor Search on GPU. In 36th IEEE International Conference on Data Engineering, ICDE 2020 , Dallas, TX, USA, April 20--24 , 2020. IEEE, 1033--1044. Weijie Zhao, Shulong Tan, and Ping Li. 2020. SONG: Approximate Nearest Neighbor Search on GPU. In 36th IEEE International Conference on Data Engineering, ICDE 2020, Dallas, TX, USA, April 20--24, 2020. IEEE, 1033--1044."},{"key":"e_1_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.14778\/3377369.3377374"},{"key":"e_1_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1145\/2393347.2393377"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3554821.3554843","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:29:34Z","timestamp":1672226974000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3554821.3554843"}},"subtitle":["a cloud native vector database management system"],"short-title":[],"issued":{"date-parts":[[2022,8]]},"references-count":90,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.14778\/3554821.3554843"],"URL":"https:\/\/doi.org\/10.14778\/3554821.3554843","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,8]]}}}