{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T01:55:47Z","timestamp":1792806947868,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":93,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,9]],"date-time":"2024-06-09T00:00:00Z","timestamp":1717891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61925205,62232009,62102215"],"award-info":[{"award-number":["61925205,62232009,62102215"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFB4503600"],"award-info":[{"award-number":["2023YFB4503600"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["2337806"],"award-info":[{"award-number":["2337806"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,9]]},"DOI":"10.1145\/3626246.3654691","type":"proceedings-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T10:26:39Z","timestamp":1716459999000},"page":"597-604","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":59,"title":["Vector Database Management Techniques and Systems"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9998-2838","authenticated-orcid":false,"given":"James Jie","family":"Pan","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3039-1175","authenticated-orcid":false,"given":"Jianguo","family":"Wang","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1398-0621","authenticated-orcid":false,"given":"Guoliang","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,6,9]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n. d.]. http:\/\/github.com\/facebookresearch\/faiss."},{"key":"e_1_3_2_1_2_1","unstructured":"[n. d.]. http:\/\/github.com\/spotify\/annoy."},{"key":"e_1_3_2_1_3_1","unstructured":"[n. d.]. http:\/\/qdrant.tech."},{"key":"e_1_3_2_1_4_1","unstructured":"[n. d.]. http:\/\/vespa.ai."},{"key":"e_1_3_2_1_5_1","unstructured":"[n. d.]. http:\/\/pinecone.io."},{"key":"e_1_3_2_1_6_1","unstructured":"[n. d.]. http:\/\/milvus.io."},{"key":"e_1_3_2_1_7_1","unstructured":"[n. d.]. http:\/\/github.com\/pgvector."},{"key":"e_1_3_2_1_8_1","unstructured":"[n. d.]. http:\/\/lucene.apache.org."},{"key":"e_1_3_2_1_9_1","unstructured":"[n. d.]. http:\/\/elastic.co."},{"key":"e_1_3_2_1_10_1","unstructured":"[n. d.]. http:\/\/vald.vdaas.org."},{"key":"e_1_3_2_1_11_1","unstructured":"[n. d.]. http:\/\/marqo.ai."},{"key":"e_1_3_2_1_12_1","unstructured":"[n. d.]. http:\/\/github.com\/vearch."},{"key":"e_1_3_2_1_13_1","unstructured":"[n. d.]. http:\/\/weaviate.io."},{"key":"e_1_3_2_1_14_1","unstructured":"[n. d.]. http:\/\/euclidesdb.readthedocs.io."},{"key":"e_1_3_2_1_15_1","unstructured":"[n. d.]. http:\/\/trychroma.com."},{"key":"e_1_3_2_1_16_1","unstructured":"[n. d.]. http:\/\/nuclia.com."},{"key":"e_1_3_2_1_17_1","unstructured":"[n. d.]. http:\/\/singlestore.com."},{"key":"e_1_3_2_1_18_1","unstructured":"[n. d.]. http:\/\/opensearch.org."},{"key":"e_1_3_2_1_19_1","unstructured":"[n. d.]. http:\/\/solr.apache.org."},{"key":"e_1_3_2_1_20_1","unstructured":"[n. d.]. http:\/\/github.com\/microsoft\/SPTAG."},{"key":"e_1_3_2_1_21_1","volume-title":"Mount","author":"Abdelkader Ahmed","year":"2019","unstructured":"Ahmed Abdelkader, Sunil Arya, Guilherme D. da Fonseca, and David M. Mount. 2019. Approximate nearest neighbor searching with non-Euclidean and weighted distances. In SODA. 355--372."},{"key":"e_1_3_2_1_22_1","volume-title":"Keim","author":"Aggarwal Charu C.","year":"2001","unstructured":"Charu C. Aggarwal, Alexander Hinneburg, and Daniel A. Keim. 2001. On the surprising behavior of distance metrics in high dimensional space. In ICDT."},{"key":"e_1_3_2_1_23_1","unstructured":"Alexandr Andoni Piotr Indyk Thijs Laarhoven Ilya Razenshteyn and Ludwig Schmidt. 2015. Practical and optimal LSH for angular distance. In NeurIPS. 1225--1233."},{"key":"e_1_3_2_1_24_1","unstructured":"Alexandr Andoni Piotr Indyk and Ilya Razenshteyn. 2018. Approximate nearest neighbor search in high dimensions. In ICM. 3287--3318."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Alexandr Andoni and Ilya Razenshteyn. 2015. Optimal data-dependent hashing for approximate near neighbors. In STOC. 793--801.","DOI":"10.1145\/2746539.2746553"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Fabien Andr\u00e9 Anne-Marie Kermarrec and Nicolas Le Scouarnec. 2017. Accelerated Nearest Neighbor Search with Quick ADC. In ICMR.","DOI":"10.1145\/3078971.3078992"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2952606"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Akari Asai Sewon Min Zexuan Zhong and Danqi Chen. 2023. Retrieval-based Language Models and Applications. In ACL.","DOI":"10.18653\/v1\/2023.acl-tutorials.6"},{"key":"e_1_3_2_1_29_1","volume-title":"ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms. Inform. Syst. 87","author":"Aum\u00fcller Martin","year":"2020","unstructured":"Martin Aum\u00fcller, Erik Bernhardsson, and Alexander Faithfull. 2020. ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms. Inform. Syst. 87 (2020)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Kevin Beyer Jonathan Goldstein Raghu Ramakrishnan and Uri Shaft. 1999. When is ?nearest neighbor\" meaningful?. In ICDT.","DOI":"10.1007\/3-540-49257-7_15"},{"key":"e_1_3_2_1_31_1","unstructured":"Wei-Cheng Chang Felix X. Yu Yin-Wen Chang Yiming Yang and Sanjiv Kumar. 2020. Pre-training tasks for embedding-based large-scale retrieval. In ICLR."},{"key":"e_1_3_2_1_32_1","volume-title":"SPANN: Highly-efficient billion-scale approximate nearest neighbor search. In NeurIPS.","author":"Chen Qi","year":"2021","unstructured":"Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zengzhong Li, Mao Yang, Jingdong Wang, Mao Yang, and Jingdong Wang. 2021. SPANN: Highly-efficient billion-scale approximate nearest neighbor search. In NeurIPS."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Sanjoy Dasgupta and Yoav Freund. 2008. Random projection trees and low dimensional manifolds. In STOC. 537--546.","DOI":"10.1145\/1374376.1374452"},{"key":"e_1_3_2_1_34_1","unstructured":"Sanjoy Dasgupta and Kaushik Sinha. 2013. Randomized partition trees for exact nearest neighbor search. In COLT. 317--337."},{"key":"e_1_3_2_1_35_1","volume-title":"Mirrokni","author":"Datar Mayur","year":"2004","unstructured":"Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S. Mirrokni. 2004. Locality-sensitive hashing scheme based on p-stable distributions. In SCG. 253--262."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Wei Dong Moses Charikar and Kai Li. 2011. Efficient k-nearest neighbor graph construction for generic similarity measures. In WWW.","DOI":"10.1145\/1963405.1963487"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476311.3476407"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01975867"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Danyel Fisher Igor Popov Steven Drucker and M. C. Schraefel. 2012. Trust me I'm partially right: Incremental visualization lets analysts explore large datasets faster. In SIGCHI. 1673--1682.","DOI":"10.1145\/2207676.2208294"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.14778\/3303753.3303754"},{"key":"e_1_3_2_1_41_1","unstructured":"Tiezheng Ge Kaiming He Qifa Ke and Jian Sun. 2013. Optimized product quantization for approximate nearest neighbor search. In CVPR. 2946--2953."},{"key":"e_1_3_2_1_42_1","volume-title":"Gray","author":"Gersho Allen","year":"1991","unstructured":"Allen Gersho and Robert M. Gray. 1991. Vector Quantization and Signal Compression. Springer."},{"key":"e_1_3_2_1_43_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.","DOI":"10.1145\/3543507.3583552"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/MASSP.1984.1162229"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.14778\/3554821.3554843"},{"key":"e_1_3_2_1_46_1","first-page":"3887","article-title":"Accelerating large-scale inference with anisotropic vector quantization","volume":"119","author":"Guo Ruiqi","year":"2020","unstructured":"Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar. 2020. Accelerating large-scale inference with anisotropic vector quantization. In ICML, Vol. 119. 3887--3896.","journal-title":"ICML"},{"key":"e_1_3_2_1_47_1","volume-title":"FANNG: Fast approximate nearest neighbour graphs. In CVPR.","author":"Harwood Ben","year":"2016","unstructured":"Ben Harwood and Tom Drummond. 2016. FANNG: Fast approximate nearest neighbour graphs. In CVPR."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Piotr Indyk and Rajeev Motwani. 1998. Approximate nearest neighbors: Towards removing the curse of dimensionality. In STOC. 604--613.","DOI":"10.1145\/276698.276876"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.57"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2019.2921572"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Yubin Kim. 2022. Applications and future of dense retrieval in industry. In SIGIR. 3373--3374.","DOI":"10.1145\/3477495.3536324"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1038\/35022643"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/1773912.1773922"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"crossref","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. 9--16.","DOI":"10.1145\/3284028.3284030"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2909204"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056489"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2013.10.006"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2889473"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.3169\/mta.6.2"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.14778\/3565838.3565847"},{"key":"e_1_3_2_1_61_1","volume-title":"Fractional Norms and Quasinorms Do Not Help to Overcome the Curse of Dimensionality. Entropy 22, 10","author":"Mirkes Evgeny M.","year":"2020","unstructured":"Evgeny M. Mirkes, Jeza Allohibi, and Alexander Gorban. 2020. Fractional Norms and Quasinorms Do Not Help to Overcome the Curse of Dimensionality. Entropy 22, 10 (2020)."},{"key":"e_1_3_2_1_62_1","volume-title":"FLANN: Fast library for approximate nearest neighbors. In VISAPP.","author":"Muja Marius","year":"2009","unstructured":"Marius Muja and David G Lowe. 2009. FLANN: Fast library for approximate nearest neighbors. In VISAPP."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"crossref","unstructured":"Gonzalo Navarro. 2002. Searching in metric spaces by spatial approximation. VLDB J. (2002).","DOI":"10.1007\/s007780200060"},{"key":"e_1_3_2_1_64_1","volume-title":"Fleet","author":"Norouzi Mohammad","year":"2013","unstructured":"Mohammad Norouzi and David J. Fleet. 2013. Cartesian k-means. In CVPR."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"crossref","unstructured":"Rodrigo Paredes Edgar Ch\u00e1vez Karina Figueroa and Gonzalo Navarro. 2006. Practical construction of k-nearest neighbor graphs in metric spaces. In WEA.","DOI":"10.1007\/11764298_8"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"crossref","unstructured":"Adam Prout Szu-Po Wang Joseph Victor Zhou Sun Yongzhu Li Jack Chen Evan Bergeron Eric Hanson Robert Walzer Rodrigo Gomes and Nikita Shamgunov. 2022. Cloud-native transactions and analytics in SingleStore. In SIGMOD. 2340--2352.","DOI":"10.1145\/3514221.3526055"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415564"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"crossref","unstructured":"Jianbin Qin Wei Wang Chuan Xiao Ying Zhang and Yaoshu Wang. 2021. High-dimensional similarity query processing for data science. In KDD. 4062--4063.","DOI":"10.1145\/3447548.3470811"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"crossref","unstructured":"Parikshit Ram and Kaushik Sinha. 2019. Revisiting kd-tree for nearest neighbor search. In KDD. 1378--1388.","DOI":"10.1145\/3292500.3330875"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"crossref","unstructured":"Aviad Rubinstein. 2018. Hardness of approximate nearest neighbor search. In STOC. 1260--1268.","DOI":"10.1145\/3188745.3188916"},{"key":"e_1_3_2_1_71_1","unstructured":"R. R. Salakhutdinov and G. E. Hinton. 2007. Learning a nonlinear embedding by preserving class neighbourhood structure. In AISTATS."},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"crossref","unstructured":"C. Silpa-Anan and R. Hartley. 2008. Optimised KD-trees for fast image descriptor matching. In CVPR.","DOI":"10.1109\/CVPR.2008.4587638"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3654990"},{"key":"e_1_3_2_1_74_1","unstructured":"Suhas Jayaram Subramanya Devvrit Rohan Kadekodi Ravishankar Krishnaswamy and Harsha Simhadri. 2019. DiskANN: Fast accurate billion-point nearest neighbor search on a single node. In NeurIPS."},{"key":"e_1_3_2_1_75_1","unstructured":"Jacopo Tagliabue and Ciro Greco. 2023. (Vector) Space is not the final frontier: Product search as program synthesis. In SIGIR."},{"key":"e_1_3_2_1_76_1","volume-title":"Incorporating entity-level knowledge in pretrained language model for biomedical dense retrieval. Comput. Biol. Med. 166","author":"Tan Jiajie","year":"2023","unstructured":"Jiajie Tan, Jinlong Hu, and Shoubin Dong. 2023. Incorporating entity-level knowledge in pretrained language model for biomedical dense retrieval. Comput. Biol. Med. 166 (2023)."},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02187718"},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"crossref","unstructured":"Jing Wang Jingdong Wang Gang Zeng Zhuowen Tu Rui Gan and Shipeng Li. 2012. Scalable k-NN graph construction for visual descriptors. In CVPR. 1106--1113.","DOI":"10.1109\/CVPR.2012.6247790"},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457550"},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"crossref","unstructured":"Jianguo Wang and Qizhen Zhang. 2023. Disaggregated database systems. In SIGMOD. 37--44.","DOI":"10.1145\/3555041.3589403"},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699960"},{"key":"e_1_3_2_1_82_1","doi-asserted-by":"crossref","unstructured":"Wenping Wang Yunxi Guo Chiyao Shen Shuai Ding Guangdeng Liao Hao Fu and Pramodh Karanth Prabhakar. 2023. Integrity and junkiness failure handling for embedding-based retrieval: A case study in social network search. In SIGIR.","DOI":"10.1145\/3539618.3591831"},{"key":"e_1_3_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1038\/30918"},{"key":"e_1_3_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415541"},{"key":"e_1_3_2_1_85_1","unstructured":"Yair Weiss Antonio Torralba and Rob Fergus. 2008. Spectral hashing. In NeurIPS. 1753--1760."},{"key":"e_1_3_2_1_86_1","doi-asserted-by":"crossref","unstructured":"Ryan Williams. 2018. On the difference between closest furthest and orthogonal pairs: Nearly-linear vs barely-subquadratic complexity. In SODA. 1207--1215.","DOI":"10.1137\/1.9781611975031.78"},{"key":"e_1_3_2_1_87_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."},{"key":"e_1_3_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2017.2740954"},{"key":"e_1_3_2_1_89_1","unstructured":"Artem Babenko Yandex and Victor Lempitsky. 2016. Efficient indexing of billion-scale datasets of deep descriptors. In CVPR. 2055--2063."},{"key":"e_1_3_2_1_90_1","volume-title":"PASE: PostgreSQL ultra-high-dimensional approximate nearest neighbor search extension. In SIGMOD. 2241--2253.","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--2253."},{"key":"e_1_3_2_1_91_1","volume-title":"Rundensteiner","author":"Zhang Huayi","year":"2020","unstructured":"Huayi Zhang, Lei Cao, Yizhou Yan, Samuel Madden, and Elke A. Rundensteiner. 2020. Continuously adaptive similarity search. In SIGMOD. 2601--2616."},{"key":"e_1_3_2_1_92_1","doi-asserted-by":"crossref","unstructured":"Yunan Zhang Shige Liu and Jianguo Wang. 2024. Are there fundamental limitations in supporting vector data management in relational databases? A case study of PostgreSQL. In ICDE.","DOI":"10.1109\/ICDE60146.2024.00280"},{"key":"e_1_3_2_1_93_1","doi-asserted-by":"crossref","unstructured":"Zhilin Zhang Ke Wang Chen Lin and Weipeng Lin. 2018. Secure top-k inner product retrieval. In CIKM. 77--86.","DOI":"10.1145\/3269206.3271791"}],"event":{"name":"SIGMOD\/PODS '24: International Conference on Management of Data","location":"Santiago AA Chile","acronym":"SIGMOD\/PODS '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Companion of the 2024 International Conference on Management of Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626246.3654691","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3626246.3654691","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T11:28:50Z","timestamp":1755862130000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626246.3654691"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,9]]},"references-count":93,"alternative-id":["10.1145\/3626246.3654691","10.1145\/3626246"],"URL":"https:\/\/doi.org\/10.1145\/3626246.3654691","relation":{},"subject":[],"published":{"date-parts":[[2024,6,9]]},"assertion":[{"value":"2024-06-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}