{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:08:56Z","timestamp":1755839336988,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Centre for Perceptual and Interactive Intelligence Limited"},{"name":"Hong Kong General Research Fund","award":["14200817"],"award-info":[{"award-number":["14200817"]}]},{"name":"Hong Kong AoE","award":["AoE\/P-404\/18"],"award-info":[{"award-number":["AoE\/P-404\/18"]}]},{"DOI":"10.13039\/501100010428","name":"Innovation and Technology Fund","doi-asserted-by":"publisher","award":["ITS\/310\/18, ITP\/047\/19LP"],"award-info":[{"award-number":["ITS\/310\/18, ITP\/047\/19LP"]}],"id":[{"id":"10.13039\/501100010428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,10]]},"DOI":"10.1145\/3514221.3520151","type":"proceedings-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T02:33:49Z","timestamp":1655001229000},"page":"2357-2360","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Everest: A Top-K Deep Video Analytics System"],"prefix":"10.1145","author":[{"given":"Ziliang","family":"Lai","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chris","family":"Liu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenxia","family":"Han","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Lo","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ben","family":"Kao","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,6,11]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Pedro Henriques Abreu and et al. 2012. Performance analysis in soccer: a Cartesian coordinates based approach using RoboCup data. Soft Computing (2012)."},{"key":"e_1_3_2_1_2_1","unstructured":"Michael R Anderson and et al. 2018. Predicate optimization for a visual analytics database. SysML (2018)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Favyen Bastani and et al. 2020. MIRIS: fast object track queries in video. In SIGMOD.","DOI":"10.1145\/3318464.3389692"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Favyen Bastani and et al. 2021. MultiScope: Efficient Video Pre-processing for Exploratory Video Analytics. arXiv (2021).","DOI":"10.1145\/3514221.3517835"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Yueting Chen and et al. 2021. Evaluating Temporal Queries Over Video Feeds. In SIGMOD.","DOI":"10.1145\/3448016.3452803"},{"key":"e_1_3_2_1_6_1","volume-title":"Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning. arXiv","author":"Chunduri Pramod","year":"2021","unstructured":"Pramod Chunduri, Jaeho Bang, Yao Lu, and Joy Arulraj. 2021. Zeus: Efficiently Localizing Actions in Videos using Reinforcement Learning. arXiv (2021)."},{"key":"e_1_3_2_1_7_1","volume-title":"Rekall: Specifying video events using compositions of spatiotemporal labels. arXiv preprint arXiv:1910.02993","author":"Fu Daniel Y","year":"2019","unstructured":"Daniel Y Fu and et al. 2019. Rekall: Specifying video events using compositions of spatiotemporal labels. arXiv preprint arXiv:1910.02993 (2019)."},{"key":"e_1_3_2_1_8_1","volume":"201","author":"Godard Cl\u00e9ment","unstructured":"Cl\u00e9ment Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J Brostow. 2019. Digging into self-supervised monocular depth estimation. In CVPR.","journal-title":"Gabriel J Brostow."},{"key":"e_1_3_2_1_9_1","unstructured":"Kaiming He and et al. 2017. Mask r-cnn. In CVPR."},{"key":"e_1_3_2_1_10_1","volume-title":"Focus: Querying large video datasets with low latency and low cost. In OSDI.","author":"Hsieh Kevin","year":"2018","unstructured":"Kevin Hsieh and et al. 2018. Focus: Querying large video datasets with low latency and low cost. In OSDI."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Daniel Kang and et al. 2017. NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale. VLDB (2017).","DOI":"10.14778\/3137628.3137664"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Daniel Kang and et al. 2019. BlazeIt: optimizing declarative aggregation and limit queries for neural network-based video analytics. VLDB (2019).","DOI":"10.14778\/3372716.3372725"},{"key":"e_1_3_2_1_13_1","volume-title":"2020 a. Approximate selection with guarantees using proxies. VLDB","author":"Kang Daniel","year":"2020","unstructured":"Daniel Kang and et al. 2020 a. Approximate selection with guarantees using proxies. VLDB (2020)."},{"key":"e_1_3_2_1_14_1","volume-title":"2020 b. Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data. arXiv","author":"Kang Daniel","year":"2020","unstructured":"Daniel Kang and et al. 2020 b. Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data. arXiv (2020)."},{"key":"e_1_3_2_1_15_1","unstructured":"Daniel Kang and et al. 2021. Exploiting Proximity Search and Easy Examples to Select Rare Events. In NeurIPS."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Nick Koudas and et al. 2020. Video monitoring queries. TKDE (2020).","DOI":"10.1109\/ICDE48307.2020.00115"},{"key":"e_1_3_2_1_17_1","unstructured":"Sanjay Krishnan and et al. 2019. DeepLens: Towards a Visual Data Management System. In CIDR."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3452786"},{"volume-title":"Proceedings of the Seventh ACM SoCC.","author":"Yao","key":"e_1_3_2_1_19_1","unstructured":"Yao Lu and et al. 2016. Optasia: A relational platform for efficient large-scale video analytics. In Proceedings of the Seventh ACM SoCC."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Yao Lu and et al. 2018. Accelerating machine learning inference with probabilistic predicates. In SIGMOD.","DOI":"10.1145\/3183713.3183751"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Luyi Mo and et al. 2013. Cleaning uncertain data for top-k queries. In ICDE.","DOI":"10.1109\/ICDE.2013.6544820"},{"key":"e_1_3_2_1_22_1","unstructured":"Oscar Moll and et al. 2020. ExSample: Efficient Searches on Video Repositories through Adaptive Sampling. arXiv preprint arXiv:2005.09141 (2020)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Shanu Sushmita Hideo Joho Mounia Lalmas and Robert Villa. 2010. Factors affecting click-through behavior in aggregated search interfaces. In CIKM.","DOI":"10.1145\/1871437.1871506"},{"volume-title":"Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining.","author":"Jianbo","key":"e_1_3_2_1_24_1","unstructured":"Jianbo Yuan and et al. 2013. Sentribute: image sentiment analysis from a mid-level perspective. In Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining."}],"event":{"name":"SIGMOD\/PODS '22: International Conference on Management of Data","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"],"location":"Philadelphia PA USA","acronym":"SIGMOD\/PODS '22"},"container-title":["Proceedings of the 2022 International Conference on Management of Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514221.3520151","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3514221.3520151","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:10:06Z","timestamp":1750183806000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514221.3520151"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,10]]},"references-count":24,"alternative-id":["10.1145\/3514221.3520151","10.1145\/3514221"],"URL":"https:\/\/doi.org\/10.1145\/3514221.3520151","relation":{},"subject":[],"published":{"date-parts":[[2022,6,10]]},"assertion":[{"value":"2022-06-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}