{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T04:36:58Z","timestamp":1768106218797,"version":"3.49.0"},"reference-count":8,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>\n            Concerning the usability and efficiency to manage video data generated from large-scale cameras, we demonstrate DoveDB, a declarative and low-latency video database. We devise a more comprehensive video query language called VMQL to improve the expressiveness of previous SQL-like languages, which are augmented with functionalities for model-oriented management and deployment. We also propose a light-weight ingestion scheme to extract tracklets of all the moving objects and build semantic indexes to facilitate efficient query processing. For user interaction, we construct a simulation environment with 120 cameras deployed in a road network and demonstrate three interesting scenarios. Using VMQL, users are allowed to 1) train a visual model using SQL-like statement and deploy it on dozens of target cameras simultaneously for online inference; 2) submit multi-object tracking (MOT) requests on target cameras, store the ingested results and build semantic indexes; and 3) issue an aggregation or top-\n            <jats:italic toggle=\"yes\">k<\/jats:italic>\n            query on the ingested cameras and obtain the response within milliseconds. A preliminary video introduction of DoveDB is available at https:\/\/www.youtube.com\/watch?v=N139dEyvAJk\n          <\/jats:p>","DOI":"10.14778\/3611540.3611582","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:32:37Z","timestamp":1694777557000},"page":"3906-3909","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["DoveDB: A Declarative and Low-Latency Video Database"],"prefix":"10.14778","volume":"16","author":[{"given":"Ziyang","family":"Xiao","sequence":"first","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zepeng","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sai","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kian-Lee","family":"Tan","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Wenisch","author":"Anderson Michael R.","year":"2019","unstructured":"Michael R. Anderson, Michael J. Cafarella, Germ\u00e1n Ros, and Thomas F. Wenisch. 2019. Physical Representation-Based Predicate Optimization for a Visual Analytics Database. In ICDE. IEEE, 1466--1477."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389692"},{"key":"e_1_2_1_3_1","volume-title":"OTIF: Efficient Tracker Pre-processing over Large Video Datasets. SIGMOD","author":"Bastani Favyen","year":"2022","unstructured":"Favyen Bastani and Sam Madden. 2022. OTIF: Efficient Tracker Pre-processing over Large Video Datasets. SIGMOD (2022)."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415498"},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Daren Chao Nick Koudas and Ioannis Xarchakos. 2020. SVQ++: Querying for Object Interactions in Video Streams. In SIGMOD. ACM 2769--2772.","DOI":"10.1145\/3318464.3384701"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3324955"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137628.3137664"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476311.3476360"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3611540.3611582","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T22:36:08Z","timestamp":1757543768000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3611540.3611582"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":8,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["10.14778\/3611540.3611582"],"URL":"https:\/\/doi.org\/10.14778\/3611540.3611582","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2023,8]]},"assertion":[{"value":"2023-08-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}