{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:15:38Z","timestamp":1766067338034,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":56,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T00:00:00Z","timestamp":1623196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CCF-1703051, IIS-1546083, CCF-1518703, CNS-1563788"],"award-info":[{"award-number":["CCF-1703051, IIS-1546083, CCF-1518703, CNS-1563788"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DARPA","award":["FA8750-16-2-0032"],"award-info":[{"award-number":["FA8750-16-2-0032"]}]},{"name":"DARPA CRISP"},{"name":"DARPA RC Center","award":["GI18518"],"award-info":[{"award-number":["GI18518"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,6,9]]},"DOI":"10.1145\/3448016.3459242","type":"proceedings-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T17:22:39Z","timestamp":1624036959000},"page":"685-696","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["VSS: A Storage System for Video Analytics"],"prefix":"10.1145","author":[{"given":"Brandon","family":"Haynes","sequence":"first","affiliation":[{"name":"Gray Systems Lab &amp; Microsoft, Redmond, WA, USA"}]},{"given":"Maureen","family":"Daum","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, WA, USA"}]},{"given":"Dong","family":"He","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, WA, USA"}]},{"given":"Amrita","family":"Mazumdar","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, WA, USA"}]},{"given":"Magdalena","family":"Balazinska","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, WA, USA"}]},{"given":"Alvin","family":"Cheung","sequence":"additional","affiliation":[{"name":"University of California, Berkeley, Berkeley, CA, USA"}]},{"given":"Luis","family":"Ceze","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, WA, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,6,18]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Amazon. 2020. Serverless Image Handler. https:\/\/aws.amazon.com\/solutions\/implementations\/serverless-image-handler.  Amazon. 2020. Serverless Image Handler. https:\/\/aws.amazon.com\/solutions\/implementations\/serverless-image-handler."},{"key":"e_1_3_2_2_2_1","volume-title":"Wenisch","author":"Anderson Michael R.","year":"2019","unstructured":"Michael R. Anderson , Michael J. Cafarella , German Ros , and Thomas F . Wenisch . 2019 . Physical Representation-Based Predicate Optimization for a Visual Analytics Database. In ICDE. IEEE , 1466--1477. Michael R. Anderson, Michael J. Cafarella, German Ros, and Thomas F. Wenisch. 2019. Physical Representation-Based Predicate Optimization for a Visual Analytics Database. In ICDE. IEEE, 1466--1477."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Favyen Bastani Songtao He Arjun Balasingam Karthik Gopalakrishnan Mohammad Alizadeh Hari Balakrishnan Michael J. Cafarella Tim Kraska and Sam Madden. 2020 a. MIRIS: Fast Object Track Queries in Video. In SIGMOD. 1907--1921.  Favyen Bastani Songtao He Arjun Balasingam Karthik Gopalakrishnan Mohammad Alizadeh Hari Balakrishnan Michael J. Cafarella Tim Kraska and Sam Madden. 2020 a. MIRIS: Fast Object Track Queries in Video. In SIGMOD. 1907--1921.","DOI":"10.1145\/3318464.3389692"},{"key":"e_1_3_2_2_4_1","first-page":"2877","article-title":"b. Vaas: Video Analytics at Scale","volume":"13","author":"Bastani Favyen","year":"2020","unstructured":"Favyen Bastani , Oscar R. Moll , and Samuel Madden . 2020 b. Vaas: Video Analytics at Scale . VLDB , Vol. 13 , 12 (2020), 2877 -- 2880 . Favyen Bastani, Oscar R. Moll, and Samuel Madden. 2020 b. Vaas: Video Analytics at Scale. VLDB, Vol. 13, 12 (2020), 2877--2880.","journal-title":"VLDB"},{"key":"e_1_3_2_2_5_1","unstructured":"Doug Beaver Sanjeev Kumar Harry C. Li Jason Sobel and Peter Vajgel. 2010. Finding a Needle in Haystack: Facebook's Photo Storage. In OSDI. 47--60.  Doug Beaver Sanjeev Kumar Harry C. Li Jason Sobel and Peter Vajgel. 2010. Finding a Needle in Haystack: Facebook's Photo Storage. In OSDI. 47--60."},{"key":"e_1_3_2_2_6_1","unstructured":"Fabrice Bellard. 2018. FFmpeg. https:\/\/ffmpeg.org.  Fabrice Bellard. 2018. FFmpeg. https:\/\/ffmpeg.org."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219617.3219627"},{"key":"e_1_3_2_2_8_1","volume-title":"YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv CoRR","author":"Bochkovskiy Alexey","year":"2020","unstructured":"Alexey Bochkovskiy , Chien-Yao Wang , and Hong-Yuan Mark Liao . 2020. YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv CoRR , Vol. abs\/ 2004 .10934 ( 2020 ). Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. 2020. YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv CoRR, Vol. abs\/2004.10934 (2020)."},{"key":"e_1_3_2_2_9_1","unstructured":"Cloudview. 2018. Visual IoT: Where the IoT Cloud and Big Data Come Together. http:\/\/www.cloudview.co\/downloads\/10. (2018).  Cloudview. 2018. Visual IoT: Where the IoT Cloud and Big Data Come Together. http:\/\/www.cloudview.co\/downloads\/10. (2018)."},{"key":"e_1_3_2_2_10_1","volume-title":"Amado Assuncc a o, and Paulo J. Cordeiro","author":"Costa Victor H.","year":"2018","unstructured":"Victor H. Costa , Pedro A. Amado Assuncc a o, and Paulo J. Cordeiro . 2018 . A pixel-based complexity model to estimate energy consumption in video decoders. In ICCE. 1--5. Victor H. Costa, Pedro A. Amado Assuncc a o, and Paulo J. Cordeiro. 2018. A pixel-based complexity model to estimate energy consumption in video decoders. In ICCE. 1--5."},{"key":"e_1_3_2_2_11_1","volume-title":"TASM: A Tile-Based Storage Manager for Video Analytics. arxiv","author":"Daum Maureen","year":"2020","unstructured":"Maureen Daum , Brandon Haynes , Dong He , Amrita Mazumdar , Magdalena Balazinska , and Alvin Cheung . 2020 . TASM: A Tile-Based Storage Manager for Video Analytics. arxiv : 2006.02958 Maureen Daum, Brandon Haynes, Dong He, Amrita Mazumdar, Magdalena Balazinska, and Alvin Cheung. 2020. TASM: A Tile-Based Storage Manager for Video Analytics. arxiv: 2006.02958"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Leonardo Mendoncc a de Moura and Nikolaj Bj\u00f8rner. 2008. Z3: An Efficient SMT Solver. In TACAS. 337--340.  Leonardo Mendoncc a de Moura and Nikolaj Bj\u00f8rner. 2008. Z3: An Efficient SMT Solver. In TACAS. 337--340.","DOI":"10.1007\/978-3-540-78800-3_24"},{"key":"e_1_3_2_2_13_1","unstructured":"Facebook. [n.d.]. Zstandard real-time compression algorithm. https:\/\/facebook.github.io\/zstd.  Facebook. [n.d.]. Zstandard real-time compression algorithm. https:\/\/facebook.github.io\/zstd."},{"key":"e_1_3_2_2_14_1","volume-title":"Strong","author":"Gupta-Cledat Vishakha","year":"2017","unstructured":"Vishakha Gupta-Cledat , Luis Remis , and Christina R . Strong . 2017 . Addressing the Dark Side of Vision Research: Storage. In HotStorage . Vishakha Gupta-Cledat, Luis Remis, and Christina R. Strong. 2017. Addressing the Dark Side of Vision Research: Storage. In HotStorage."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s007780100054"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Miska M. Hannuksela Ye Yan Xuehui Huang and Houqiang Li. 2015. Overview of the multiview high efficiency video coding (MV-HEVC) standard. In ICIP. 2154--2158.  Miska M. Hannuksela Ye Yan Xuehui Huang and Houqiang Li. 2015. Overview of the multiview high efficiency video coding (MV-HEVC) standard. In ICIP. 2154--2158.","DOI":"10.1109\/ICIP.2015.7351182"},{"key":"e_1_3_2_2_17_1","volume-title":"VSS: A Storage System for Video Analytics [Technical Report]. arxiv: 2103.16604","author":"Haynes Brandon","year":"2021","unstructured":"Brandon Haynes , Maureen Daum , Dong He , Amrita Mazumdar , Magdalena Balazinska , Alvin Cheung , and Luis Ceze . 2021 . VSS: A Storage System for Video Analytics [Technical Report]. arxiv: 2103.16604 Brandon Haynes, Maureen Daum, Dong He, Amrita Mazumdar, Magdalena Balazinska, Alvin Cheung, and Luis Ceze. 2021. VSS: A Storage System for Video Analytics [Technical Report]. arxiv: 2103.16604"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"Brandon Haynes Maureen Daum Amrita Mazumdar Magdalena Balazinska Alvin Cheung and Luis Ceze. 2020. VisualWorldDB: A DBMS for the Visual World. In CIDR.  Brandon Haynes Maureen Daum Amrita Mazumdar Magdalena Balazinska Alvin Cheung and Luis Ceze. 2020. VisualWorldDB: A DBMS for the Visual World. In CIDR.","DOI":"10.1145\/3299869.3324955"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.14778\/3231751.3231768"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3324955"},{"key":"e_1_3_2_2_21_1","volume-title":"Flash memory in the emerging age of autonomy. Flash Memory Summit","author":"Heinrich Stephan","year":"2017","unstructured":"Stephan Heinrich and Lucid Motors . 2017. Flash memory in the emerging age of autonomy. Flash Memory Summit ( 2017 ). Stephan Heinrich and Lucid Motors. 2017. Flash memory in the emerging age of autonomy. Flash Memory Summit (2017)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Alain Hor\u00e9 and Djemel Ziou. 2010. Image Quality Metrics: PSNR vs. SSIM. In ICPR. 2366--2369.  Alain Hor\u00e9 and Djemel Ziou. 2010. Image Quality Metrics: PSNR vs. SSIM. In ICPR. 2366--2369.","DOI":"10.1109\/ICPR.2010.579"},{"key":"e_1_3_2_2_23_1","volume-title":"Shivaram Venkataraman, Paramvir Bahl, Matthai Philipose, Phillip B. Gibbons, and Onur Mutlu.","author":"Hsieh Kevin","year":"2018","unstructured":"Kevin Hsieh , Ganesh Ananthanarayanan , Peter Bod'i k , Shivaram Venkataraman, Paramvir Bahl, Matthai Philipose, Phillip B. Gibbons, and Onur Mutlu. 2018 . Focus : Querying Large Video Datasets with Low Latency and Low Cost. In OSDI. 269--286. Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bod'i k, Shivaram Venkataraman, Paramvir Bahl, Matthai Philipose, Phillip B. Gibbons, and Onur Mutlu. 2018. Focus: Querying Large Video Datasets with Low Latency and Low Cost. In OSDI. 269--286."},{"key":"e_1_3_2_2_24_1","volume-title":"Body-worn cameras in law enforcement agencies","author":"Hyland Shelley S","year":"2016","unstructured":"Shelley S Hyland . 2018. Body-worn cameras in law enforcement agencies , 2016 . Bureau of Justice Statistics Publication No . NCJ251775 (2018). Shelley S Hyland. 2018. Body-worn cameras in law enforcement agencies, 2016. Bureau of Justice Statistics Publication No. NCJ251775 (2018)."},{"key":"e_1_3_2_2_25_1","volume-title":"Siddhartha Sen, and Ion Stoica.","author":"Jiang Junchen","year":"2018","unstructured":"Junchen Jiang , Ganesh Ananthanarayanan , Peter Bod'i k , Siddhartha Sen, and Ion Stoica. 2018 . Chameleon: scalable adaptation of video analytics. In SIGCOMM. 253--266. Junchen Jiang, Ganesh Ananthanarayanan, Peter Bod'i k, Siddhartha Sen, and Ion Stoica. 2018. Chameleon: scalable adaptation of video analytics. In SIGCOMM. 253--266."},{"key":"e_1_3_2_2_26_1","volume-title":"Chien","author":"Jiang Junchen","year":"2019","unstructured":"Junchen Jiang , Yuhao Zhou , Ganesh Ananthanarayanan , Yuanchao Shu , and Andrew A . Chien . 2019 . Networked Cameras Are the New Big Data Clusters (HotEdgeVideo '19). 1--7. Junchen Jiang, Yuhao Zhou, Ganesh Ananthanarayanan, Yuanchao Shu, and Andrew A. Chien. 2019. Networked Cameras Are the New Big Data Clusters (HotEdgeVideo'19). 1--7."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.14778\/3372716.3372725"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137628.3137664"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"crossref","unstructured":"Daniel Kang Ankit Mathur Teja Veeramacheneni Peter Bailis and Matei Zaharia. 2020. Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics.  Daniel Kang Ankit Mathur Teja Veeramacheneni Peter Bailis and Matei Zaharia. 2020. Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics.","DOI":"10.14778\/3425879.3425881"},{"key":"e_1_3_2_2_30_1","first-page":"63","article-title":"Storage technique for real-time streaming of layered video","volume":"15","author":"Kang Sooyong","year":"2009","unstructured":"Sooyong Kang , Sungwoo Hong , and Youjip Won . 2009 . Storage technique for real-time streaming of layered video . MMSys , Vol. 15 , 2 (2009), 63 -- 81 . Sooyong Kang, Sungwoo Hong, and Youjip Won. 2009. Storage technique for real-time streaming of layered video. MMSys, Vol. 15, 2 (2009), 63--81.","journal-title":"MMSys"},{"key":"e_1_3_2_2_31_1","volume-title":"Joel Coburn, Martha A. Kim, Parthasarathy Ranganathan, Daniel Stodolsky, and Mark Wachsler.","author":"Lottarini Andrea","year":"2018","unstructured":"Andrea Lottarini , Alex Ram'i rez , Joel Coburn, Martha A. Kim, Parthasarathy Ranganathan, Daniel Stodolsky, and Mark Wachsler. 2018 . vbench: Benchmarking Video Transcoding in the Cloud. In ASPLOS. 797--809. Andrea Lottarini, Alex Ram'i rez, Joel Coburn, Martha A. Kim, Parthasarathy Ranganathan, Daniel Stodolsky, and Mark Wachsler. 2018. vbench: Benchmarking Video Transcoding in the Cloud. In ASPLOS. 797--809."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"David G. Lowe. 1999. Object Recognition from Local Scale-Invariant Features. In ICCV. 1150--1157.  David G. Lowe. 1999. Object Recognition from Local Scale-Invariant Features. In ICCV. 1150--1157.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2987550.2987564"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Yao Lu Aakanksha Chowdhery Srikanth Kandula and Surajit Chaudhuri. 2018. Accelerating Machine Learning Inference with Probabilistic Predicates. In SIGMOD. 1493--1508.  Yao Lu Aakanksha Chowdhery Srikanth Kandula and Surajit Chaudhuri. 2018. Accelerating Machine Learning Inference with Probabilistic Predicates. In SIGMOD. 1493--1508.","DOI":"10.1145\/3183713.3183751"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364916679498"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"crossref","unstructured":"Amrita Mazumdar Brandon Haynes Magda Balazinska Luis Ceze Alvin Cheung and Mark Oskin. 2019. Perceptual Compression for Video Storage and Processing Systems. In SoCC. 179--192.  Amrita Mazumdar Brandon Haynes Magda Balazinska Luis Ceze Alvin Cheung and Mark Oskin. 2019. Perceptual Compression for Video Storage and Processing Systems. In SoCC. 179--192.","DOI":"10.1145\/3357223.3362725"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Andrea Melloni S. Lameri Paolo Bestagini Marco Tagliasacchi and Stefano Tubaro. 2015. Near-duplicate detection and alignment for multi-view videos. In ICIP. 2444--2448.  Andrea Melloni S. Lameri Paolo Bestagini Marco Tagliasacchi and Stefano Tubaro. 2015. Near-duplicate detection and alignment for multi-view videos. In ICIP. 2444--2448.","DOI":"10.1109\/ICIP.2015.7351241"},{"volume-title":"NVIDIA CUDA Compute Unified Device Architecture Programming Guide","author":"NVIDIA Corporation","key":"e_1_3_2_2_39_1","unstructured":"NVIDIA Corporation . 2007. NVIDIA CUDA Compute Unified Device Architecture Programming Guide . NVIDIA Corporation . NVIDIA Corporation. 2007. NVIDIA CUDA Compute Unified Device Architecture Programming Guide .NVIDIA Corporation."},{"key":"e_1_3_2_2_40_1","unstructured":"OpenCV. 2018. Open Source Computer Vision Library. https:\/\/opencv.org.  OpenCV. 2018. Open Source Computer Vision Library. https:\/\/opencv.org."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Giuliano Pinheiro Marcos Cirne Paolo Bestagini Stefano Tubaro and Anderson Rocha. 2019. Detection and Synchronization of Video Sequences for Event Reconstruction. In ICIP. 4060--4064.  Giuliano Pinheiro Marcos Cirne Paolo Bestagini Stefano Tubaro and Anderson Rocha. 2019. Detection and Synchronization of Video Sequences for Event Reconstruction. In ICIP. 4060--4064.","DOI":"10.1109\/ICIP.2019.8803545"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201394"},{"key":"e_1_3_2_2_43_1","volume-title":"VDMS: An Efficient Big-Visual-Data Access for Machine Learning Workloads. CoRR","author":"Remis Luis","year":"2018","unstructured":"Luis Remis , Vishakha Gupta-Cledat , Christina R. Strong , and Ragaad Altarawneh . 2018 . VDMS: An Efficient Big-Visual-Data Access for Machine Learning Workloads. CoRR , Vol. abs\/ 1810 .11832 (2018). Luis Remis, Vishakha Gupta-Cledat, Christina R. Strong, and Ragaad Altarawneh. 2018. VDMS: An Efficient Big-Visual-Data Access for Machine Learning Workloads. CoRR, Vol. abs\/1810.11832 (2018)."},{"key":"e_1_3_2_2_44_1","unstructured":"SQLite Consortium. 2020. SQLite. https:\/\/www.sqlite.org.  SQLite Consortium. 2020. SQLite. https:\/\/www.sqlite.org."},{"key":"e_1_3_2_2_45_1","unstructured":"Michael Stonebraker Bharat Bhargava Michael Cafarella Zachary Collins Jenna McClellan Aaron Sipser Tao Sun Alina Nesen KMA Solaiman Ganapathy Mani etal 2020. Surveillance Video Querying with a Human-in-the-Loop. (2020).  Michael Stonebraker Bharat Bhargava Michael Cafarella Zachary Collins Jenna McClellan Aaron Sipser Tao Sun Alina Nesen KMA Solaiman Ganapathy Mani et al. 2020. Surveillance Video Querying with a Human-in-the-Loop. (2020)."},{"key":"e_1_3_2_2_46_1","first-page":"2453","article-title":"ODIN","volume":"13","author":"Suprem Abhijit","year":"2020","unstructured":"Abhijit Suprem , Joy Arulraj , Calton Pu , and Jo a o Eduardo Ferreira . 2020 . ODIN : Automated Drift Detection and Recovery in Video Analytics. VLDB , Vol. 13 , 11 (2020), 2453 -- 2465 . Abhijit Suprem, Joy Arulraj, Calton Pu, and Jo a o Eduardo Ferreira. 2020. ODIN: Automated Drift Detection and Recovery in Video Analytics. VLDB, Vol. 13, 11 (2020), 2453--2465.","journal-title":"Automated Drift Detection and Recovery in Video Analytics. VLDB"},{"key":"e_1_3_2_2_47_1","volume-title":"Louis-Philippe Morency, Ruslan Salakhutdinov, and Ali Farhadi.","author":"Hubert Tsai Yao-Hung","year":"2019","unstructured":"Yao-Hung Hubert Tsai , Santosh Kumar Divvala , Louis-Philippe Morency, Ruslan Salakhutdinov, and Ali Farhadi. 2019 . Video Relationship Reasoning Using Gated Spatio-Temporal Energy Graph. In CVPR. 10424--10433. Yao-Hung Hubert Tsai, Santosh Kumar Divvala, Louis-Philippe Morency, Ruslan Salakhutdinov, and Ali Farhadi. 2019. Video Relationship Reasoning Using Gated Spatio-Temporal Energy Graph. In CVPR. 10424--10433."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"crossref","unstructured":"Kaushik Veeraraghavan Jason Flinn Edmund B. Nightingale and Brian Noble. 2010. quFiles: The Right File at the Right Time. In FAST. 1--14.  Kaushik Veeraraghavan Jason Flinn Edmund B. Nightingale and Brian Noble. 2010. quFiles: The Right File at the Right Time. In FAST. 1--14.","DOI":"10.1145\/1837915.1837920"},{"key":"e_1_3_2_2_49_1","first-page":"1","article-title":"Deluceva","volume":"14","author":"Wang Jingjing","year":"2020","unstructured":"Jingjing Wang and Magdalena Balazinska . 2020 . Deluceva : Delta-Based Neural Network Inference for Fast Video Analytics. In SSDBM. 14 : 1 -- 14 :12. Jingjing Wang and Magdalena Balazinska. 2020. Deluceva: Delta-Based Neural Network Inference for Fast Video Analytics. In SSDBM. 14:1--14:12.","journal-title":"Delta-Based Neural Network Inference for Fast Video Analytics. In SSDBM."},{"key":"e_1_3_2_2_50_1","unstructured":"Susan Wojcicki. 2018. The Potential Unintended Consequences of Article 13. https:\/\/youtube-creators.googleblog.com\/2018\/11\/i-support-goals-of-article-13-i-also.html.  Susan Wojcicki. 2018. The Potential Unintended Consequences of Article 13. https:\/\/youtube-creators.googleblog.com\/2018\/11\/i-support-goals-of-article-13-i-also.html."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3320230"},{"key":"e_1_3_2_2_52_1","first-page":"1","article-title":"VStore","volume":"16","author":"Xu Tiantu","year":"2019","unstructured":"Tiantu Xu , Luis Materon Botelho , and Felix Xiaozhu Lin . 2019 . VStore : A Data Store for Analytics on Large Videos. In EuroSys. 16 : 1 -- 16 :17. Tiantu Xu, Luis Materon Botelho, and Felix Xiaozhu Lin. 2019. VStore: A Data Store for Analytics on Large Videos. In EuroSys. 16:1--16:17.","journal-title":"A Data Store for Analytics on Large Videos. In EuroSys."},{"key":"e_1_3_2_2_53_1","unstructured":"Billy Yates. 2018. Body Worn Cameras: Making Them Mandatory. (2018).  Billy Yates. 2018. Body Worn Cameras: Making Them Mandatory. (2018)."},{"key":"e_1_3_2_2_54_1","volume-title":"Matthai Philipose, Paramvir Bahl, and Michael J. Freedman.","author":"Zhang Haoyu","year":"2017","unstructured":"Haoyu Zhang , Ganesh Ananthanarayanan , Peter Bod'i k , Matthai Philipose, Paramvir Bahl, and Michael J. Freedman. 2017 . Live Video Analytics at Scale with Approximation and Delay-Tolerance. In NSDI. 377--392. Haoyu Zhang, Ganesh Ananthanarayanan, Peter Bod'i k, Matthai Philipose, Paramvir Bahl, and Michael J. Freedman. 2017. Live Video Analytics at Scale with Approximation and Delay-Tolerance. In NSDI. 377--392."},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/233269.233324"},{"key":"e_1_3_2_2_56_1","first-page":"477","article-title":"Panorama: A Data System for Unbounded Vocabulary Querying over Video","volume":"13","author":"Zhang Yuhao","year":"2019","unstructured":"Yuhao Zhang and Arun Kumar . 2019 . Panorama: A Data System for Unbounded Vocabulary Querying over Video . VLDB , Vol. 13 , 4 (2019), 477 -- 491 . Yuhao Zhang and Arun Kumar. 2019. Panorama: A Data System for Unbounded Vocabulary Querying over Video. VLDB, Vol. 13, 4 (2019), 477--491.","journal-title":"VLDB"}],"event":{"name":"SIGMOD\/PODS '21: International Conference on Management of Data","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"],"location":"Virtual Event China","acronym":"SIGMOD\/PODS '21"},"container-title":["Proceedings of the 2021 International Conference on Management of Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3448016.3459242","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3448016.3459242","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3448016.3459242","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:25:04Z","timestamp":1750195504000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3448016.3459242"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,9]]},"references-count":56,"alternative-id":["10.1145\/3448016.3459242","10.1145\/3448016"],"URL":"https:\/\/doi.org\/10.1145\/3448016.3459242","relation":{},"subject":[],"published":{"date-parts":[[2021,6,9]]},"assertion":[{"value":"2021-06-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}