research-article

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VStore: A Data Store for Analytics on Large Videos

Authors:

Luis Materon Botelho,

Felix Xiaozhu LinAuthors Info & Claims

EuroSys '19: Proceedings of the Fourteenth EuroSys Conference 2019

Article No.: 16, Pages 1 - 17

https://doi.org/10.1145/3302424.3303971

Published: 25 March 2019 Publication History

Abstract

We present VStore, a data store for supporting fast, resource-efficient analytics over large archival videos. VStore manages video ingestion, storage, retrieval, and consumption. It controls video formats along the video data path. It is challenged by i) the huge combinatorial space of video format knobs; ii) the complex impacts of these knobs and their high profiling cost; iii) optimizing for multiple resource types. It explores an idea called backward derivation of configuration: in the opposite direction along the video data path, VStore passes the video quantity and quality expected by analytics backward to retrieval, to storage, and to ingestion. In this process, VStore derives an optimal set of video formats, optimizing for different resources in a progressive manner.

VStore automatically derives large, complex configurations consisting of more than one hundred knobs over tens of video formats. In response to queries, VStore selects video formats catering to the executed operators and the target accuracy. It streams video data from disks through decoder to operators. It runs queries as fast as 362x of video realtime.

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Cited By

Kossoski CSimão JLopes H(2024)Modeling and Performance Analysis of a Notification-Based Method for Processing Video Queries on the FlyApplied Sciences10.3390/app1409356614:9(3566)Online publication date: 24-Apr-2024
https://doi.org/10.3390/app14093566
Winecki DNandi A(2024)V2V: Efficiently Synthesizing Video Results for Video Queries2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00449(5614-5621)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00449
Zhong TZhang ZLu GYuan YWang YWang G(2023)TVM: A Tile-based Video Management FrameworkProceedings of the VLDB Endowment10.14778/3636218.363622417:4(671-684)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.14778/3636218.3636224
Show More Cited By

Index Terms

VStore: A Data Store for Analytics on Large Videos
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision tasks
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics

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EuroSys '19: Proceedings of the Fourteenth EuroSys Conference 2019

March 2019

714 pages

ISBN:9781450362818

DOI:10.1145/3302424

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Published: 25 March 2019

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March 25 - 28, 2019

Dresden, Germany

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https://doi.org/10.3390/app14093566
Winecki DNandi A(2024)V2V: Efficiently Synthesizing Video Results for Video Queries2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00449(5614-5621)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00449
Zhong TZhang ZLu GYuan YWang YWang G(2023)TVM: A Tile-based Video Management FrameworkProceedings of the VLDB Endowment10.14778/3636218.363622417:4(671-684)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.14778/3636218.3636224
Kossmann FWu ZLai ETatbul NCao LKraska TMadden S(2023)Extract-Transform-Load for Video StreamsProceedings of the VLDB Endowment10.14778/3598581.359860016:9(2302-2315)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.14778/3598581.3598600
Du KLiu YHao YZhang QWang HHuang YAnanthanarayanan GJiang J(2023)OneAdaptProceedings of the 2023 ACM Symposium on Cloud Computing10.1145/3620678.3624653(158-176)Online publication date: 30-Oct-2023
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Xu ZKakkar GArulraj JRamachandran UIves ZBonifati AEl Abbadi A(2022)EVA: A Symbolic Approach to Accelerating Exploratory Video Analytics with Materialized ViewsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526142(602-616)Online publication date: 10-Jun-2022
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