# VStore: A Data Store for Analytics on Large Videos

arXiv: https://arxiv.org/abs/1810.01794 · HTML: https://mlsanity.com/paper/1810.01794/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2018.

## Built on by (most cited first, 9 of 9)

- [Boggart: Towards General-Purpose Acceleration of Retrospective Video Analytics](https://mlsanity.com/paper/2106.15315/) (2021)
- [AccMPEG: Optimizing Video Encoding for Video Analytics](https://mlsanity.com/paper/2204.12534/) (2022)
- [Extract-Transform-Load for Video Streams](https://mlsanity.com/paper/2310.04830/) (2023)
- [CoVA: Exploiting Compressed-Domain Analysis to Accelerate Video Analytics](https://mlsanity.com/paper/2207.00588/) (2022)
- [D'ej`a Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse](https://mlsanity.com/paper/2506.14107/) (2025)
- [OneAdapt: Fast Configuration Adaptation for Video Analytics Applications via Backpropagation](https://mlsanity.com/paper/2310.02422/) (2023)
- [A Survey of Performance Optimization in Neural Network-Based Video Analytics Systems](https://mlsanity.com/paper/2105.14195/) (2021)
- [Towards Causal Physical Error Discovery in Video Analytics Systems](https://mlsanity.com/paper/2405.17686/) (2024)
- [A Survey on Efficiency Optimization Techniques for DNN-based Video Analytics: Process Systems, Algorithms, and Applications](https://mlsanity.com/paper/2507.15628/) (2025)

## Abstract (arXiv)

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.