VStore: A Video Store for AI
VStore is a data store for supporting fast, resource-efficient analytics over large archival videos. Catering to analytics, VStore manages the whole life cycle of videos – ingestion, storage, retrieval, and consumption. See the figure below.
Why we need another video store, given that we already have a lot, e.g. those for YouTube and Netflix? Yet, these existing stores are for human consumers. They are not for algorithmic consumers (AI), which watch videos in a way very different from humans.
VStore’s central design concern: to configure video formats to respect AI’s accuracy while maximizing resource efficiency. It is difficult – the configuration space is enormous!
VStore therefore builds on 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 automatically derives an optimal set of video formats, optimizing for different resources in a progressive manner. See the figure above.
As a query comes in, VStore selects video formats catering to the executed operators and the target accuracy. It streams video data from disks through decoder to operators.
On our commodity Xeon server, it runs queries as fast as 362x of video realtime.
Read our paper for more details.
[EuroSys’19] Tiantu Xu, Luis Materon Botelho, and Felix Xiaozhu Lin. VStore: A Data Store for Analytics on Large Videos. In Proceedings of the Fourteenth EuroSys Conference 2019 (EuroSys’19). ACM, New York, NY, USA, Article 16, 17 pages. DOI: https://doi.org/10.1145/3302424.3303971
- Tiantu Xu (lead), Purdue ECE
- Luis Materon Botelho, Purdue ECE
- Felix Xiaozhu Lin, Purdue ECE
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