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We present VStore, a data store for supporting fast, resource-efficientanalytics over large archival videos. VStore manages video ingestion, storage,retrieval, and consumption. It controls video formats along the video datapath. It is challenged by i) the huge combinatorial space of video formatknobs; ii) the complex impacts of these knobs and their high profiling cost;iii) optimizing for multiple resource types. It explores an idea calledbackward derivation of configuration: in the opposite direction along the videodata path, VStore passes the video quantity and quality expected by analyticsbackward to retrieval, to storage, and to ingestion. In this process, VStorederives an optimal set of video formats, optimizing for different resources ina progressive manner. VStore automatically derives large, complexconfigurations consisting of more than one hundred knobs over tens of videoformats. In response to queries, VStore selects video formats catering to theexecuted operators and the target accuracy. It streams video data from disksthrough decoder to operators. It runs queries as fast as 362x of videorealtime.

VStore: A Data Store for Analytics on Large Videos