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Prior work uses approximate filtering to reduce the cost of video analytics, but does not handle two important classes of queries, aggregation and limit queries; moreover, these approaches still require complex code to deploy. To address the computational and usability challenges of querying video at scale, we introduce BlazeIt, a system that optimizes queries of spatiotemporal information of objects in video. BlazeIt accepts queries via FrameQL, a declarative extension of SQL for video analytics that enables video-specific query optimization. We introduce two new query optimization techniques in BlazeIt that are not supported by prior work. First, we develop methods of using NNs as control variates to quickly answer approximate aggregation queries with error bounds. Second, we present a novel search algorithm for cardinality-limited video queries. Through these these optimizations, BlazeIt can deliver up to 83X speedups over the recent literature on video processing.<\/jats:p>","DOI":"10.14778\/3372716.3372725","type":"journal-article","created":{"date-parts":[[2020,1,6]],"date-time":"2020-01-06T20:19:37Z","timestamp":1578341977000},"page":"533-546","source":"Crossref","is-referenced-by-count":82,"title":["BlazeIt"],"prefix":"10.14778","volume":"13","author":[{"given":"Daniel","family":"Kang","sequence":"first","affiliation":[{"name":"Stanford DAWN Project"}]},{"given":"Peter","family":"Bailis","sequence":"additional","affiliation":[{"name":"Stanford DAWN Project"}]},{"given":"Matei","family":"Zaharia","sequence":"additional","affiliation":[{"name":"Stanford DAWN Project"}]}],"member":"320","published-online":{"date-parts":[[2020,1,6]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Cornell lab bird cams. http:\/\/cams.allaboutbirds.org\/.  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