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We present a new method to process one of the most fundamental analytical primitives, quantile queries, on the union of historical and streaming data. Our method combines an index on historical data with a memory-efficient sketch on streaming data to answer quantile queries with accuracy-resource tradeoffs that are significantly better than current solutions that are based solely on disk-resident indexes or solely on streaming algorithms.<\/jats:p>","DOI":"10.14778\/3025111.3025124","type":"journal-article","created":{"date-parts":[[2017,1,24]],"date-time":"2017-01-24T15:29:41Z","timestamp":1485271781000},"page":"433-444","source":"Crossref","is-referenced-by-count":4,"title":["Estimating quantiles from the union of historical and streaming data"],"prefix":"10.14778","volume":"10","author":[{"given":"Sneha Aman","family":"Singh","sequence":"first","affiliation":[{"name":"Iowa State University"}]},{"given":"Divesh","family":"Srivastava","sequence":"additional","affiliation":[{"name":"AT&amp;T Labs --- Research"}]},{"given":"Srikanta","family":"Tirthapura","sequence":"additional","affiliation":[{"name":"Iowa State University"}]}],"member":"320","published-online":{"date-parts":[[2016,11]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"277","volume-title":"CIDR","author":"Abadi D. 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