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Given a sequence of incoming queries over time, we study the problem of sequential decision making on when to acquire data and when to use existing versions to answer each query. We propose two approaches to solve this problem using reinforcement learning and tailored locality-sensitive hashing. A systematic empirical study using two real-world datasets shows that our approaches are effective and efficient.<\/jats:p>","DOI":"10.14778\/3021924.3021942","type":"journal-article","created":{"date-parts":[[2017,1,24]],"date-time":"2017-01-24T15:29:41Z","timestamp":1485271781000},"page":"277-288","source":"Crossref","is-referenced-by-count":3,"title":["Stochastic data acquisition for answering queries as time goes by"],"prefix":"10.14778","volume":"10","author":[{"given":"Zheng","family":"Li","sequence":"first","affiliation":[{"name":"University of Massachusetts, Lowell"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingjian","family":"Ge","sequence":"additional","affiliation":[{"name":"University of Massachusetts, Lowell"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2016,11]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Aggdata. http:\/\/www.aggdata.com\/.  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