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This involves two steps: choosing a proper history length and choosing the number of data points to use within this history. Further, we use an I\/O conscious skip list data structure to provide samples of the original data set. Based on the statistics collected for a query workload, which we model as a probability mass function (PMF) over query intervals, we devise a randomized algorithm that selects a set of pre-built models (PM's) to construct, subject to some maintenance cost constraint when there are updates. Given this set of PM's, we discuss interesting query processing strategies for not only point queries, but also range, aggregation, and JOIN queries. We conduct a comprehensive empirical study on real world datasets to verify the effectiveness of our approaches and algorithms.<\/jats:p>","DOI":"10.14778\/1453856.1453962","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"984-995","source":"Crossref","is-referenced-by-count":14,"title":["A skip-list approach for efficiently processing forecasting queries"],"prefix":"10.14778","volume":"1","author":[{"given":"Tingjian","family":"Ge","sequence":"first","affiliation":[{"name":"Brown University"}]},{"given":"Stan","family":"Zdonik","sequence":"additional","affiliation":[{"name":"Brown University"}]}],"member":"320","published-online":{"date-parts":[[2008,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/11795490_28"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0927-5398(99)00013-4"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1011767.1011785"},{"key":"e_1_2_1_4_1","first-page":"384","volume-title":"Aspnes and Gauri Shah. 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