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We propose an algorithm that learns from the past behavior of ACEP runs, and makes decisions on what to process first in an online manner, so as to maximize the number of full matches found. In addition, we devise effective optimization techniques. Finally, we propose a mechanism that uses reinforcement learning to dynamically update the history structure without incurring much overhead. Put together, these techniques drastically improve the fraction of full matches found in resource constrained environments.<\/jats:p>","DOI":"10.14778\/3025111.3025121","type":"journal-article","created":{"date-parts":[[2017,1,24]],"date-time":"2017-01-24T15:29:41Z","timestamp":1485271781000},"page":"397-408","source":"Crossref","is-referenced-by-count":14,"title":["History is a mirror to the future"],"prefix":"10.14778","volume":"10","author":[{"given":"Zheng","family":"Li","sequence":"first","affiliation":[{"name":"Oracle Corporation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingjian","family":"Ge","sequence":"additional","affiliation":[{"name":"University of Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2016,11]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"https:\/\/dev.twitter.com\/streaming\/public.  https:\/\/dev.twitter.com\/streaming\/public."},{"key":"e_1_2_1_2_1","unstructured":"https:\/\/en.wikipedia.org\/wiki\/Most_common_words_in_English.  https:\/\/en.wikipedia.org\/wiki\/Most_common_words_in_English."},{"key":"e_1_2_1_3_1","unstructured":"http:\/\/www.cs.uml.edu\/~ge\/paper\/rc_acep_tech_report.pdf.  http:\/\/www.cs.uml.edu\/~ge\/paper\/rc_acep_tech_report.pdf."},{"key":"e_1_2_1_4_1","unstructured":"http:\/\/www.ee.ucr.edu\/~hamed\/PEVData.zip.  http:\/\/www.ee.ucr.edu\/~hamed\/PEVData.zip."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1376616.1376634"},{"key":"e_1_2_1_6_1","volume-title":"Principles, Techniques. 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Approximate matching of regular expressions. Bulletin of math. biology, 1989."},{"key":"e_1_2_1_17_1","volume-title":"Artificial intelligence: a modern approach","author":"Russell S.","year":"2010","unstructured":"S. Russell and P. Norvig . Artificial intelligence: a modern approach . 2010 . S. Russell and P. Norvig. Artificial intelligence: a modern approach. 2010."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1142473.1142520"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2011.67"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3025111.3025121","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:20:00Z","timestamp":1672222800000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3025111.3025121"}},"subtitle":["best-effort approximate complex event matching with insufficient resources"],"short-title":[],"issued":{"date-parts":[[2016,11]]},"references-count":19,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2016,11]]}},"alternative-id":["10.14778\/3025111.3025121"],"URL":"https:\/\/doi.org\/10.14778\/3025111.3025121","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2016,11]]}}}