{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T19:02:15Z","timestamp":1768071735343,"version":"3.49.0"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,11]]},"abstract":"<jats:p>Event streams generated by smart devices common in modern Internet of Things applications must be continuously mined to monitor the behavior of the underlying system. In this work, we propose a stream pattern mining system for supporting online IoT applications. First, to solve the pattern explosion problem of existing stream pattern mining strategies, we now design pattern semantics that continuously produce a compact set of patterns that max-imumly compresses the dynamic data streams, called MDL-based Representative Patterns (MRP). We then design a one-pass SWIFT approach that continuously mines the up-to-date MRP pattern set for each stream window upon the arrival or expiration of individual events. We show that SWIFT is guaranteed to select the update operation for each individual incoming event that leads to the most compact encoding of the sequence in the current window. We further enhance SWIFT to support batch updates, called B-SWIFT. B-SWIFT adopts a<jats:italic>lazy update<\/jats:italic>strategy that guarantees that only the minimal number of operations are conducted to process an incoming event batch for MRP pattern mining. Evaluation by our industry lighting lab collaborator demonstrates that SWIFT successfully solves their use cases and finds more representative patterns than the alternative approaches adapting the state-of-the-art static representative pattern mining methods. Our experimental study confirms that SWIFT outperforms the best existing method up to 50% in the compactness of produced pattern encodings, while providing a 4 orders of magnitude speedup.<\/jats:p>","DOI":"10.14778\/3291264.3291271","type":"journal-article","created":{"date-parts":[[2019,2,4]],"date-time":"2019-02-04T13:13:43Z","timestamp":1549286023000},"page":"265-277","source":"Crossref","is-referenced-by-count":8,"title":["SWIFT"],"prefix":"10.14778","volume":"12","author":[{"given":"Yizhou","family":"Yan","sequence":"first","affiliation":[{"name":"Worcester Polytechnic Institute Worcester"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Cao","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"Madden","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elke A.","family":"Rundensteiner","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute Worcester"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-07821-2","volume-title":"Frequent Pattern Mining","author":"Aggarwal C. C.","year":"2014","unstructured":"C. C. Aggarwal and J. Han , editors . Frequent Pattern Mining . Springer , 2014 . C. C. Aggarwal and J. Han, editors. Frequent Pattern Mining. Springer, 2014."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/645480.655281"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/18.720554"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2757217"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.36"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014114"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-06483-3_8"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-53914-5_15"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/3279541"},{"key":"e_1_2_1_10_1","first-page":"50","volume-title":"PAKDD","author":"Gomariz A.","year":"2013","unstructured":"A. Gomariz , M. Campos , R. Marin , and B. Goethals . Clasp: An efficient algorithm for mining frequent closed sequences . In PAKDD , pages 50 -- 61 . Springer , 2013 . A. Gomariz, M. Campos, R. Marin, and B. Goethals. Clasp: An efficient algorithm for mining frequent closed sequences. In PAKDD, pages 50--61. Springer, 2013."},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4643.001.0001","volume-title":"The minimum description length principle","author":"Gr\u00fcnwald P. D.","year":"2007","unstructured":"P. D. Gr\u00fcnwald . The minimum description length principle . MIT press , 2007 . P. D. Gr\u00fcnwald. The minimum description length principle. MIT press, 2007."},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/1114.001.0001","volume-title":"Advances in minimum description length: Theory and applications","author":"Gr\u00fcnwald P. D.","year":"2005","unstructured":"P. D. Gr\u00fcnwald , I. J. Myung , and M. A. Pitt . Advances in minimum description length: Theory and applications . MIT press , 2005 . P. D. Gr\u00fcnwald, I. J. Myung, and M. A. Pitt. Advances in minimum description length: Theory and applications. 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