{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T05:20:48Z","timestamp":1672291248300},"reference-count":18,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2008,8]]},"abstract":"<jats:p>\n            Sequence data is ubiquitous and finding frequent sequences in a large database is one of the most common problems when analyzing sequence data. Unfortunately many sources of sequence data, e.g., sensor networks for data-driven science, RFID-based supply chain monitoring, and computing system monitoring infrastructure, produce a challenging workload for sequence mining. It is common to find\n            <jats:italic>bursts<\/jats:italic>\n            of events of the same type. Such bursts result in high mining cost, because input sequences are longer. An even greater challenge is that these bursts tend to produce an overwhelming number of irrelevant repetitive sequence patterns with high support. Simply raising the support threshold is not a solution, because at some point interesting sequences will get eliminated. As an alternative we propose a novel transformation of the input sequences. We show that this transformation has several desirable properties. First, the transformed data can still be mined with existing sequence mining algorithms. Second, for a given support threshold the mining result can often be obtained much faster and it is usually much smaller and easier to interpret. Third, and most importantly, we show that the result sequences retain the important characteristics of the sequences that would have been found in the original (not transformed) data. We validate our technique with an experimental study using synthetic and real data.\n          <\/jats:p>","DOI":"10.14778\/1453856.1453870","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"78-89","source":"Crossref","is-referenced-by-count":2,"title":["Finding relevant patterns in bursty sequences"],"prefix":"10.14778","volume":"1","author":[{"given":"Alexander","family":"Lachmann","sequence":"first","affiliation":[{"name":"RWTH, Aachen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mirek","family":"Riedewald","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, New York"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2008,8]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"487","volume-title":"VLDB","author":"Agrawal R.","year":"1994","unstructured":"R. Agrawal and R. Srikant . Fast algorithms for mining association rules . In VLDB , pages 487 -- 499 , 1994 . R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In VLDB, pages 487--499, 1994."},{"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":"10.1145\/775047.775109"},{"key":"e_1_2_1_4_1","volume-title":"webSPADE: A parallel sequence mining algorithm to analyze the web log data","author":"Demiriz A.","year":"2002","unstructured":"A. Demiriz and M. J. Zaki . webSPADE: A parallel sequence mining algorithm to analyze the web log data , 2002 . A. Demiriz and M. J. Zaki. webSPADE: A parallel sequence mining algorithm to analyze the web log data, 2002."},{"key":"e_1_2_1_5_1","volume-title":"Sequence Data Mining (Advances in Database Systems)","author":"Dong G.","year":"2007","unstructured":"G. Dong and J. Pei . Sequence Data Mining (Advances in Database Systems) . Springer-Verlag New York , 2007 . G. Dong and J. Pei. Sequence Data Mining (Advances in Database Systems). Springer-Verlag New York, 2007."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1031453.1031477"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/347090.347167"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775061"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/645503.656256"},{"key":"e_1_2_1_10_1","first-page":"215","volume-title":"ICDE","author":"Pei J.","year":"2001","unstructured":"J. Pei , J. Han , B. Mortazavi-Asl , H. Pinto , Q. Chen , U. Dayal , and M.-C. Hsu . PrefixSpan : Mining sequential patterns efficiently by prefix projected pattern growth . In ICDE , pages 215 -- 224 , 2001 . J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. PrefixSpan: Mining sequential patterns efficiently by prefix projected pattern growth. In ICDE, pages 215--224, 2001."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/645337.650382"},{"key":"e_1_2_1_12_1","volume-title":"Mining Sequential Patterns from Large Data Sets","author":"Wang W.","year":"2005","unstructured":"W. Wang and J. Yang . Mining Sequential Patterns from Large Data Sets . Springer-Verlag New York , 2005 . W. Wang and J. Yang. Mining Sequential Patterns from Large Data Sets. Springer-Verlag New York, 2005."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972733.15"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2005.235"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/354756.354849"},{"key":"e_1_2_1_16_1","volume-title":"Spade: An efficient algorithm for mining frequent sequences. Machine Learning, 42(1--2):31--60","author":"Zaki M. J.","year":"2001","unstructured":"M. J. Zaki . Spade: An efficient algorithm for mining frequent sequences. Machine Learning, 42(1--2):31--60 , 2001 . M. J. Zaki. Spade: An efficient algorithm for mining frequent sequences. Machine Learning, 42(1--2):31--60, 2001."},{"key":"e_1_2_1_17_1","first-page":"369","volume-title":"ACM SIGKDD","author":"Zaki M. J.","year":"1998","unstructured":"M. J. Zaki , N. Lesh , and M. Ogihara . PlanMine: Sequence mining for plan failures . In ACM SIGKDD , pages 369 -- 373 , 1998 . M. J. Zaki, N. Lesh, and M. Ogihara. PlanMine: Sequence mining for plan failures. In ACM SIGKDD, pages 369--373, 1998."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2007.367916"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/1453856.1453870","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:06:02Z","timestamp":1672225562000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/1453856.1453870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008,8]]},"references-count":18,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2008,8]]}},"alternative-id":["10.14778\/1453856.1453870"],"URL":"https:\/\/doi.org\/10.14778\/1453856.1453870","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2008,8]]}}}