{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T02:53:56Z","timestamp":1776394436139,"version":"3.51.2"},"reference-count":18,"publisher":"Association for Computing Machinery (ACM)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2014,5]]},"abstract":"<jats:p>\n            Driven by the advance of positioning technology and the popularity of location-sharing services, semantic-enriched trajectory data have become unprecedentedly available. The sequential patterns hidden in such data, when properly defined and extracted, can greatly benefit tasks like targeted advertising and urban planning. Unfortunately, classic sequential pattern mining algorithms developed for transactional data cannot effectively mine patterns in semantic trajectories, mainly because the places in the continuous space cannot be regarded as independent \"items\". Instead, similar places need to be grouped to collaboratively form frequent sequential patterns. That said, it remains a challenging task to mine what we call\n            <jats:italic>fine-grained sequential patterns<\/jats:italic>\n            , which must satisfy spatial compactness, semantic consistency and temporal continuity simultaneously. We propose Splitter to effectively mine such fine-grained sequential patterns in two steps. In the first step, it retrieves a set of spatially coarse patterns, each attached with a set of trajectory snippets that precisely record the pattern's occurrences in the database. In the second step, Splitter breaks each coarse pattern into fine-grained ones in a top-down manner, by progressively detecting dense and compact clusters in a higher-dimensional space spanned by the snippets. Splitter uses an effective algorithm called weighted snippet shift to detect such clusters, and leverages a divide-and-conquer strategy to speed up the top-down pattern splitting process. Our experiments on both real and synthetic data sets demonstrate the effectiveness and efficiency of Splitter.\n          <\/jats:p>","DOI":"10.14778\/2732939.2732949","type":"journal-article","created":{"date-parts":[[2015,5,12]],"date-time":"2015-05-12T15:37:52Z","timestamp":1431445072000},"page":"769-780","source":"Crossref","is-referenced-by-count":112,"title":["Splitter"],"prefix":"10.14778","volume":"7","author":[{"given":"Chao","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Illinois, Urbana-Champaign, Urbana, IL"}]},{"given":"Jiawei","family":"Han","sequence":"additional","affiliation":[{"name":"University of Illinois, Urbana-Champaign, Urbana, IL"}]},{"given":"Lidan","family":"Shou","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]},{"given":"Jiajun","family":"Lu","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL"}]},{"given":"Thomas","family":"La Porta","sequence":"additional","affiliation":[{"name":"Penn State University, PA"}]}],"member":"320","published-online":{"date-parts":[[2014,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/645480.655281"},{"key":"e_1_2_1_2_1","volume-title":"Data Mining and Knowledge Discovery","author":"Alvares L. O.","year":"2007","unstructured":"L. O. Alvares , V. Bogorny , B. Kuijpers , B. Moelans , J. A. Fern , E. D. Macedo , and A. T. Palma . Towards semantic trajectory knowledge discovery . Data Mining and Knowledge Discovery , 2007 . L. O. Alvares, V. Bogorny, B. Kuijpers, B. Moelans, J. A. Fern, E. D. Macedo, and A. T. Palma. Towards semantic trajectory knowledge discovery. Data Mining and Knowledge Discovery, 2007."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.1000236"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/946247.946595"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281230"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/1453856.1453971"},{"key":"e_1_2_1_7_1","first-page":"132","volume-title":"GIScience","author":"Laube P.","year":"2002","unstructured":"P. Laube and S. Imfeld . Analyzing relative motion within groups of trackable moving point objects . In GIScience , pages 132 -- 144 , 2002 . P. Laube and S. Imfeld. Analyzing relative motion within groups of trackable moving point objects. In GIScience, pages 132--144, 2002."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920934"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463676.2465287"},{"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. Hsu . Prefixspan: Mining sequential patterns by prefix-projected growth . In ICDE , pages 215 -- 224 , 2001 . J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M. Hsu. Prefixspan: Mining sequential patterns by prefix-projected growth. In ICDE, pages 215--224, 2001."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/647227.719225"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2004.63"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1951365.1951398"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014091"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2093973.2093980"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007652502315"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2013.6544829"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526816"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/2732939.2732949","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:21:58Z","timestamp":1672222918000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/2732939.2732949"}},"subtitle":["mining fine-grained sequential patterns in semantic trajectories"],"short-title":[],"issued":{"date-parts":[[2014,5]]},"references-count":18,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2014,5]]}},"alternative-id":["10.14778\/2732939.2732949"],"URL":"https:\/\/doi.org\/10.14778\/2732939.2732949","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2014,5]]}}}