{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:30:12Z","timestamp":1750221012281,"version":"3.41.0"},"publisher-location":"New York, New York, USA","reference-count":14,"publisher":"ACM Press","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1145\/3331076.3331112","type":"proceedings-article","created":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T17:40:26Z","timestamp":1563558026000},"page":"1-10","source":"Crossref","is-referenced-by-count":1,"title":["Mining complex temporal dependencies from heterogeneous sensor data streams"],"prefix":"10.1145","author":[{"given":"Amine El","family":"Ouassouli","sequence":"first","affiliation":[{"name":"Univ Lyon, Villeurbanne, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lionel","family":"Robinault","sequence":"additional","affiliation":[{"name":"Foxstream, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasile-Marian","family":"Scuturici","sequence":"additional","affiliation":[{"name":"Univ Lyon, Villeurbanne, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","reference":[{"key":"key-10.1145\/3331076.3331112-1","unstructured":"J. F. Allen and P. J. Hayes. A common-sense theory of time. In IJCAI, pages 528--531. Morgan Kaufmann, 1985."},{"key":"key-10.1145\/3331076.3331112-2","doi-asserted-by":"crossref","unstructured":"G. Casas-Garriga. Discovering unbounded episodes in sequential data. In PKDD, volume 2838 of Lecture Notes in Computer Science, pages 83--94. Springer, 2003.","DOI":"10.1007\/978-3-540-39804-2_10"},{"key":"key-10.1145\/3331076.3331112-3","unstructured":"T. Guyet and R. Quiniou. Extracting temporal patterns from interval-based sequences. In IJCAI, pages 1306--1311. IJCAI\/AAAI, 2011."},{"key":"key-10.1145\/3331076.3331112-4","doi-asserted-by":"crossref","unstructured":"M. Hassani, Y. Lu, J. Wischnewsky, and T. Seidl. A geometric approach for mining sequential patterns in interval-based data streams. In FUZZ-IEEE, pages 2128--2135. IEEE, 2016.","DOI":"10.1109\/FUZZ-IEEE.2016.7737954"},{"key":"key-10.1145\/3331076.3331112-5","doi-asserted-by":"crossref","unstructured":"F. H&#246;ppner and S. Peter. Temporal interval pattern languages to characterize time flow. Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 4(3):196--212, 2014.","DOI":"10.1002\/widm.1122"},{"key":"key-10.1145\/3331076.3331112-6","unstructured":"T. Li and S. Ma. Mining temporal patterns without predefined time windows. In ICDM, pages 451--454. IEEE Computer Society, 2004."},{"key":"key-10.1145\/3331076.3331112-7","doi-asserted-by":"crossref","unstructured":"H. Mannila, H. Toivonen, and A. I. Verkamo. Discovery of frequent episodes in event sequences. Data Min. Knowl. Discov., 1(3):259--289, 1997.","DOI":"10.1023\/A:1009748302351"},{"key":"key-10.1145\/3331076.3331112-8","unstructured":"F. M&#246;rchen. Algorithms for time series knowledge mining. In KDD, pages 668--673. ACM, 2006."},{"key":"key-10.1145\/3331076.3331112-9","doi-asserted-by":"crossref","unstructured":"R. Moskovitch and Y. Shahar. Fast time intervals mining using the transitivity of temporal relations. Knowl. Inf. Syst., 42(1):21--48, 2015.","DOI":"10.1007\/s10115-013-0707-x"},{"key":"key-10.1145\/3331076.3331112-10","doi-asserted-by":"crossref","unstructured":"F. Nakagaito, T. Ozaki, and T. Ohkawa. Discovery of quantitative sequential patterns from event sequences. In ICDM Workshops, pages 31--36. IEEE Computer Society, 2009.","DOI":"10.1109\/ICDMW.2009.13"},{"key":"key-10.1145\/3331076.3331112-11","doi-asserted-by":"crossref","unstructured":"M. Plantevit, C. Robardet, and V. Scuturici. Graph dependency construction based on interval-event dependencies detection in data streams. Intell. Data Anal., 20(2):223--256, 2016.","DOI":"10.3233\/IDA-160803"},{"key":"key-10.1145\/3331076.3331112-12","doi-asserted-by":"crossref","unstructured":"G. Ruan, H. Zhang, and B. Plale. Parallel and quantitative sequential pattern mining for large-scale interval-based temporal data. In BigData, pages 32--39. IEEE Computer Society, 2014.","DOI":"10.1109\/BigData.2014.7004410"},{"key":"key-10.1145\/3331076.3331112-13","doi-asserted-by":"crossref","unstructured":"L. Tang, T. Li, and L. Shwartz. Discovering lag intervals for temporal dependencies. In KDD, pages 633--641. ACM, 2012.","DOI":"10.1145\/2339530.2339633"},{"key":"key-10.1145\/3331076.3331112-14","doi-asserted-by":"crossref","unstructured":"W. Wang, C. Zeng, and T. Li. Discovering multiple time lags of temporal dependencies from fluctuating events. In APWeb\/WAIM (2), volume 10988 of Lecture Notes in Computer Science, pages 121--137. Springer, 2018.","DOI":"10.1007\/978-3-319-96893-3_10"}],"event":{"name":"the 23rd International Database Applications & Engineering Symposium","start":{"date-parts":[[2019,6,10]]},"number":"23","location":"Athens, Greece","end":{"date-parts":[[2019,6,12]]},"acronym":"IDEAS '19"},"container-title":["Proceedings of the 23rd International Database Applications &amp; Engineering Symposium on   - IDEAS '19"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3331076.3331112","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=3331112&ftid=2073409&dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:06Z","timestamp":1750206366000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=3331076.3331112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":14,"URL":"https:\/\/doi.org\/10.1145\/3331076.3331112","relation":{},"subject":[],"published":{"date-parts":[[2019]]}}}