{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T01:41:53Z","timestamp":1707788513314},"reference-count":30,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2012,7]]},"abstract":"<jats:p>In the last years there has been a considerable increase in the availability of continuous sensor measurements in a wide range of application domains, such as Location-Based Services (LBS), medical monitoring systems, manufacturing plants and engineering facilities to ensure efficiency, product quality and safety, hydrologic and geologic observing systems, pollution management, and others.<\/jats:p>\n          <jats:p>Due to the inherent imprecision of sensor observations, many investigations have recently turned into querying, mining and storing uncertain data. Uncertainty can also be due to data aggregation, privacy-preserving transforms, and error-prone mining algorithms.<\/jats:p>\n          <jats:p>In this study, we survey the techniques that have been proposed specifically for modeling and processing uncertain time series, an important model for temporal data. We provide an analytical evaluation of the alternatives that have been proposed in the literature, highlighting the advantages and disadvantages of each approach, and further compare these alternatives with two additional techniques that were carefully studied before. We conduct an extensive experimental evaluation with 17 real datasets, and discuss some surprising results, which suggest that a fruitful research direction is to take into account the temporal correlations in the time series. Based on our evaluations, we also provide guidelines useful for the practitioners in the field.<\/jats:p>","DOI":"10.14778\/2350229.2350278","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"1662-1673","source":"Crossref","is-referenced-by-count":30,"title":["Uncertain time-series similarity"],"prefix":"10.14778","volume":"5","author":[{"given":"Michele","family":"Dallachiesa","sequence":"first","affiliation":[{"name":"University of Trento"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Besmira","family":"Nushi","sequence":"additional","affiliation":[{"name":"University of Trento"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katsiaryna","family":"Mirylenka","sequence":"additional","affiliation":[{"name":"University of Trento"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Themis","family":"Palpanas","sequence":"additional","affiliation":[{"name":"University of Trento"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2012,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Keogh E. Xi X. Wei L. &amp; Ratanamahatana C. A. (2006). The UCR Time Series Classification\/Clustering Homepage: www.cs.ucr.edu\/~eamonn\/timeseries.data\/. Accessed on 17 May 2011.  Keogh E. Xi X. Wei L. &amp; Ratanamahatana C. A. (2006). The UCR Time Series Classification\/Clustering Homepage: www.cs.ucr.edu\/~eamonn\/timeseries.data\/. Accessed on 17 May 2011."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2008.4497447"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/1513922"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/645415.652239"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-02279-1_31"},{"key":"e_1_2_1_6_1","first-page":"359","volume-title":"KDD Workshop","author":"Berndt D. J.","year":"1994"},{"key":"e_1_2_1_7_1","first-page":"187","volume-title":"Wireless Sensor Networks for Adaptive Lighting in Road Tunnels. In International Conference on Information Processing in Sensor Networks (IPSN)","author":"Ceriotti M.","year":"2011"},{"key":"e_1_2_1_8_1","first-page":"126","volume-title":"ICDE","author":"Chan K.","year":"2002"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.14778\/1454159.1454226"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/191839.191925"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/1749603.1749605"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","unstructured":"M. Hamilton E. Graham P. Rundel M. Allen W. Kaiser M. Hansen and D. Estrin. New Approaches in Embedded Networked Sensing for Terrestrial Ecological Observatories. Environmental Engineering Science 24(2) 2007.  M. Hamilton E. Graham P. Rundel M. Allen W. Kaiser M. Hansen and D. Estrin. New Approaches in Embedded Networked Sensing for Terrestrial Ecological Observatories. 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Kollios and P. S. Yu. Time series compressibility and privacy. 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