{"id":"https://openalex.org/W2097098223","doi":"https://doi.org/10.1145/1989323.1989364","title":"Finding semantics in time series","display_name":"Finding semantics in time series","publication_year":2011,"publication_date":"2011-06-12","ids":{"openalex":"https://openalex.org/W2097098223","doi":"https://doi.org/10.1145/1989323.1989364","mag":"2097098223"},"language":"en","primary_location":{"id":"doi:10.1145/1989323.1989364","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1989323.1989364","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2011 ACM SIGMOD International Conference on Management of data","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100396080","display_name":"Peng Wang","orcid":"https://orcid.org/0000-0002-8136-9621"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peng Wang","raw_affiliation_strings":["Fudan University, Shanghai, China","Fudan University Shanghai, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Fudan University Shanghai, China#TAB#","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063351917","display_name":"Haixun Wang","orcid":"https://orcid.org/0009-0007-0773-7004"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haixun Wang","raw_affiliation_strings":["Microsoft Research Asia, Beijing, China","Microsoft research Asia, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]},{"raw_affiliation_string":"Microsoft research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100392156","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0003-0264-788X"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["Fudan University, Shanghai, China","Fudan University Shanghai, China#TAB#"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Fudan University Shanghai, China#TAB#","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100396080"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":5.3032,"has_fulltext":false,"cited_by_count":78,"citation_normalized_percentile":{"value":0.96343288,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"385","last_page":"396"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.982200026512146,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6966984272003174},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6612361669540405},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.6302651762962341},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.4296181797981262},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.07639649510383606}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6966984272003174},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6612361669540405},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6302651762962341},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.4296181797981262},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.07639649510383606},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1989323.1989364","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1989323.1989364","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2011 ACM SIGMOD International Conference on Management of data","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.6499999761581421,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1570448133","https://openalex.org/W1973222433","https://openalex.org/W1994920552","https://openalex.org/W2009727399","https://openalex.org/W2020865809","https://openalex.org/W2023987238","https://openalex.org/W2024760831","https://openalex.org/W2026331881","https://openalex.org/W2033854332","https://openalex.org/W2042591571","https://openalex.org/W2065426594","https://openalex.org/W2066796814","https://openalex.org/W2070134328","https://openalex.org/W2086086639","https://openalex.org/W2096472608","https://openalex.org/W2097997754","https://openalex.org/W2098759488","https://openalex.org/W2101005720","https://openalex.org/W2107633943","https://openalex.org/W2111263072","https://openalex.org/W2121353572","https://openalex.org/W2125693462","https://openalex.org/W2126455177","https://openalex.org/W2128061541","https://openalex.org/W2129981753","https://openalex.org/W2157868174","https://openalex.org/W2164274563","https://openalex.org/W2168781105","https://openalex.org/W2313953460","https://openalex.org/W2798058877","https://openalex.org/W2966207845"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2382290278","https://openalex.org/W2350741829","https://openalex.org/W2130043461","https://openalex.org/W2530322880","https://openalex.org/W2788308474"],"abstract_inverted_index":{"In":[0,96,179],"order":[1],"to":[2,31,40,72,84,90,138,172,187,197,209],"understand":[3],"a":[4,19,45,49,92,112,144,155,199],"complex":[5],"system,":[6],"we":[7,17,36,47,99,121,136,182,204],"analyze":[8],"its":[9,12,116],"output":[10],"or":[11,53],"log":[13],"data.":[14,107,166,178],"For":[15],"example,":[16],"track":[18],"system's":[20,93],"resource":[21],"consumption":[22],"(CPU,":[23],"memory,":[24],"message":[25],"queues":[26],"of":[27,44,115,131,158,225],"different":[28],"types,":[29],"etc)":[30],"help":[32],"avert":[33],"system":[34,160],"failures;":[35],"examine":[37],"economic":[38],"indicators":[39],"assess":[41],"the":[42,86,132,159,163,177,184,212,221,226],"severity":[43],"recession;":[46],"monitor":[48],"patient's":[50],"heart":[51],"rate":[52],"EEG":[54],"for":[55],"disease":[56],"diagnosis.":[57],"Time":[58],"series":[59,77,88,106,141,165,190],"data":[60,89,117,142],"is":[61],"involved":[62],"in":[63],"many":[64],"such":[65],"applications.":[66],"Much":[67],"work":[68],"has":[69,82],"been":[70],"devoted":[71],"pattern":[73],"discovery":[74],"from":[75,104,176],"time":[76,87,105,140,164,189],"data,":[78],"but":[79],"not":[80],"much":[81],"attempted":[83],"use":[85,183],"unveil":[91],"internal":[94],"dynamics.":[95],"this":[97],"paper,":[98],"go":[100],"beyond":[101],"learning":[102],"patterns":[103,123],"We":[108,167],"focus":[109],"on":[110,217],"obtaining":[111],"better":[113],"understanding":[114],"generating":[118],"mechanism,":[119],"and":[120,124,192,223],"regard":[122],"their":[125],"temporal":[126],"relations":[127],"as":[128],"organic":[129],"components":[130],"hidden":[133,147],"mechanism.":[134],"Specifically,":[135],"propose":[137,168,205],"model":[139,149],"using":[143],"novel":[145],"pattern-based":[146],"Markov":[148],"(pHMM),":[150],"which":[151,194],"aims":[152],"at":[153],"revealing":[154],"global":[156],"picture":[157],"that":[161],"generates":[162],"an":[169],"iterative":[170],"approach":[171],"refine":[173],"pHMMs":[174],"learned":[175],"each":[180],"iteration,":[181],"current":[185],"pHMM":[186],"guide":[188],"segmentation":[191],"clustering,":[193],"enables":[195],"us":[196],"learn":[198],"more":[200],"accurate":[201],"pHMM.":[202],"Furthermore,":[203],"three":[206],"pruning":[207],"strategies":[208],"speed":[210],"up":[211],"refinement":[213],"process.":[214],"Empirical":[215],"results":[216],"real":[218],"datasets":[219],"demonstrate":[220],"feasibility":[222],"effectiveness":[224],"proposed":[227],"approach.":[228]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":9},{"year":2017,"cited_by_count":11},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":4}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}