{"id":"https://openalex.org/W2105709740","doi":"https://doi.org/10.1145/1066157.1066235","title":"Subsequence matching on structured time series data","display_name":"Subsequence matching on structured time series data","publication_year":2005,"publication_date":"2005-06-14","ids":{"openalex":"https://openalex.org/W2105709740","doi":"https://doi.org/10.1145/1066157.1066235","mag":"2105709740"},"language":"en","primary_location":{"id":"doi:10.1145/1066157.1066235","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1066157.1066235","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2005 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/A5011747432","display_name":"Huanmei Wu","orcid":"https://orcid.org/0000-0003-0346-6044"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Huanmei Wu","raw_affiliation_strings":["Northeastern University","northeastern Univ"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]},{"raw_affiliation_string":"northeastern Univ","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053141494","display_name":"Betty Salzberg","orcid":null},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Betty Salzberg","raw_affiliation_strings":["Northeastern University","northeastern Univ"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]},{"raw_affiliation_string":"northeastern Univ","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065983899","display_name":"G Sharp","orcid":"https://orcid.org/0000-0001-8575-9611"},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gregory C Sharp","raw_affiliation_strings":["Harvard Medical School"],"affiliations":[{"raw_affiliation_string":"Harvard Medical School","institution_ids":["https://openalex.org/I136199984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018120191","display_name":"Steve Jiang","orcid":"https://orcid.org/0000-0002-3083-6752"},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Steve B Jiang","raw_affiliation_strings":["Harvard Medical School"],"affiliations":[{"raw_affiliation_string":"Harvard Medical School","institution_ids":["https://openalex.org/I136199984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020774876","display_name":"Hiroki Shirato","orcid":"https://orcid.org/0000-0003-3699-6439"},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroki Shirato","raw_affiliation_strings":["Hokkaido University"],"affiliations":[{"raw_affiliation_string":"Hokkaido University","institution_ids":["https://openalex.org/I205349734"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061128237","display_name":"David Kaeli","orcid":"https://orcid.org/0000-0002-5692-0151"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"David Kaeli","raw_affiliation_strings":["Northeastern University","northeastern Univ"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]},{"raw_affiliation_string":"northeastern Univ","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5011747432"],"corresponding_institution_ids":["https://openalex.org/I87182695"],"apc_list":null,"apc_paid":null,"fwci":6.8095,"has_fulltext":false,"cited_by_count":67,"citation_normalized_percentile":{"value":0.97350893,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"682","last_page":"693"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998000264167786,"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":0.9998000264167786,"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/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.9483000040054321,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9129999876022339,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/subsequence","display_name":"Subsequence","score":0.8975471258163452},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6629976034164429},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6265544891357422},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.6142654418945312},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5318405628204346},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5149127244949341},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4849865436553955},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4181564152240753},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.41460371017456055},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38383638858795166},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.363547146320343},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28816986083984375},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07652220129966736}],"concepts":[{"id":"https://openalex.org/C137877099","wikidata":"https://www.wikidata.org/wiki/Q1332977","display_name":"Subsequence","level":3,"score":0.8975471258163452},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6629976034164429},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6265544891357422},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.6142654418945312},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5318405628204346},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5149127244949341},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4849865436553955},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4181564152240753},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.41460371017456055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38383638858795166},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.363547146320343},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28816986083984375},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07652220129966736},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/1066157.1066235","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1066157.1066235","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2005 ACM SIGMOD international conference on Management of data","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.111.1521","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.111.1521","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.informatik.uni-freiburg.de/~ml/teaching/ss07/MLDMApps/papers/medicine/p682-wu.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.79.1643","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.1643","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ece.neu.edu/info/architecture/publications/SIGMOD05.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.5199999809265137,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1499049447","https://openalex.org/W1507201003","https://openalex.org/W1514224985","https://openalex.org/W1826290430","https://openalex.org/W1864972570","https://openalex.org/W1949687736","https://openalex.org/W1986188966","https://openalex.org/W2004110412","https://openalex.org/W2004154913","https://openalex.org/W2009316899","https://openalex.org/W2010142086","https://openalex.org/W2022655240","https://openalex.org/W2039843363","https://openalex.org/W2042591571","https://openalex.org/W2053062040","https://openalex.org/W2066796814","https://openalex.org/W2067288508","https://openalex.org/W2081189989","https://openalex.org/W2101005720","https://openalex.org/W2106163100","https://openalex.org/W2109498222","https://openalex.org/W2119594372","https://openalex.org/W2128061541","https://openalex.org/W2132520482","https://openalex.org/W2132851185","https://openalex.org/W2148034761","https://openalex.org/W2148039410","https://openalex.org/W2163336863","https://openalex.org/W2166064220","https://openalex.org/W6661122342"],"related_works":["https://openalex.org/W2996768723","https://openalex.org/W4212895949","https://openalex.org/W4230102134","https://openalex.org/W2319693127","https://openalex.org/W308539617","https://openalex.org/W2072263576","https://openalex.org/W2474567666","https://openalex.org/W1940044583","https://openalex.org/W2806903871","https://openalex.org/W4320802053"],"abstract_inverted_index":{"Subsequence":[0],"matching":[1,59,181,238],"in":[2,12,47,57,73,103,119,214,258],"time":[3,23,54,105,264,275],"series":[4,24,55,265,276],"databases":[5],"is":[6,63,101,111,132,140,154,190,267],"a":[7,53,79,98,107,120,135,143,185,196],"useful":[8],"technique,":[9],"with":[10,273],"applications":[11],"pattern":[13],"matching,":[14],"prediction,":[15],"and":[16,33,85,110,116,139,212,222,234,242,247],"rule":[17],"discovery.":[18,249],"Internal":[19],"structure":[20,51],"within":[21],"the":[22,38,49,58,66,130,151,158,170,176,203,217],"data":[25,72,108],"can":[26,164,225],"be":[27,165,226],"used":[28,141,166,231],"to":[29,65,167,201,263],"improve":[30],"these":[31],"tasks,":[32],"provide":[34],"important":[35],"insight":[36],"into":[37],"problem":[39,67],"domain.":[40],"This":[41,61,192],"paper":[42],"introduces":[43],"our":[44,251,261],"research":[45],"effort":[46],"using":[48,91],"internal":[50],"of":[52,69,88,129,160,179,206],"directly":[56],"process.":[60],"idea":[62],"applied":[64,243],"domain":[68],"respiratory":[70,89],"motion":[71,90,99,245],"cancer":[74],"radiation":[75,259],"treatment.":[76],"We":[77],"propose":[78],"comprehensive":[80],"solution":[81],"for":[82,114,122,145,232,244,256,270],"analysis,":[83],"clustering,":[84],"online":[86,235],"prediction":[87,246],"subsequence":[92,146,153,161,172,187,218],"similarity":[93,180,188,219,224],"matching.":[94,147],"In":[95],"this":[96],"system,":[97],"signal":[100,131],"captured":[102],"real":[104],"as":[106,142],"stream,":[109,209],"analyzed":[112,241],"immediately":[113],"treatment":[115],"also":[117],"saved":[118],"database":[121],"future":[123],"study.":[124],"A":[125],"piecewise":[126],"linear":[127],"representation":[128],"generated":[133],"from":[134],"finite":[136],"state":[137],"model,":[138],"query":[144,152,171],"To":[148,174],"ensure":[149],"that":[150],"representative,":[155],"we":[156],"introduce":[157],"concept":[159],"stability,":[162],"which":[163,228],"dynamically":[168],"adjust":[169],"length.":[173],"satisfy":[175],"special":[177],"needs":[178],"over":[182],"breathing":[183],"patterns,":[184],"new":[186,193],"measure":[189,194],"introduced.":[191],"uses":[195],"weighted":[197],"L1":[198],"distance":[199],"function":[200],"capture":[202],"relative":[204],"importance":[205],"each":[207],"source":[208],"amplitude,":[210],"frequency,":[211],"proximity":[213],"time.":[215],"From":[216],"measure,":[220],"stream":[221],"patient":[223],"defined,":[227],"are":[229,240],"then":[230],"offline":[233],"applications.":[236],"The":[237],"results":[239],"correlation":[248],"While":[250],"system":[252],"has":[253],"been":[254],"customized":[255],"use":[257],"therapy,":[260],"approach":[262],"modeling":[266],"general":[268],"enough":[269],"application":[271],"domains":[272],"structured":[274],"data.":[277]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":5},{"year":2012,"cited_by_count":9}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}