{"id":"https://openalex.org/W2623413278","doi":"https://doi.org/10.1145/3085504.3085515","title":"Data Series Similarity Using Correlation-Aware Measures","display_name":"Data Series Similarity Using Correlation-Aware Measures","publication_year":2017,"publication_date":"2017-06-05","ids":{"openalex":"https://openalex.org/W2623413278","doi":"https://doi.org/10.1145/3085504.3085515","mag":"2623413278"},"language":"en","primary_location":{"id":"doi:10.1145/3085504.3085515","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3085504.3085515","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th International Conference on Scientific and Statistical Database Management","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/A5082535778","display_name":"\u041a\u0430\u0446\u044f\u0440\u044b\u043d\u0430 \u041c\u0438\u0440\u044b\u043b\u0435\u043d\u043a\u0430","orcid":"https://orcid.org/0000-0002-1614-6835"},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]},{"id":"https://openalex.org/I4210126328","display_name":"IBM Research - Zurich","ror":"https://ror.org/02js37d36","country_code":"CH","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126328"]}],"countries":["CH","IT"],"is_corresponding":true,"raw_author_name":"Katsiaryna Mirylenka","raw_affiliation_strings":["IBM Research - Zurich, Zurich, Switzerland and University of Trento, Italy"],"affiliations":[{"raw_affiliation_string":"IBM Research - Zurich, Zurich, Switzerland and University of Trento, Italy","institution_ids":["https://openalex.org/I4210126328","https://openalex.org/I193223587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008462261","display_name":"Michele Dallachiesa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michele Dallachiesa","raw_affiliation_strings":["Skysense, Trento, Italy"],"affiliations":[{"raw_affiliation_string":"Skysense, Trento, Italy","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053726723","display_name":"Themis Palpanas","orcid":"https://orcid.org/0000-0002-8031-0265"},"institutions":[{"id":"https://openalex.org/I110736937","display_name":"D\u00e9l\u00e9gation Paris 5","ror":"https://ror.org/02e0y6e06","country_code":"FR","type":"government","lineage":["https://openalex.org/I110736937","https://openalex.org/I154526488"]},{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Themis Palpanas","raw_affiliation_strings":["Paris Descartes University, Paris, France"],"affiliations":[{"raw_affiliation_string":"Paris Descartes University, Paris, France","institution_ids":["https://openalex.org/I110736937","https://openalex.org/I204730241"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5082535778"],"corresponding_institution_ids":["https://openalex.org/I193223587","https://openalex.org/I4210126328"],"apc_list":null,"apc_paid":null,"fwci":2.2191,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.89105085,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9670000076293945,"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"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9596999883651733,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7125579118728638},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6682243347167969},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6528890132904053},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6334186792373657},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6146332025527954},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5157856941223145},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4820595681667328},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.45506948232650757},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4432172477245331},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.43942713737487793},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4340601861476898},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4305015504360199},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40636566281318665},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.272702693939209},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17224007844924927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7125579118728638},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6682243347167969},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6528890132904053},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6334186792373657},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6146332025527954},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5157856941223145},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4820595681667328},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.45506948232650757},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4432172477245331},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.43942713737487793},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4340601861476898},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4305015504360199},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40636566281318665},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.272702693939209},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17224007844924927},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3085504.3085515","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3085504.3085515","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th International Conference on Scientific and Statistical Database Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W116902681","https://openalex.org/W145669980","https://openalex.org/W1495933060","https://openalex.org/W1499049447","https://openalex.org/W1514224985","https://openalex.org/W1963737619","https://openalex.org/W1965741190","https://openalex.org/W1976338956","https://openalex.org/W1990591351","https://openalex.org/W2009662548","https://openalex.org/W2028020839","https://openalex.org/W2029438113","https://openalex.org/W2039260438","https://openalex.org/W2049145592","https://openalex.org/W2076249942","https://openalex.org/W2077720176","https://openalex.org/W2095223629","https://openalex.org/W2095409369","https://openalex.org/W2097747115","https://openalex.org/W2098759488","https://openalex.org/W2099302229","https://openalex.org/W2099973337","https://openalex.org/W2100718094","https://openalex.org/W2106154221","https://openalex.org/W2107105977","https://openalex.org/W2111483789","https://openalex.org/W2117157603","https://openalex.org/W2118371392","https://openalex.org/W2122233046","https://openalex.org/W2124279406","https://openalex.org/W2133246278","https://openalex.org/W2138011093","https://openalex.org/W2141536962","https://openalex.org/W2150563191","https://openalex.org/W2161621125","https://openalex.org/W2162756694","https://openalex.org/W2166299063","https://openalex.org/W2215890149","https://openalex.org/W2266934531","https://openalex.org/W2292011317","https://openalex.org/W2296028074","https://openalex.org/W2405230789","https://openalex.org/W2513211580","https://openalex.org/W2604912483","https://openalex.org/W4372267129"],"related_works":["https://openalex.org/W2943623134","https://openalex.org/W2494523064","https://openalex.org/W2002739602","https://openalex.org/W1968042686","https://openalex.org/W2897881820","https://openalex.org/W2345647014","https://openalex.org/W2201192772","https://openalex.org/W2161960196","https://openalex.org/W3136891595","https://openalex.org/W1964819397"],"abstract_inverted_index":{"The":[0],"increased":[1],"availability":[2],"of":[3,6,33,50,53,60,72,104,111,150],"unprecedented":[4],"amounts":[5],"sequential":[7],"data":[8,34,73,95],"(generated":[9],"by":[10,132],"Internet-of-Things,":[11],"as":[12,14],"well":[13],"scientific":[15],"applications)":[16],"has":[17],"led":[18],"in":[19,90,93,123,178],"the":[20,31,58,80,87,100,107,118,124,148,151,155,161,172],"past":[21],"few":[22],"years":[23],"to":[24,30,86,116,175],"a":[25,47,94],"renewed":[26],"interest":[27],"and":[28,37,44,102,134,145,159],"attention":[29],"field":[32],"series":[35,40,74],"processing":[36],"analysis.":[38],"Data":[39],"collections":[41],"are":[42,55,84],"processed":[43],"analyzed":[45],"using":[46,154],"large":[48],"variety":[49],"techniques,":[51],"most":[52],"which":[54,113,137],"based":[56],"on":[57],"computation":[59],"some":[61],"distance":[62,75,82,108],"function.":[63],"In":[64],"this":[65,69],"study,":[66],"we":[67,98,165],"revisit":[68],"basic":[70],"operation":[71],"calculation.":[76],"We":[77,126,143],"observe":[78],"that":[79],"popular":[81],"measures":[83,110,153,174],"oblivious":[85],"correlations":[88],"inherent":[89],"neighboring":[91,121],"values":[92,122],"series.":[96],"Therefore,":[97],"evaluate":[99],"plausibility":[101],"benefit":[103],"incorporating":[105],"into":[106],"function":[109],"correlation,":[112],"enable":[114],"us":[115],"capture":[117],"associations":[119],"among":[120],"sequence.":[125],"propose":[127,166],"four":[128],"such":[129],"measures,":[130],"inspired":[131],"statistical":[133],"probabilistic":[135],"approaches,":[136],"can":[138],"effectively":[139],"model":[140],"these":[141],"correlations.":[142],"analytically":[144],"experimentally":[146],"demonstrate":[147],"benefits":[149],"new":[152],"1NN":[156],"classification":[157],"task,":[158],"discuss":[160],"lessons":[162],"learned.":[163],"Finally,":[164],"future":[167],"research":[168],"directions":[169],"for":[170],"enabling":[171],"proposed":[173],"be":[176],"used":[177],"practice.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
