{"id":"https://openalex.org/W2043935429","doi":"https://doi.org/10.1145/1451940.1451977","title":"Measuring text similarity with dynamic time warping","display_name":"Measuring text similarity with dynamic time warping","publication_year":2008,"publication_date":"2008-01-01","ids":{"openalex":"https://openalex.org/W2043935429","doi":"https://doi.org/10.1145/1451940.1451977","mag":"2043935429"},"language":"en","primary_location":{"id":"doi:10.1145/1451940.1451977","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1451940.1451977","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2008 international symposium on Database engineering & applications - IDEAS '08","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/A5013126220","display_name":"Michael Matuschek","orcid":null},"institutions":[{"id":"https://openalex.org/I44260953","display_name":"Heinrich Heine University D\u00fcsseldorf","ror":"https://ror.org/024z2rq82","country_code":"DE","type":"education","lineage":["https://openalex.org/I44260953"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Michael Matuschek","raw_affiliation_strings":["Heinrich-Heine-Universitat, dusseldorf, Germany"],"affiliations":[{"raw_affiliation_string":"Heinrich-Heine-Universitat, dusseldorf, Germany","institution_ids":["https://openalex.org/I44260953"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031910306","display_name":"Tim Schl\u00fcter","orcid":null},"institutions":[{"id":"https://openalex.org/I44260953","display_name":"Heinrich Heine University D\u00fcsseldorf","ror":"https://ror.org/024z2rq82","country_code":"DE","type":"education","lineage":["https://openalex.org/I44260953"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tim Schl\u00fcter","raw_affiliation_strings":["Heinrich-Heine-Universitat, dusseldorf, Germany"],"affiliations":[{"raw_affiliation_string":"Heinrich-Heine-Universitat, dusseldorf, Germany","institution_ids":["https://openalex.org/I44260953"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037038868","display_name":"Stefan Conrad","orcid":"https://orcid.org/0000-0003-2788-3854"},"institutions":[{"id":"https://openalex.org/I44260953","display_name":"Heinrich Heine University D\u00fcsseldorf","ror":"https://ror.org/024z2rq82","country_code":"DE","type":"education","lineage":["https://openalex.org/I44260953"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Stefan Conrad","raw_affiliation_strings":["Heinrich-Heine-Universitat, dusseldorf, Germany"],"affiliations":[{"raw_affiliation_string":"Heinrich-Heine-Universitat, dusseldorf, Germany","institution_ids":["https://openalex.org/I44260953"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5013126220"],"corresponding_institution_ids":["https://openalex.org/I44260953"],"apc_list":null,"apc_paid":null,"fwci":0.3733,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.60454908,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"263","last_page":"263"},"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9976999759674072,"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/T11309","display_name":"Music and Audio Processing","score":0.9750999808311462,"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/dynamic-time-warping","display_name":"Dynamic time warping","score":0.9161268472671509},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8141714334487915},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6561747789382935},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6528239250183105},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.6340929269790649},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5983126163482666},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.5333559513092041},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5139688849449158},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.48562899231910706},{"id":"https://openalex.org/keywords/subject","display_name":"Subject (documents)","score":0.42680540680885315},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38072073459625244},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.32555490732192993},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24358603358268738},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.13841331005096436}],"concepts":[{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.9161268472671509},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8141714334487915},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6561747789382935},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6528239250183105},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.6340929269790649},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5983126163482666},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.5333559513092041},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5139688849449158},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.48562899231910706},{"id":"https://openalex.org/C2777855551","wikidata":"https://www.wikidata.org/wiki/Q12310021","display_name":"Subject (documents)","level":2,"score":0.42680540680885315},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38072073459625244},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.32555490732192993},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24358603358268738},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.13841331005096436},{"id":"https://openalex.org/C161191863","wikidata":"https://www.wikidata.org/wiki/Q199655","display_name":"Library science","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1451940.1451977","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1451940.1451977","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2008 international symposium on Database engineering & applications - IDEAS '08","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W39942585","https://openalex.org/W69342273","https://openalex.org/W109109120","https://openalex.org/W116902681","https://openalex.org/W152645600","https://openalex.org/W1534304300","https://openalex.org/W1587157435","https://openalex.org/W1660390307","https://openalex.org/W1966554111","https://openalex.org/W1997964931","https://openalex.org/W2020663302","https://openalex.org/W2061263584","https://openalex.org/W2067789269","https://openalex.org/W2086506616","https://openalex.org/W2095728454","https://openalex.org/W2128160875","https://openalex.org/W2144994235","https://openalex.org/W2156301828","https://openalex.org/W2165612380","https://openalex.org/W3160736186"],"related_works":["https://openalex.org/W4366275534","https://openalex.org/W3014558862","https://openalex.org/W2609942398","https://openalex.org/W2764033112","https://openalex.org/W3092399163","https://openalex.org/W4380451100","https://openalex.org/W2772616816","https://openalex.org/W4205256820","https://openalex.org/W3005992387","https://openalex.org/W4360924407"],"abstract_inverted_index":{"In":[0,79],"this":[1,35,80,106],"work,":[2],"we":[3,103],"describe":[4],"an":[5],"approach":[6,107],"which":[7],"aims":[8],"to":[9,28,45,64],"make":[10],"typed":[11],"texts":[12,51,115],"comparable":[13],"with":[14,73],"temporal":[15],"data":[16],"mining":[17],"methods.":[18],"This":[19],"proposal":[20],"was":[21,89],"made":[22],"in":[23,59,110,140],"earlier":[24],"work":[25],"[11],":[26],"but":[27],"our":[29],"knowledge":[30],"no":[31],"significant":[32],"research":[33],"on":[34],"subject":[36],"has":[37],"been":[38],"done":[39],"yet.":[40],"The":[41],"basic":[42],"idea":[43,132],"is":[44],"derive":[46],"artificial":[47],"time":[48,68,76,97],"series":[49,69,77,98],"from":[50],"by":[52],"counting":[53],"the":[54,83,111,117,145],"occurrences":[55],"of":[56,75,113],"relevant":[57],"keywords":[58],"a":[60],"sliding":[61],"window":[62],"applied":[63],"them,":[65],"and":[66,102],"these":[67],"can":[70],"be":[71,135,149],"compared":[72],"techniques":[74],"analysis.":[78],"particular":[81],"case":[82],"Dynamic":[84],"Time":[85],"Warping":[86],"distance":[87],"[3]":[88],"used.":[90],"By":[91],"extensive":[92],"testing":[93],"adequate":[94],"parameters":[95],"for":[96,138],"calculation":[99],"were":[100],"derived,":[101],"show":[104],"that":[105],"might":[108,133],"aid":[109],"recognition":[112],"similar":[114,121],"since":[116,143],"observed":[118],"distances":[119],"between":[120,128],"documents":[122],"are":[123],"significantly":[124],"lower":[125],"than":[126],"those":[127],"unrelated":[129],"texts.":[130],"Our":[131],"also":[134],"especially":[136],"suitable":[137],"comparison":[139],"different":[141],"languages":[142],"only":[144],"keyword":[146],"translations":[147],"must":[148],"known.":[150]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}