{"id":"https://openalex.org/W2782996586","doi":"https://doi.org/10.1109/bigdata.2017.8258343","title":"Ranked time series matching by interleaving similarity distances","display_name":"Ranked time series matching by interleaving similarity distances","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2782996586","doi":"https://doi.org/10.1109/bigdata.2017.8258343","mag":"2782996586"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8258343","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258343","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big 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/A5002964664","display_name":"Cuong Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Cuong Nguyen","raw_affiliation_strings":["Worcester Polytechnic Institute"],"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018940839","display_name":"Charles Lovering","orcid":null},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charles Lovering","raw_affiliation_strings":["Worcester Polytechnic Institute"],"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008363462","display_name":"Rodica Neamtu","orcid":"https://orcid.org/0000-0003-4647-5610"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rodica Neamtu","raw_affiliation_strings":["Worcester Polytechnic Institute"],"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute","institution_ids":["https://openalex.org/I107077323"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5002964664"],"corresponding_institution_ids":["https://openalex.org/I107077323"],"apc_list":null,"apc_paid":null,"fwci":1.1095,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.80045539,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"101","issue":null,"first_page":"3530","last_page":"3539"},"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/T11309","display_name":"Music and Audio Processing","score":0.9796000123023987,"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/T11106","display_name":"Data Management and Algorithms","score":0.9549000263214111,"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":0.8649782538414001},{"id":"https://openalex.org/keywords/interleaving","display_name":"Interleaving","score":0.7920445203781128},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7026323080062866},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6874712109565735},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.6799719333648682},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5577516555786133},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5564334392547607},{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.5044273138046265},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.49310800433158875},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4922443926334381},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4873410165309906},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4794943928718567},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4211551547050476},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38740241527557373},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36613500118255615},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21846413612365723},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19917243719100952},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1990501880645752},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11691200733184814}],"concepts":[{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.8649782538414001},{"id":"https://openalex.org/C28034677","wikidata":"https://www.wikidata.org/wiki/Q17092530","display_name":"Interleaving","level":2,"score":0.7920445203781128},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7026323080062866},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6874712109565735},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.6799719333648682},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5577516555786133},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5564334392547607},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.5044273138046265},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.49310800433158875},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4922443926334381},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4873410165309906},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4794943928718567},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4211551547050476},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38740241527557373},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36613500118255615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21846413612365723},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19917243719100952},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1990501880645752},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11691200733184814},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2017.8258343","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258343","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1984475","https://openalex.org/W69342273","https://openalex.org/W116902681","https://openalex.org/W195533127","https://openalex.org/W1278605765","https://openalex.org/W1499049447","https://openalex.org/W1534304300","https://openalex.org/W1766818275","https://openalex.org/W1966406756","https://openalex.org/W1966554111","https://openalex.org/W2046144220","https://openalex.org/W2091921805","https://openalex.org/W2095409369","https://openalex.org/W2098759488","https://openalex.org/W2099302229","https://openalex.org/W2110908910","https://openalex.org/W2125120904","https://openalex.org/W2128061541","https://openalex.org/W2128160875","https://openalex.org/W2130876538","https://openalex.org/W2138550913","https://openalex.org/W2159236148","https://openalex.org/W2162193833","https://openalex.org/W2162800060","https://openalex.org/W2163336863","https://openalex.org/W2209953108","https://openalex.org/W2577640080","https://openalex.org/W3004804023","https://openalex.org/W3147178137","https://openalex.org/W6600087469","https://openalex.org/W6604828220","https://openalex.org/W6607976765","https://openalex.org/W6678907263"],"related_works":["https://openalex.org/W2767543488","https://openalex.org/W4206434459","https://openalex.org/W3111157199","https://openalex.org/W3118503757","https://openalex.org/W2760717005","https://openalex.org/W2014214435","https://openalex.org/W2163118894","https://openalex.org/W58346954","https://openalex.org/W2901968357","https://openalex.org/W2359638073"],"abstract_inverted_index":{"Similarity":[0],"analytics":[1,28],"of":[2,12,96,130,163,181],"time":[3,99,112],"series":[4,100,113],"data":[5,38],"are":[6,30,119,140,160],"critical":[7],"for":[8,36,43],"a":[9,57,64,88],"wide":[10],"range":[11],"applications":[13],"ranging":[14],"from":[15,20],"medical":[16],"to":[17,23,32,80,87,126],"financial,":[18],"and":[19,47,59,133,169],"weather":[21],"forecasting":[22],"image":[24],"processing.":[25],"Yet":[26],"these":[27,117],"tasks":[29],"known":[31],"be":[33],"prohibitively":[34],"expensive":[35],"large":[37],"sets,":[39],"especially":[40],"when":[41],"accounting":[42],"varying":[44],"temporal":[45,134],"alignments":[46],"lengths.":[48],"Our":[49,92,150],"proposed":[50],"framework":[51],"tackles":[52],"this":[53],"challenge":[54],"by":[55,108,176],"adopting":[56],"preprocess-once":[58],"query-many-times":[60],"paradigm.":[61],"We":[62],"extend":[63],"previous":[65],"formal":[66],"model":[67],"interleaving":[68],"the":[69,74,82,110,123,167,182],"inexpensive":[70],"Euclidean":[71],"distance":[72],"with":[73,136],"robust":[75],"Dynamic":[76],"Time":[77],"Warping":[78],"(DTW)":[79],"retrieve":[81],"k":[83,98],"most":[84],"similar":[85,128],"matches":[86],"given":[89],"sample":[90],"sequence.":[91],"extended":[93],"ONline":[94],"EXploration":[95],"top":[97],"similarity":[101,106],"system":[102],"(K-ONEX)":[103],"first":[104],"encodes":[105],"relationships":[107],"compressing":[109],"raw":[111],"into":[114],"Euclidean-based":[115],"groups;":[116],"groups":[118],"further":[120],"explored":[121],"using":[122],"elastic":[124],"DTW":[125],"find":[127],"sequences":[129,183],"any":[131],"length":[132],"alignment":[135],"response":[137,157],"times":[138,158],"that":[139,154,159],"almost":[141],"as":[142,144],"fast":[143],"retrieving":[145],"only":[146],"one":[147],"best":[148],"match.":[149],"empirical":[151],"results":[152],"illustrate":[153],"K-ONEX":[155],"provides":[156],"2-3":[161],"orders":[162],"magnitude":[164],"faster":[165],"than":[166,179],"benchmark":[168],"state-of-the-art":[170],"methods":[171],"while":[172],"achieving":[173],"100%":[174],"accuracy":[175],"exploring":[177],"less":[178],"0.5%":[180],"in":[184],"each":[185],"dataset.":[186]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}