{"id":"https://openalex.org/W2577640080","doi":"https://doi.org/10.14778/3021924.3021933","title":"Interactive time series exploration powered by the marriage of similarity distances","display_name":"Interactive time series exploration powered by the marriage of similarity distances","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2577640080","doi":"https://doi.org/10.14778/3021924.3021933","mag":"2577640080"},"language":"en","primary_location":{"id":"doi:10.14778/3021924.3021933","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3021924.3021933","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-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/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 Worcester"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute Worcester","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ramoza Ahsan","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":"Ramoza Ahsan","raw_affiliation_strings":["Worcester Polytechnic Institute Worcester"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute Worcester","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008269094","display_name":"Elke A. Rundensteiner","orcid":"https://orcid.org/0000-0001-5375-9254"},"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":"Elke Rundensteiner","raw_affiliation_strings":["Worcester Polytechnic Institute Worcester"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute Worcester","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087999366","display_name":"G\u00e1bor N. S\u00e1rk\u00f6zy","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":"Gabor Sarkozy","raw_affiliation_strings":["Worcester Polytechnic Institute Worcester"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute Worcester","institution_ids":["https://openalex.org/I107077323"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I107077323"],"apc_list":null,"apc_paid":null,"fwci":3.5011,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.93808891,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"10","issue":"3","first_page":"169","last_page":"180"},"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":0.9710999727249146,"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":0.9643999934196472,"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":0.7160815596580505},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6836158633232117},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6353963017463684},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.6326911449432373},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6065467000007629},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6010400652885437},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5649434328079224},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5541945695877075},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5441222786903381},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5090115070343018},{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.4464595913887024},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36136674880981445},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3405117392539978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.328988254070282},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15078961849212646},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.13930580019950867},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09156760573387146}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7160815596580505},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6836158633232117},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6353963017463684},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.6326911449432373},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6065467000007629},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6010400652885437},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5649434328079224},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5541945695877075},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5441222786903381},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5090115070343018},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.4464595913887024},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36136674880981445},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3405117392539978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.328988254070282},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15078961849212646},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.13930580019950867},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09156760573387146},{"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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.14778/3021924.3021933","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3021924.3021933","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"},{"id":"pmh:oai:real.mtak.hu:43722","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400081","display_name":"Repository of the Academy's Library (Library of the Hungarian Academy of Sciences)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210140733","host_organization_name":"Library and Information Centre of the Hungarian Academy of Sciences","host_organization_lineage":["https://openalex.org/I4210140733"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332550","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1984475","https://openalex.org/W69342273","https://openalex.org/W116902681","https://openalex.org/W151863654","https://openalex.org/W1278605765","https://openalex.org/W1499049447","https://openalex.org/W1507039213","https://openalex.org/W1597504361","https://openalex.org/W1732493212","https://openalex.org/W1966554111","https://openalex.org/W1987019289","https://openalex.org/W2027506564","https://openalex.org/W2036557187","https://openalex.org/W2058168013","https://openalex.org/W2066796814","https://openalex.org/W2083888736","https://openalex.org/W2091921805","https://openalex.org/W2098759488","https://openalex.org/W2099302229","https://openalex.org/W2110908910","https://openalex.org/W2128061541","https://openalex.org/W2128160875","https://openalex.org/W2129330015","https://openalex.org/W2133706543","https://openalex.org/W2138550913","https://openalex.org/W2143325592","https://openalex.org/W2147347448","https://openalex.org/W2147880780","https://openalex.org/W2162193833","https://openalex.org/W2163336863","https://openalex.org/W2916086000","https://openalex.org/W3004804023","https://openalex.org/W3145042860","https://openalex.org/W3145128584"],"related_works":["https://openalex.org/W3017936921","https://openalex.org/W3118503757","https://openalex.org/W3111157199","https://openalex.org/W2174518382","https://openalex.org/W2760717005","https://openalex.org/W2163118894","https://openalex.org/W58346954","https://openalex.org/W2359638073","https://openalex.org/W3009759344","https://openalex.org/W2086226917"],"abstract_inverted_index":{"Finding":[0],"similar":[1],"trends":[2],"among":[3],"time":[4,25,29,44,48,95,122,195],"series":[5,30,49,96,196],"data":[6,78],"is":[7,36,98,163],"critical":[8,71],"for":[9,91],"applications":[10],"ranging":[11],"from":[12],"financial":[13],"planning":[14],"to":[15,39,74,165],"policy":[16],"making.":[17],"The":[18],"detection":[19],"of":[20,28,31,59,83,87,153],"these":[21],"multifaceted":[22],"relationships,":[23],"especially":[24],"warped":[26,123],"matching":[27],"different":[32],"lengths":[33,94],"and":[34,169,181],"alignments":[35],"prohibitively":[37],"expensive":[38],"compute.":[40],"To":[41],"achieve":[42],"real":[43],"responsiveness":[45],"on":[46,104],"large":[47],"datasets,":[50],"we":[51],"propose":[52],"a":[53,64,84,135,178,188],"novel":[54,194],"paradigm":[55],"called":[56],"Online":[57],"Exploration":[58],"Time":[60],"Series":[61],"(ONEX)":[62],"employing":[63],"powerful":[65],"one-time":[66],"preprocessing":[67],"step":[68],"that":[69,108,161],"encodes":[70],"similarity":[72,89],"relationships":[73,90],"support":[75,120],"subsequent":[76,121],"rapid":[77],"exploration.":[79],"Since":[80],"the":[81,105,116,129,141,147,154,174],"encoding":[82],"huge":[85],"number":[86],"pairwise":[88],"all":[92],"variable":[93],"segments":[97],"not":[99],"feasible,":[100],"our":[101],"work":[102],"rests":[103],"important":[106],"insight":[107],"clustering":[109],"with":[110,134],"inexpensive":[111],"point-to-point":[112],"distances":[113],"such":[114],"as":[115],"Euclidean":[117],"Distance":[118],"can":[119],"matching.":[124],"Our":[125,157],"ONEX":[126,162,186],"framework":[127],"overcomes":[128],"prohibitive":[130],"computational":[131],"costs":[132],"associated":[133],"more":[136,167],"robust":[137],"elastic":[138],"distance":[139],"namely":[140],"DTW":[142],"by":[143],"applying":[144],"it":[145],"over":[146],"surprisingly":[148],"compact":[149],"knowledge":[150],"base":[151],"instead":[152],"raw":[155],"data.":[156],"comparative":[158],"study":[159],"reveals":[160],"up":[164],"19%":[166],"accurate":[168,180],"several":[170],"times":[171],"faster":[172],"than":[173],"state-of-the-art.":[175],"Beyond":[176],"being":[177],"highly":[179],"fast":[182],"domain":[183],"independent":[184],"solution,":[185],"offers":[187],"truly":[189],"interactive":[190],"exploration":[191],"experience":[192],"supporting":[193],"operations.":[197]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":3}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}