{"id":"https://openalex.org/W2119323564","doi":"https://doi.org/10.14778/2536206.2536208","title":"A data-adaptive and dynamic segmentation index for whole matching on time series","display_name":"A data-adaptive and dynamic segmentation index for whole matching on time series","publication_year":2013,"publication_date":"2013-08-01","ids":{"openalex":"https://openalex.org/W2119323564","doi":"https://doi.org/10.14778/2536206.2536208","mag":"2119323564"},"language":"en","primary_location":{"id":"doi:10.14778/2536206.2536208","is_oa":false,"landing_page_url":"https://doi.org/10.14778/2536206.2536208","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/A5115602260","display_name":"Yang Wang","orcid":"https://orcid.org/0000-0002-9694-2267"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Wang","raw_affiliation_strings":["School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100396080","display_name":"Peng Wang","orcid":"https://orcid.org/0000-0002-8136-9621"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Wang","raw_affiliation_strings":["School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062247330","display_name":"Jian Pei","orcid":"https://orcid.org/0000-0002-2200-8711"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jian Pei","raw_affiliation_strings":["School of Computing Science, Simon Fraser University, Burnaby, BC, Canada","School of Computing Science, Simon Fraser University, Burnaby BC, Canada#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing Science, Simon Fraser University, Burnaby, BC, Canada","institution_ids":["https://openalex.org/I18014758"]},{"raw_affiliation_string":"School of Computing Science, Simon Fraser University, Burnaby BC, Canada#TAB#","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100392156","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0003-0264-788X"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["School of Computer Science, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102752342","display_name":"Sheng Huang","orcid":"https://orcid.org/0009-0001-4692-5522"},"institutions":[{"id":"https://openalex.org/I4210126794","display_name":"IBM Research (China)","ror":"https://ror.org/02yg1pf55","country_code":"CN","type":"company","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126794"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Huang","raw_affiliation_strings":["IBM Research China, Shanghai, China","IBM-Research China, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research China, Shanghai, China","institution_ids":["https://openalex.org/I4210126794"]},{"raw_affiliation_string":"IBM-Research China, Shanghai, China","institution_ids":["https://openalex.org/I4210126794"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.8987,"has_fulltext":false,"cited_by_count":99,"citation_normalized_percentile":{"value":0.91674365,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"6","issue":"10","first_page":"793","last_page":"804"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9828000068664551,"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":0.980400025844574,"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/search-engine-indexing","display_name":"Search engine indexing","score":0.7811654806137085},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.7575781345367432},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6662934422492981},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6384327411651611},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5947282314300537},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.5426303744316101},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5364542603492737},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5115240812301636},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.48853930830955505},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.475236177444458},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46796947717666626},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3588408827781677},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3582988977432251},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26918357610702515},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26319825649261475},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1397128701210022},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1370711326599121},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.10074418783187866}],"concepts":[{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.7811654806137085},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.7575781345367432},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6662934422492981},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6384327411651611},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5947282314300537},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.5426303744316101},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5364542603492737},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5115240812301636},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.48853930830955505},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.475236177444458},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46796947717666626},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3588408827781677},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3582988977432251},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26918357610702515},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26319825649261475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1397128701210022},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1370711326599121},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.10074418783187866},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.14778/2536206.2536208","is_oa":false,"landing_page_url":"https://doi.org/10.14778/2536206.2536208","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:CiteSeerX.psu:10.1.1.406.208","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.406.208","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.vldb.org/pvldb/vol6/p793-wang.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W21640363","https://openalex.org/W1499049447","https://openalex.org/W1552695456","https://openalex.org/W1989037929","https://openalex.org/W1993855803","https://openalex.org/W2006783944","https://openalex.org/W2020865809","https://openalex.org/W2036557187","https://openalex.org/W2039260438","https://openalex.org/W2042591571","https://openalex.org/W2046353575","https://openalex.org/W2049877533","https://openalex.org/W2066796814","https://openalex.org/W2077720176","https://openalex.org/W2084481683","https://openalex.org/W2101005720","https://openalex.org/W2118269922","https://openalex.org/W2128061541","https://openalex.org/W2133105380","https://openalex.org/W2159138228","https://openalex.org/W2161621125","https://openalex.org/W2163336863","https://openalex.org/W6600914332","https://openalex.org/W6633153253","https://openalex.org/W6659467978","https://openalex.org/W6661122342","https://openalex.org/W6662787740","https://openalex.org/W7043153843"],"related_works":["https://openalex.org/W3024364549","https://openalex.org/W4206019083","https://openalex.org/W1949910768","https://openalex.org/W1480566255","https://openalex.org/W2254397067","https://openalex.org/W2013685631","https://openalex.org/W1610355325","https://openalex.org/W1882921205","https://openalex.org/W2129925734","https://openalex.org/W4387185219"],"abstract_inverted_index":{"Similarity":[0],"search":[1,137],"on":[2,50,97,118],"time":[3,25,38,73,82,85,98,121,134],"series":[4,26,39,74,135],"is":[5],"an":[6,66],"essential":[7],"operation":[8],"in":[9,42,79,104],"many":[10],"applications.":[11],"In":[12,61,100],"the":[13,18,43,56],"state-of-the-art":[14],"methods,":[15,21],"such":[16],"as":[17],"R-tree":[19],"based":[20],"SAX":[22],"and":[23,58,81,93,106,115,139],"iSAX,":[24],"are":[27,40],"by":[28],"default":[29],"divided":[30],"into":[31],"equi-length":[32],"segments":[33,57],"globally,":[34],"that":[35,128],"is,":[36],"all":[37,72],"segmented":[41],"same":[44],"way.":[45],"Those":[46],"methods":[47],"then":[48],"focus":[49],"how":[51],"to":[52,102],"approximate":[53],"or":[54],"symbolize":[55],"construct":[59],"indexes.":[60],"this":[62],"paper,":[63],"we":[64],"make":[65],"important":[67],"observation:":[68],"global":[69],"segmentation":[70,95],"of":[71],"may":[75],"incur":[76],"unnecessary":[77],"cost":[78],"space":[80,105],"for":[83],"indexing":[84],"series.":[86,99,122],"We":[87],"develop":[88],"DSTree,":[89],"a":[90],"data":[91],"adaptive":[92],"dynamic":[94],"index":[96,110,131],"addition":[101],"savings":[103],"time,":[107],"our":[108,129],"new":[109,130],"can":[111],"provide":[112],"tight":[113],"upper":[114],"lower":[116],"bounds":[117],"distances":[119],"between":[120],"An":[123],"extensive":[124],"empirical":[125],"study":[126],"shows":[127],"DSTree":[132],"supports":[133],"similarity":[136],"effectively":[138],"efficiently.":[140]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":23},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":13},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
