{"id":"https://openalex.org/W2121186986","doi":"https://doi.org/10.14778/1687627.1687721","title":"Anticipatory DTW for efficient similarity search in time series databases","display_name":"Anticipatory DTW for efficient similarity search in time series databases","publication_year":2009,"publication_date":"2009-08-01","ids":{"openalex":"https://openalex.org/W2121186986","doi":"https://doi.org/10.14778/1687627.1687721","mag":"2121186986"},"language":"en","primary_location":{"id":"doi:10.14778/1687627.1687721","is_oa":false,"landing_page_url":"https://doi.org/10.14778/1687627.1687721","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/A5104360871","display_name":"Ira Assent","orcid":"https://orcid.org/0000-0002-1091-9948"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":true,"raw_author_name":"Ira Assent","raw_affiliation_strings":["Aalborg University, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalborg University, Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024318819","display_name":"Marc Wichterich","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Marc Wichterich","raw_affiliation_strings":["RWTH Aachen University, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049176185","display_name":"Ralph Krieger","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ralph Krieger","raw_affiliation_strings":["RWTH Aachen University, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022706154","display_name":"Hardy Kremer","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hardy Kremer","raw_affiliation_strings":["RWTH Aachen University, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003335849","display_name":"Thomas Seidl","orcid":"https://orcid.org/0000-0002-4861-1412"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Thomas Seidl","raw_affiliation_strings":["RWTH Aachen University, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5104360871"],"corresponding_institution_ids":["https://openalex.org/I891191580"],"apc_list":null,"apc_paid":null,"fwci":5.3552,"has_fulltext":false,"cited_by_count":68,"citation_normalized_percentile":{"value":0.95911199,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"2","issue":"1","first_page":"826","last_page":"837"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998000264167786,"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":0.9998000264167786,"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":0.991100013256073,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9501000046730042,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9444093108177185},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7593207359313965},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6242423057556152},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5883201360702515},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5677784085273743},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.49484652280807495},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.49311086535453796},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.47671622037887573},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4456339180469513},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.43683481216430664},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4245935082435608},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.41990065574645996},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4019142985343933},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.38170674443244934},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37115466594696045},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3441123366355896},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22023186087608337},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.1027708649635315}],"concepts":[{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.9444093108177185},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7593207359313965},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6242423057556152},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5883201360702515},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5677784085273743},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.49484652280807495},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49311086535453796},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.47671622037887573},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4456339180469513},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.43683481216430664},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4245935082435608},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.41990065574645996},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4019142985343933},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.38170674443244934},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37115466594696045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3441123366355896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22023186087608337},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.1027708649635315},{"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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.14778/1687627.1687721","is_oa":false,"landing_page_url":"https://doi.org/10.14778/1687627.1687721","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:10.1.1.151.4862","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.151.4862","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/2/vldb09-793.pdf","raw_type":"text"},{"id":"pmh:oai:publications.rwth-aachen.de:185327","is_oa":false,"landing_page_url":"https://publications.rwth-aachen.de/record/185327","pdf_url":null,"source":{"id":"https://openalex.org/S4306401362","display_name":"RWTH Publications (RWTH Aachen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887968799","host_organization_name":"RWTH Aachen University","host_organization_lineage":["https://openalex.org/I887968799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"5. International Conference on Very Large Data Bases, VLDB 2009, Lyon, France, 2009","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W58346954","https://openalex.org/W69342273","https://openalex.org/W116902681","https://openalex.org/W152645600","https://openalex.org/W1536481811","https://openalex.org/W1538563633","https://openalex.org/W1559148916","https://openalex.org/W1597504361","https://openalex.org/W1601044198","https://openalex.org/W1864972570","https://openalex.org/W1966826021","https://openalex.org/W1968010112","https://openalex.org/W2034528422","https://openalex.org/W2062903088","https://openalex.org/W2066796814","https://openalex.org/W2066834853","https://openalex.org/W2098759488","https://openalex.org/W2101319014","https://openalex.org/W2107856142","https://openalex.org/W2112791883","https://openalex.org/W2128160875","https://openalex.org/W2132585078","https://openalex.org/W2133184712","https://openalex.org/W2133706543","https://openalex.org/W2134627110","https://openalex.org/W2137089646","https://openalex.org/W2144117844","https://openalex.org/W2144994235","https://openalex.org/W2155919101","https://openalex.org/W2162193833","https://openalex.org/W2163336863","https://openalex.org/W2997437965","https://openalex.org/W3004804023"],"related_works":["https://openalex.org/W2513074791","https://openalex.org/W4206434459","https://openalex.org/W2182136398","https://openalex.org/W2347413598","https://openalex.org/W2014214435","https://openalex.org/W3049200503","https://openalex.org/W2982697608","https://openalex.org/W2609942398","https://openalex.org/W2052451333","https://openalex.org/W3141827490"],"abstract_inverted_index":{"Time":[0,35],"series":[1,30,81,161,175],"arise":[2],"in":[3,7,31,90,152],"many":[4],"different":[5],"applications":[6],"the":[8,84,88,91,104,108,133],"form":[9],"of":[10,21,114,121,132],"sensor":[11],"data,":[12,14],"stocks":[13],"videos,":[15],"and":[16,86,143,157,163,176],"other":[17],"time-related":[18],"information.":[19],"Analysis":[20],"this":[22,61],"data":[23],"typically":[24],"requires":[25],"searching":[26],"for":[27,45,55,111,124],"similar":[28,79],"time":[29,46,80,160,174],"a":[32,39,65,119],"database.":[33],"Dynamic":[34],"Warping":[36],"(DTW)":[37],"is":[38,50,168],"widely":[40],"used":[41],"high-quality":[42],"distance":[43],"measure":[44],"series.":[47],"As":[48],"DTW":[49,68,89,178],"computationally":[51],"expensive,":[52],"efficient":[53],"algorithms":[54,74],"fast":[56],"computation":[57],"are":[58],"crucial.":[59],"In":[60],"paper,":[62],"we":[63],"propose":[64],"novel":[66,136],"filter-and-refine":[67],"algorithm":[69],"called":[70],"Anticipatory":[71],"DTW.":[72,134],"Existing":[73],"aim":[75],"at":[76],"efficiently":[77],"finding":[78],"by":[82],"filtering":[83],"database":[85],"computing":[87],"refinement":[92],"step.":[93],"Unlike":[94],"these":[95],"algorithms,":[96],"our":[97,125,166],"approach":[98],"exploits":[99],"previously":[100],"unused":[101],"information":[102],"from":[103],"filter":[105],"step":[106],"during":[107],"refinement,":[109],"allowing":[110],"faster":[112],"rejection":[113],"false":[115,145],"candidates.":[116],"We":[117,147],"characterize":[118],"class":[120],"applicable":[122],"filters":[123],"approach,":[126],"which":[127],"comprises":[128],"state-of-the-art":[129],"lower":[130],"bounds":[131],"Our":[135],"anticipatory":[137],"pruning":[138],"incurs":[139],"hardly":[140],"any":[141],"over-head":[142],"no":[144],"dismissals.":[146],"demonstrate":[148],"substantial":[149],"efficiency":[150],"improvements":[151],"thorough":[153],"experiments":[154],"on":[155],"synthetic":[156],"real":[158],"world":[159],"databases":[162],"show":[164],"that":[165],"technique":[167],"highly":[169],"scalable":[170],"to":[171],"multivariate,":[172],"long":[173],"wide":[177],"bands.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":5},{"year":2012,"cited_by_count":9}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}