{"id":"https://openalex.org/W2751600782","doi":"https://doi.org/10.14778/3137628.3137655","title":"Distributed trajectory similarity search","display_name":"Distributed trajectory similarity search","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2751600782","doi":"https://doi.org/10.14778/3137628.3137655","mag":"2751600782"},"language":"en","primary_location":{"id":"doi:10.14778/3137628.3137655","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3137628.3137655","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/A5101871030","display_name":"Dong Xie","orcid":"https://orcid.org/0000-0003-4857-900X"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Dong Xie","raw_affiliation_strings":["University of Utah"],"affiliations":[{"raw_affiliation_string":"University of Utah","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100450470","display_name":"Feifei Li","orcid":"https://orcid.org/0009-0003-0770-5775"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Feifei Li","raw_affiliation_strings":["University of Utah"],"affiliations":[{"raw_affiliation_string":"University of Utah","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017619650","display_name":"Jeff M. Phillips","orcid":"https://orcid.org/0000-0003-1169-2965"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeff M. Phillips","raw_affiliation_strings":["University of Utah"],"affiliations":[{"raw_affiliation_string":"University of Utah","institution_ids":["https://openalex.org/I223532165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101871030"],"corresponding_institution_ids":["https://openalex.org/I223532165"],"apc_list":null,"apc_paid":null,"fwci":8.9481,"has_fulltext":false,"cited_by_count":131,"citation_normalized_percentile":{"value":0.98474634,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"10","issue":"11","first_page":"1478","last_page":"1489"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","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/T11106","display_name":"Data Management and Algorithms","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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9923999905586243,"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/T10317","display_name":"Advanced Database Systems and Queries","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/computer-science","display_name":"Computer science","score":0.8024284243583679},{"id":"https://openalex.org/keywords/spark","display_name":"SPARK (programming language)","score":0.729495108127594},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.655516505241394},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.652458131313324},{"id":"https://openalex.org/keywords/hausdorff-distance","display_name":"Hausdorff distance","score":0.6498188972473145},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.607501745223999},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5462145805358887},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5346996188163757},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5098639726638794},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.50180983543396},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.49186182022094727},{"id":"https://openalex.org/keywords/result-set","display_name":"Result set","score":0.4573054611682892},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.44545817375183105},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4147701561450958},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3211885988712311},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22281557321548462},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.20671850442886353}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8024284243583679},{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.729495108127594},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.655516505241394},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.652458131313324},{"id":"https://openalex.org/C141898687","wikidata":"https://www.wikidata.org/wiki/Q1501997","display_name":"Hausdorff distance","level":2,"score":0.6498188972473145},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.607501745223999},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5462145805358887},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5346996188163757},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5098639726638794},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.50180983543396},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.49186182022094727},{"id":"https://openalex.org/C4969071","wikidata":"https://www.wikidata.org/wiki/Q7316353","display_name":"Result set","level":3,"score":0.4573054611682892},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.44545817375183105},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4147701561450958},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3211885988712311},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22281557321548462},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.20671850442886353},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"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/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14778/3137628.3137655","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3137628.3137655","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"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W578585133","https://openalex.org/W1567097384","https://openalex.org/W1597504361","https://openalex.org/W1633157623","https://openalex.org/W1801564529","https://openalex.org/W1814491495","https://openalex.org/W1864972570","https://openalex.org/W1881181920","https://openalex.org/W1970995121","https://openalex.org/W1981398125","https://openalex.org/W1991594768","https://openalex.org/W2000705203","https://openalex.org/W2008814185","https://openalex.org/W2031674781","https://openalex.org/W2046466133","https://openalex.org/W2049644877","https://openalex.org/W2061959470","https://openalex.org/W2065663378","https://openalex.org/W2072237376","https://openalex.org/W2073992193","https://openalex.org/W2079168081","https://openalex.org/W2083244827","https://openalex.org/W2083364906","https://openalex.org/W2097921974","https://openalex.org/W2099973661","https://openalex.org/W2110707662","https://openalex.org/W2112877387","https://openalex.org/W2116400826","https://openalex.org/W2118269922","https://openalex.org/W2118371392","https://openalex.org/W2124299914","https://openalex.org/W2131975293","https://openalex.org/W2133522940","https://openalex.org/W2135961964","https://openalex.org/W2145195191","https://openalex.org/W2147880780","https://openalex.org/W2149648981","https://openalex.org/W2151263703","https://openalex.org/W2157092487","https://openalex.org/W2171361861","https://openalex.org/W2292151842","https://openalex.org/W2436533802","https://openalex.org/W2479685394","https://openalex.org/W2561479996","https://openalex.org/W2568308068","https://openalex.org/W4242599275","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2766461310","https://openalex.org/W4247566972","https://openalex.org/W4388692845","https://openalex.org/W3202731209","https://openalex.org/W3211874991","https://openalex.org/W2904953191","https://openalex.org/W3097048546","https://openalex.org/W2130821339","https://openalex.org/W2506818298","https://openalex.org/W1963812750"],"abstract_inverted_index":{"Mobile":[0],"and":[1,24,76,81,83,100,162,173],"sensing":[2],"devices":[3],"have":[4,9,28,125,157],"already":[5],"become":[6],"ubiquitous.":[7],"They":[8],"made":[10],"tracking":[11],"moving":[12],"objects":[13],"an":[14],"easy":[15],"task.":[16],"As":[17],"a":[18,42,56,89,109,119,132],"result,":[19],"mobile":[20],"applications":[21,69],"like":[22],"Uber":[23],"many":[25,93],"IoT":[26],"projects":[27],"generated":[29],"massive":[30],"amounts":[31],"of":[32,102,122],"trajectory":[33,63,115],"data":[34,135],"that":[35],"can":[36],"no":[37],"longer":[38],"be":[39],"processed":[40],"by":[41,138,166],"single":[43],"machine":[44],"efficiently.":[45],"Among":[46],"the":[47,127,149,152,159],"typical":[48],"query":[49,111,145,163],"operations":[50,96],"over":[51,118],"trajectories,":[52],"similarity":[53,116],"search":[54,117],"is":[55,66,87],"common":[57],"yet":[58],"expensive":[59],"operator":[60,91],"in":[61,70,130],"querying":[62],"data.":[64],"It":[65,86],"useful":[67],"for":[68,92],"different":[71,141],"domains":[72],"such":[73,97],"as":[74,98],"traffic":[75],"transportation":[77],"optimizations,":[78],"weather":[79],"forecast":[80],"modeling,":[82],"sports":[84],"analytics.":[85],"also":[88],"fundamental":[90],"important":[94],"mining":[95],"clustering":[99],"classification":[101],"trajectories.":[103,123],"In":[104],"this":[105],"paper,":[106],"we":[107],"propose":[108],"distributed":[110,134],"framework":[112,129,146],"to":[113,170],"process":[114],"large":[120],"set":[121],"We":[124],"implemented":[126],"proposed":[128],"Spark,":[131],"popular":[133],"processing":[136],"engine,":[137],"carefully":[139],"considering":[140],"design":[142,174],"choices.":[143],"Our":[144],"supports":[147],"both":[148],"Hausdorff":[150],"distance":[151],"Fr\u00e9chet":[153],"distance.":[154],"Extensive":[155],"experiments":[156],"demonstrated":[158],"excellent":[160],"scalability":[161],"efficiency":[164],"achieved":[165],"our":[167],"design,":[168],"compared":[169],"other":[171],"methods":[172],"alternatives.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":19},{"year":2018,"cited_by_count":8}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
