{"id":"https://openalex.org/W2426948338","doi":"https://doi.org/10.1109/icde.2016.7498306","title":"Automatic user identification method across heterogeneous mobility data sources","display_name":"Automatic user identification method across heterogeneous mobility data sources","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2426948338","doi":"https://doi.org/10.1109/icde.2016.7498306","mag":"2426948338"},"language":"en","primary_location":{"id":"doi:10.1109/icde.2016.7498306","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde.2016.7498306","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100694032","display_name":"Wei Cao","orcid":"https://orcid.org/0000-0002-3872-3226"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Cao","raw_affiliation_strings":["Big Data Lab, Baidu Inc","Institute for Interdisciplinary Information Sciences, Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Big Data Lab, Baidu Inc","institution_ids":["https://openalex.org/I98301712"]},{"raw_affiliation_string":"Institute for Interdisciplinary Information Sciences, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107946810","display_name":"Zhengwei Wu","orcid":"https://orcid.org/0000-0003-3107-0397"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengwei Wu","raw_affiliation_strings":["Big Data Lab, Baidu Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Big Data Lab, Baidu Inc","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100391517","display_name":"Dong Wang","orcid":"https://orcid.org/0000-0002-9599-8023"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Wang","raw_affiliation_strings":["Institute for Interdisciplinary Information Sciences, Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Interdisciplinary Information Sciences, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100402538","display_name":"Jian Li","orcid":"https://orcid.org/0000-0002-7725-4346"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Li","raw_affiliation_strings":["Institute for Interdisciplinary Information Sciences, Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Interdisciplinary Information Sciences, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012284260","display_name":"Haishan Wu","orcid":"https://orcid.org/0000-0002-6571-7024"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haishan Wu","raw_affiliation_strings":["Big Data Lab, Baidu Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Big Data Lab, Baidu Inc","institution_ids":["https://openalex.org/I98301712"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.7007,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.97376093,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"978","last_page":"989"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9991999864578247,"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/T10757","display_name":"Geographic Information Systems Studies","score":0.989300012588501,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8545318841934204},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6748543381690979},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.566136360168457},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5316731333732605},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.5151456594467163},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4421229362487793},{"id":"https://openalex.org/keywords/data-integration","display_name":"Data integration","score":0.4260634183883667},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.42432650923728943},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.42267242074012756},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.32786309719085693},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16764739155769348}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8545318841934204},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6748543381690979},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.566136360168457},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5316731333732605},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.5151456594467163},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4421229362487793},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.4260634183883667},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.42432650923728943},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.42267242074012756},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.32786309719085693},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16764739155769348},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icde.2016.7498306","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde.2016.7498306","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W345783279","https://openalex.org/W1484405533","https://openalex.org/W1507327861","https://openalex.org/W1567097384","https://openalex.org/W1597504361","https://openalex.org/W1785837166","https://openalex.org/W1814491495","https://openalex.org/W1833521484","https://openalex.org/W1864972570","https://openalex.org/W1936915774","https://openalex.org/W1969382479","https://openalex.org/W1974854524","https://openalex.org/W1982300822","https://openalex.org/W1995629273","https://openalex.org/W1999842613","https://openalex.org/W2027428235","https://openalex.org/W2035349694","https://openalex.org/W2045686369","https://openalex.org/W2047328790","https://openalex.org/W2051938031","https://openalex.org/W2057135445","https://openalex.org/W2064792883","https://openalex.org/W2074194940","https://openalex.org/W2081394934","https://openalex.org/W2097268493","https://openalex.org/W2109341384","https://openalex.org/W2112877387","https://openalex.org/W2114353347","https://openalex.org/W2114797768","https://openalex.org/W2115240023","https://openalex.org/W2118371392","https://openalex.org/W2121086382","https://openalex.org/W2126194848","https://openalex.org/W2126907894","https://openalex.org/W2136283497","https://openalex.org/W2147880780","https://openalex.org/W2167686542","https://openalex.org/W2169896292","https://openalex.org/W2171960770","https://openalex.org/W6639107604"],"related_works":["https://openalex.org/W3162200841","https://openalex.org/W2319693127","https://openalex.org/W2474567666","https://openalex.org/W2072263576","https://openalex.org/W2790658443","https://openalex.org/W1940044583","https://openalex.org/W2056226831","https://openalex.org/W2806903871","https://openalex.org/W4320802053","https://openalex.org/W1549395822"],"abstract_inverted_index":{"With":[0],"the":[1,83,90,120,134,146,157,161,171],"ubiquity":[2],"of":[3,11,42,92],"location":[4,31],"based":[5,32,76,85],"services":[6],"and":[7,65,106],"applications,":[8],"large":[9,70,140],"volume":[10],"mobility":[12,135,147,175],"data":[13,21,48,71,98,136,142,148,158,180],"has":[14],"been":[15],"generated":[16],"routinely,":[17],"usually":[18],"from":[19,96,150],"heterogeneous":[20,47,179],"sources,":[22,99],"such":[23,46],"as":[24],"different":[25,97,103,151],"GPS-embedded":[26],"devices,":[27],"mobile":[28],"apps":[29],"or":[30],"service":[33],"providers.":[34],"In":[35],"this":[36],"paper,":[37],"we":[38],"investigate":[39],"efficient":[40],"ways":[41],"identifying":[43],"users":[44],"across":[45],"sources.":[49,181],"We":[50,109],"present":[51],"a":[52,78],"MapReduce-based":[53],"framework":[54,74,118],"called":[55,82],"Automatic":[56],"User":[57],"Identification":[58],"(AUI)":[59],"which":[60,88,114],"is":[61,75],"easy":[62],"to":[63,68,155],"deploy":[64],"can":[66],"scale":[67,141],"very":[69,102],"set.":[72],"Our":[73,124],"on":[77,126,139],"novel":[79],"similarity":[80,86,91],"measure":[81],"signal":[84],"(SIG)":[87],"measures":[89],"users'":[93],"trajectories":[94],"gathered":[95],"typically":[100],"with":[101],"sampling":[104],"rates":[105],"noise":[107],"patterns.":[108],"conduct":[110],"extensive":[111],"experimental":[112],"evaluations,":[113],"show":[115],"that":[116],"our":[117,164],"outperforms":[119],"existing":[121],"methods":[122],"significantly.":[123],"study":[125,165],"one":[127],"hand":[128],"provides":[129,166],"an":[130,167],"effective":[131],"approach":[132],"for":[133,170],"integration":[137],"problem":[138,177],"sets,":[143],"i.e.,":[144],"combining":[145],"sets":[149],"sources":[152],"in":[153],"order":[154],"enhance":[156],"quality.":[159],"On":[160],"other":[162],"hand,":[163],"in-depth":[168],"investigation":[169],"widely":[172],"studied":[173],"human":[174],"uniqueness":[176],"under":[178]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
