{"id":"https://openalex.org/W2240471007","doi":null,"title":"Mobility Prediction Using Non-Parametric Bayesian Model.","display_name":"Mobility Prediction Using Non-Parametric Bayesian Model.","publication_year":2015,"publication_date":"2015-07-12","ids":{"openalex":"https://openalex.org/W2240471007","mag":"2240471007"},"language":"en","primary_location":{"id":"mag:2240471007","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1507.03292v2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/1507.03292v2","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075305744","display_name":"Jaeseong Jeong","orcid":"https://orcid.org/0000-0003-4533-3418","observed_orcids":["https://orcid.org/0000-0003-4533-3418"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jaeseong Jeong","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-4533-3418","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015149788","display_name":"Mathieu Leconte","orcid":"https://orcid.org/0009-0005-6342-8957","observed_orcids":["https://orcid.org/0009-0005-6342-8957"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mathieu Leconte","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5025136069","display_name":"Alexandre Prouti\u00e8re","orcid":"https://orcid.org/0000-0002-4679-4673","observed_orcids":["https://orcid.org/0000-0002-4679-4673"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alexandre Prouti\u00e8re","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-4679-4673","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.2775999903678894,"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":0.2775999903678894,"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":0.2615000009536743,"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/T10651","display_name":"IPv6, Mobility, Handover, Networks, Security","score":0.12729999423027039,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mobility-prediction","display_name":"mobility prediction","score":0.8759999871253967},{"id":"https://openalex.org/keywords/next-location-prediction","display_name":"next location prediction","score":0.7630000114440918},{"id":"https://openalex.org/keywords/nonparametric-bayesian-models","display_name":"nonparametric Bayesian models","score":0.9210000038146973},{"id":"https://openalex.org/keywords/camp","display_name":"cyclic AMP","score":0.7689999938011169},{"id":"https://openalex.org/keywords/wireless-networks","display_name":"wireless networks","score":0.628000020980835},{"id":"https://openalex.org/keywords/forecast-consistency","display_name":"prediction consistency","score":0.7400000095367432},{"id":"https://openalex.org/keywords/limited-training-data","display_name":"limited training data","score":0.597000002861023}],"concepts":[{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.890789270401001},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.809654712677002},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6070092916488647},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5786990523338318},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5742586255073547},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5493011474609375},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4949576258659363},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.45861175656318665},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.4426160454750061},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3763003349304199},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08775192499160767},{"id":"https://openalex.org/C136264566","wikidata":"https://www.wikidata.org/wiki/Q159810","display_name":"Economy","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2240471007","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1507.03292v2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:2240471007","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1507.03292v2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W66477288","https://openalex.org/W385466589","https://openalex.org/W1548584376","https://openalex.org/W1977793519","https://openalex.org/W1979745145","https://openalex.org/W2003706076","https://openalex.org/W2017170340","https://openalex.org/W2020213096","https://openalex.org/W2034716977","https://openalex.org/W2065246942","https://openalex.org/W2087122499","https://openalex.org/W2114925746","https://openalex.org/W2115870554","https://openalex.org/W2135291511"],"related_works":["https://openalex.org/W2962741536","https://openalex.org/W2039392404","https://openalex.org/W2895881388","https://openalex.org/W7098674202","https://openalex.org/W2896873793","https://openalex.org/W4281400155","https://openalex.org/W6997263689","https://openalex.org/W2765958396","https://openalex.org/W7095782377","https://openalex.org/W2171267091"],"abstract_inverted_index":{"Predicting":[0],"the":[1,20,35,44,55,65,87,103,108,112,117,146,163,169,172],"future":[2],"location":[3,29,37,89],"of":[4,22,38,48,90,111,171],"users":[5,84,114],"in":[6,145,152,162,191],"wireless":[7],"net-":[8],"works":[9],"has":[10],"numerous":[11],"applications,":[12],"and":[13,100,143,177,190],"can":[14],"help":[15],"service":[16,23],"providers":[17],"to":[18,85,115],"improve":[19,116],"quality":[21],"perceived":[24],"by":[25,42,175],"their":[26],"clients.":[27],"The":[28],"predictors":[30,76,95],"proposed":[31],"so":[32],"far":[33],"estimate":[34],"next":[36,88],"a":[39,52,60,91,127],"specific":[40],"user":[41,62],"inspecting":[43],"past":[45,79,198],"individual":[46,197],"trajectories":[47,80],"this":[49,71],"user.":[50,93],"As":[51],"consequence,":[53],"when":[54],"training":[56,104,164],"data":[57,105],"collected":[58,81],"for":[59],"given":[61,92],"is":[63,68,132,141],"limited,":[64],"resulting":[66],"prediction":[67,118],"inaccurate.":[69],"In":[70],"paper,":[72],"we":[73,121],"develop":[74],"cluster-aided":[75,128],"that":[77,148,194],"exploit":[78,196],"from":[82,102],"all":[83],"predict":[86],"These":[94],"rely":[96],"on":[97,134],"clustering":[98],"techniques":[99],"extract":[101],"similarities":[106,151,158],"among":[107],"mobility":[109,154],"patterns":[110],"various":[113],"accuracy.":[119],"Specifically,":[120],"present":[122,161],"CAMP":[123,140,185],"(Cluster-Aided":[124],"Mobility":[125],"Predictor),":[126],"predictor":[129],"whose":[130],"design":[131],"based":[133],"recent":[135],"non-parametric":[136],"bayesian":[137],"statistical":[138],"tools.":[139],"robust":[142],"adaptive":[144],"sense":[147],"it":[149],"exploits":[150],"users'":[153],"only":[155,195],"if":[156],"such":[157],"are":[159],"really":[160],"data.":[165],"We":[166],"analytically":[167],"prove":[168],"consistency":[170],"predictions":[173],"provided":[174],"CAMP,":[176],"investigate":[178],"its":[179],"performance":[180],"using":[181],"two":[182],"large-scale":[183],"datasets.":[184],"significantly":[186],"outperforms":[187],"existing":[188],"predictors,":[189],"particular":[192],"those":[193],"trajectories.":[199]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2016-06-24T00:00:00"}