{"id":"https://openalex.org/W2044337911","doi":"https://doi.org/10.1145/2487575.2487707","title":"Inferring distant-time location in low-sampling-rate trajectories","display_name":"Inferring distant-time location in low-sampling-rate trajectories","publication_year":2013,"publication_date":"2013-08-11","ids":{"openalex":"https://openalex.org/W2044337911","doi":"https://doi.org/10.1145/2487575.2487707","mag":"2044337911"},"language":"en","primary_location":{"id":"doi:10.1145/2487575.2487707","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2487575.2487707","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5024176415","display_name":"Meng-Fen Chiang","orcid":null},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Meng-Fen Chiang","raw_affiliation_strings":["National Chiao Tung University, Hsinchu, Taiwan Roc","National Chiao Tung University , Hsinchu, Taiwan, ROC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Chiao Tung University, Hsinchu, Taiwan Roc","institution_ids":["https://openalex.org/I148366613"]},{"raw_affiliation_string":"National Chiao Tung University , Hsinchu, Taiwan, ROC","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026631880","display_name":"Yung-Hsiang Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yung-Hsiang Lin","raw_affiliation_strings":["National Chiao Tung University, Hsinchu, Taiwan Roc","National Chiao Tung University , Hsinchu, Taiwan, ROC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Chiao Tung University, Hsinchu, Taiwan Roc","institution_ids":["https://openalex.org/I148366613"]},{"raw_affiliation_string":"National Chiao Tung University , Hsinchu, Taiwan, ROC","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102958591","display_name":"Wen-Chih Peng","orcid":"https://orcid.org/0000-0001-8964-0933"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Wen-Chih Peng","raw_affiliation_strings":["National Chiao Tung University, Hsinchu, Taiwan Roc","National Chiao Tung University , Hsinchu, Taiwan, ROC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Chiao Tung University, Hsinchu, Taiwan Roc","institution_ids":["https://openalex.org/I148366613"]},{"raw_affiliation_string":"National Chiao Tung University , Hsinchu, Taiwan, ROC","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036357902","display_name":"Philip S. Yu","orcid":"https://orcid.org/0000-0002-3491-5968"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Philip S. Yu","raw_affiliation_strings":["University of Illinois at Chicago, Chicago, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, USA","institution_ids":["https://openalex.org/I39422238"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1741,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.78634713,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1454","last_page":"1457"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9994999766349792,"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":0.9994999766349792,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9994999766349792,"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/T11478","display_name":"Caching and Content Delivery","score":0.996399998664856,"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":0.7917400598526001},{"id":"https://openalex.org/keywords/reachability","display_name":"Reachability","score":0.7621579170227051},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6045956611633301},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5844840407371521},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.5485636591911316},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5385068655014038},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.497571736574173},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.4342584013938904},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3279343545436859},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.24010300636291504},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.1564340889453888},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11967259645462036},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11716717481613159}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7917400598526001},{"id":"https://openalex.org/C136643341","wikidata":"https://www.wikidata.org/wiki/Q1361526","display_name":"Reachability","level":2,"score":0.7621579170227051},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6045956611633301},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5844840407371521},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.5485636591911316},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5385068655014038},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.497571736574173},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.4342584013938904},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3279343545436859},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.24010300636291504},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.1564340889453888},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11967259645462036},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11716717481613159},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"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/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2487575.2487707","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2487575.2487707","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2031674781","https://openalex.org/W2049626361","https://openalex.org/W2062231365","https://openalex.org/W2110455487","https://openalex.org/W2114608211","https://openalex.org/W2117742206","https://openalex.org/W2160642098","https://openalex.org/W2343082343"],"related_works":["https://openalex.org/W2038693912","https://openalex.org/W1991602789","https://openalex.org/W1582396021","https://openalex.org/W1988359706","https://openalex.org/W312558119","https://openalex.org/W4210985407","https://openalex.org/W2335441444","https://openalex.org/W138014004","https://openalex.org/W2075598034","https://openalex.org/W2984441180"],"abstract_inverted_index":{"With":[0],"the":[1,59,69,110,115,130,141,156,170,180,196,212,232,247,271],"growth":[2],"of":[3,61,136,201,225],"location-based":[4],"services":[5],"and":[6,53,199,228],"social":[7],"services,":[8],"low-":[9],"sampling-rate":[10,45],"trajectories":[11,32,104,166],"from":[12,155],"check-in":[13],"data":[14,62,194,221],"or":[15],"photos":[16],"with":[17,152,192,205],"geo-":[18],"tag":[19],"information":[20,29,128],"becomes":[21],"ubiquitous.":[22],"In":[23,134,246],"general,":[24],"most":[25],"detailed":[26],"mov-":[27],"ing":[28],"in":[30,43,64,71,147,164,223,241,260,265],"low-sampling-rate":[31,65,72],"are":[33,51,106],"lost.":[34],"Prior":[35],"works":[36],"have":[37],"elaborated":[38],"on":[39,80,114],"distant-time":[40,89],"location":[41,151,158,188],"prediction":[42,49,78,215,272],"high-":[44],"trajectories.":[46,66],"However,":[47],"existing":[48,213],"models":[50],"pattern-based":[52,214],"thus":[54],"not":[55],"applicable":[56],"due":[57],"to":[58,85,100,109,125,186,268],"sparsity":[60,70,222],"points":[63],"To":[67,174],"address":[68],"trajectories,":[73,117],"we":[74,92,138,178,249],"develop":[75],"a":[76,118,150],"Reachability-based":[77],"model":[79,216],"Time-constrained":[81,119],"Mobility":[82],"Graph":[83,121],"(RTMG)":[84],"predict":[86],"locations":[87,146,163],"for":[88],"queries.":[90],"Specifically,":[91],"de-":[93],"sign":[94],"an":[95],"adaptive":[96,206],"temporal":[97],"exploration":[98,209],"approach":[99],"extract":[101],"effective":[102],"supporting":[103,116,165],"that":[105],"temporally":[107],"close":[108],"query":[111,132],"time.":[112,133],"Based":[113],"mobility":[120,127],"(TG)":[122],"is":[123,167],"constructed":[124],"capture":[126],"at":[129],"given":[131],"light":[135],"TG,":[137],"further":[139,269],"derive":[140],"reacha-":[142],"bility":[143],"probabilities":[144],"among":[145,159],"TG.":[148],"Thus,":[149],"maximum":[153],"reachability":[154],"current":[157],"all":[160],"possi-":[161],"ble":[162],"considered":[168],"as":[169],"predic-":[171],"tion":[172],"result.":[173],"efficiently":[175,238],"process":[176],"queries,":[177],"proposed":[179,233],"index":[181,234],"structure":[182,235],"Sorted":[183],"Interval-Tree":[184],"(SOIT)":[185],"organize":[187],"records.":[189],"Extensive":[190],"experiments":[191],"real":[193],"demonstrated":[195],"effective-":[197],"ness":[198],"efficiency":[200],"RTMG.":[202],"First,":[203],"RTMG":[204,240,252],"tempo-":[207],"ral":[208],"significantly":[210],"outperforms":[211],"HPM":[217],"[2]":[218],"over":[219],"varying":[220],"terms":[224],"higher":[226,229],"accuracy":[227],"coverage.":[230],"Also,":[231],"SOIT":[236],"can":[237],"speedup":[239],"large-scale":[242],"trajec-":[243],"tory":[244],"dataset.":[245],"future,":[248],"could":[250],"extend":[251],"by":[253],"considering":[254],"more":[255],"factors":[256],"(e.g.,":[257],"staying":[258],"durations":[259],"locations,":[261],"application":[262],"us-":[263],"ages":[264],"smart":[266],"phones)":[267],"improve":[270],"accuracy.":[273]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}