{"id":"https://openalex.org/W2186346403","doi":"https://doi.org/10.1109/dsaa.2015.7344850","title":"Scalable extraction of timeline information from road traffic data using MapReduce","display_name":"Scalable extraction of timeline information from road traffic data using MapReduce","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2186346403","doi":"https://doi.org/10.1109/dsaa.2015.7344850","mag":"2186346403"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa.2015.7344850","doi":"https://doi.org/10.1109/dsaa.2015.7344850","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa.2015.7344850","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"IEEE International Conference on Data Science and Advanced Analytics (DSAA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5089962580","display_name":"Ardi Imawan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ardi Imawan","raw_affiliation_strings":["Department of Big Data, Pusan National University, Busan, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Big Data, Pusan National University, Busan, South Korea","institution_ids":["https://openalex.org/I4921948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081056095","display_name":"Fadhilah Kurnia Putri","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Fadhilah Kurnia Putri","raw_affiliation_strings":["Department of Big Data, Pusan National University, Busan, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Big Data, Pusan National University, Busan, South Korea","institution_ids":["https://openalex.org/I4921948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077941526","display_name":"Seonga An","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seonga An","raw_affiliation_strings":["Department of Big Data, Pusan National University, Busan, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Big Data, Pusan National University, Busan, South Korea","institution_ids":["https://openalex.org/I4921948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001601713","display_name":"Han\u2010You Jeong","orcid":"https://orcid.org/0000-0002-1074-8464","observed_orcids":["https://orcid.org/0000-0002-1074-8464"]},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Han-You Jeong","raw_affiliation_strings":["Department of Electrical Engineering, Pusan National University, Busan, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Pusan National University, Busan, South Korea","institution_ids":["https://openalex.org/I4921948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081543046","display_name":"Joonho Kwon","orcid":"https://orcid.org/0000-0002-8207-9415","observed_orcids":["https://orcid.org/0000-0002-8207-9415"]},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Joonho Kwon","raw_affiliation_strings":["Department of Big Data, Pusan National University, Busan, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Big Data, Pusan National University, Busan, South Korea","institution_ids":["https://openalex.org/I4921948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4921948"],"apc_list":null,"apc_paid":null,"fwci":0.9881,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.75130272,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10101","display_name":"Cloud Computing and Resource Management","score":0.2870999872684479,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.2870999872684479,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11106","display_name":"Data Management and Algorithms","score":0.20190000534057617,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.20180000364780426,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/road-traffic-data","display_name":"road traffic data","score":0.8859999775886536},{"id":"https://openalex.org/keywords/mapreduce","display_name":"MapReduce","score":0.8629999756813049},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"traffic congestion","score":0.8309999704360962},{"id":"https://openalex.org/keywords/scalability","display_name":"scalability","score":0.824999988079071},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"distributed computing","score":0.9020000100135803},{"id":"https://openalex.org/keywords/intelligent-transportation-systems","display_name":"intelligent transportation systems","score":0.6740000247955322},{"id":"https://openalex.org/keywords/big-data-analytics","display_name":"big data analytics","score":0.6119999885559082}],"concepts":[{"id":"https://openalex.org/C4438859","wikidata":"https://www.wikidata.org/wiki/Q186117","display_name":"Timeline","level":2,"score":0.9304081201553345},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8637521266937256},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.8477474451065063},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.6618620157241821},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.45984116196632385},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42091572284698486},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3945084810256958},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2741953730583191},{"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},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa.2015.7344850","doi":"https://doi.org/10.1109/dsaa.2015.7344850","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa.2015.7344850","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"IEEE International Conference on Data Science and Advanced Analytics (DSAA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.8970999717712402,"display_name":"Industry, innovation and infrastructure"},{"id":"https://metadata.un.org/sdg/11","score":0.8787999749183655,"display_name":"Sustainable cities and communities"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1606805693","https://openalex.org/W1966979702","https://openalex.org/W1980518068","https://openalex.org/W1991770012","https://openalex.org/W2051773470","https://openalex.org/W2075116523","https://openalex.org/W2089465157","https://openalex.org/W2173213060","https://openalex.org/W2542700999"],"related_works":["https://openalex.org/W1606805693","https://openalex.org/W2729421440","https://openalex.org/W205345346","https://openalex.org/W2497298752","https://openalex.org/W2029445945","https://openalex.org/W3009347267","https://openalex.org/W2056552583","https://openalex.org/W2382310345","https://openalex.org/W2894387672","https://openalex.org/W2204739923"],"abstract_inverted_index":{"Due":[0],"to":[1,58,120],"the":[2,28,54,59,76,85,92],"increasing":[3],"number":[4],"of":[5,19,25,63,79,100],"vehicles":[6],"in":[7],"recent":[8],"years,":[9],"traffic":[10,26,51,64,81,142],"congestion":[11],"problem":[12,44],"is":[13],"a":[14,22,42,103,107,132,145],"common":[15],"issue":[16],"for":[17],"residents":[18],"metropolises.":[20],"For":[21],"better":[23],"understanding":[24],"congestion,":[27],"analyzed":[29],"data":[30,33,52],"from":[31,144],"big":[32],"technology":[34],"can":[35,90],"be":[36],"provided":[37],"as":[38,102,111],"timeline":[39,55,77],"information.":[40],"However,":[41],"scalability":[43,93],"would":[45],"occur":[46],"when":[47],"we":[48,69,89],"convert":[49],"raw":[50],"into":[53],"information":[56,78],"due":[57],"volume":[60],"and":[61,106],"complexity":[62],"data.":[65,82],"In":[66],"this":[67],"paper,":[68],"present":[70],"two":[71],"MapReduce-based":[72,138],"approaches":[73,139],"which":[74],"extract":[75],"road":[80],"By":[83],"utilizing":[84],"distribute":[86],"processing":[87],"strategy,":[88],"resolve":[91],"problem.":[94],"We":[95,134],"propose":[96],"an":[97,112],"iterative":[98],"approach":[99,105,110],"MapReduce":[101],"baseline":[104],"single":[108,116],"iteration":[109,117],"efficient":[113],"solution.":[114],"The":[115],"easily":[118],"extended":[119],"support":[121],"various":[122],"and/or":[123],"complex":[124],"analytic":[125],"queries":[126],"by":[127],"providing":[128],"proper":[129],"codes":[130],"at":[131],"reducer.":[133],"validate":[135],"experimentally":[136],"our":[137],"on":[140],"real":[141],"dataset":[143],"Busan":[146],"Intelligent":[147],"Transport":[148],"System":[149],"(ITS)":[150],"center.":[151]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2026-10-06T10:28:23.643775","created_date":"2016-06-24T00:00:00"}