{"id":"https://openalex.org/W4251944524","doi":"https://doi.org/10.1109/wsc.2018.8632330","title":"Comparison of data analytics approaches using simulation","display_name":"Comparison of data analytics approaches using simulation","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W4251944524","doi":"https://doi.org/10.1109/wsc.2018.8632330"},"language":"en","primary_location":{"id":"doi:10.1109/wsc.2018.8632330","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2018.8632330","pdf_url":null,"source":{"id":"https://openalex.org/S4363607834","display_name":"2018 Winter Simulation Conference (WSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"2018 Winter Simulation Conference (WSC)","raw_type":"proceedings-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/A5021519204","display_name":"Sanjay Jain","orcid":"https://orcid.org/0000-0002-6606-3077"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]},{"id":"https://openalex.org/I4210133369","display_name":"Decision Sciences (United States)","ror":"https://ror.org/03gcvf773","country_code":"US","type":"company","lineage":["https://openalex.org/I4210133369"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sanjay Jain","raw_affiliation_strings":["Department of Decision Sciences, George Washington University, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Decision Sciences, George Washington University, Washington, DC, USA","institution_ids":["https://openalex.org/I193531525","https://openalex.org/I4210133369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000142741","display_name":"Anantha Narayanan","orcid":"https://orcid.org/0000-0002-1577-803X"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anantha Narayanan","raw_affiliation_strings":["Department of Mechanical Engineering, University of Maryland, College Park, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053745197","display_name":"Yung-Tsun Tina Lee","orcid":"https://orcid.org/0000-0002-1134-2001"},"institutions":[{"id":"https://openalex.org/I1321296531","display_name":"National Institute of Standards and Technology","ror":"https://ror.org/05xpvk416","country_code":"US","type":"funder","lineage":["https://openalex.org/I1321296531","https://openalex.org/I1343035065"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yung-Tsun Tina Lee","raw_affiliation_strings":["Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA","institution_ids":["https://openalex.org/I1321296531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5021519204"],"corresponding_institution_ids":["https://openalex.org/I193531525","https://openalex.org/I4210133369"],"apc_list":null,"apc_paid":null,"fwci":2.8846,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.91304348,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1084","last_page":"1095"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9771000146865845,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9771000146865845,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11159","display_name":"Manufacturing Process and Optimization","score":0.9706000089645386,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.940500020980835,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/computer-science","display_name":"Computer science","score":0.7877070903778076},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5764644145965576},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5675766468048096},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5217545628547668},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.4942047894001007},{"id":"https://openalex.org/keywords/data-analysis","display_name":"Data analysis","score":0.48048725724220276},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4682985842227936},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4425390958786011},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.43121036887168884},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4082721769809723},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13817870616912842},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08064469695091248}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7877070903778076},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5764644145965576},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5675766468048096},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5217545628547668},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.4942047894001007},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.48048725724220276},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4682985842227936},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4425390958786011},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.43121036887168884},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4082721769809723},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13817870616912842},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08064469695091248},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wsc.2018.8632330","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc.2018.8632330","pdf_url":null,"source":{"id":"https://openalex.org/S4363607834","display_name":"2018 Winter Simulation Conference (WSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"2018 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4226266853","https://openalex.org/W4210252074","https://openalex.org/W3092201768","https://openalex.org/W2796632413","https://openalex.org/W2740083192","https://openalex.org/W2794907032","https://openalex.org/W4255802207","https://openalex.org/W4299701476","https://openalex.org/W2904574413","https://openalex.org/W2462007151"],"abstract_inverted_index":{"Manufacturers":[0],"need":[1],"to":[2,69,103,107],"quickly":[3],"estimate":[4],"cycle":[5],"times":[6],"for":[7,10,33,46],"incoming":[8],"orders":[9],"promising":[11,111],"delivery":[12],"dates.":[13],"This":[14,80,96],"can":[15,55,65,81],"be":[16,67],"achieved":[17],"by":[18,59,73,129],"using":[19,126],"data":[20,45,76,127],"analytics":[21],"(DA)":[22],"/":[23],"machine":[24],"learning":[25],"(ML)":[26],"approaches.":[27],"Selecting":[28],"the":[29,84,109,137,143,146],"right":[30,42],"DA/ML":[31],"approach":[32,106],"an":[34,105],"application":[35],"is":[36,50,140],"rather":[37],"complex.":[38],"Obtaining":[39],"sufficient":[40],"and":[41,120],"type":[43],"of":[44,86,93,101,136,145],"evaluating":[47],"these":[48],"approaches":[49,89,139],"a":[51,130],"challenge.":[52],"Simulation":[53,63],"models":[54,64,72],"support":[56,108],"this":[57],"process":[58],"generating":[60,74],"synthetic":[61],"data.":[62],"also":[66],"used":[68],"validate":[70],"DA":[71,88,116],"new":[75],"under":[77],"varying":[78],"conditions.":[79],"help":[82],"in":[83,113,142],"evaluation":[85],"alternative":[87],"across":[90],"expected":[91],"range":[92],"operational":[94],"scenarios.":[95],"paper":[97],"reports":[98],"on":[99],"use":[100],"simulation":[102,132],"select":[104],"order":[110],"function":[112],"manufacturing.":[114],"Two":[115],"approaches,":[117],"Neural":[118],"Networks":[119],"Gaussian":[121],"Process":[122],"Regression,":[123],"are":[124],"evaluated":[125],"generated":[128],"manufacturing":[131],"model.":[133],"The":[134],"applicability":[135],"two":[138],"discussed":[141],"context":[144],"selected":[147],"application.":[148]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":3}],"updated_date":"2026-05-01T08:36:08.643496","created_date":"2025-10-10T00:00:00"}