{"id":"https://openalex.org/W2203010770","doi":"https://doi.org/10.1109/bigdata.2015.7363864","title":"Semantics for Big Data access & integration: Improving industrial equipment design through increased data usability","display_name":"Semantics for Big Data access & integration: Improving industrial equipment design through increased data usability","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2203010770","doi":"https://doi.org/10.1109/bigdata.2015.7363864","mag":"2203010770"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2015.7363864","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7363864","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","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/A5055810120","display_name":"Jenny Weisenberg Williams","orcid":null},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jenny Weisenberg Williams","raw_affiliation_strings":["Knowledge Discovery Lab, GE Global Research, Niskayuna, NY, USA"],"affiliations":[{"raw_affiliation_string":"Knowledge Discovery Lab, GE Global Research, Niskayuna, NY, USA","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036164078","display_name":"Paul Cuddihy","orcid":"https://orcid.org/0000-0002-8653-4390"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Paul Cuddihy","raw_affiliation_strings":["Knowledge Discovery Lab, GE Global Research, Niskayuna, NY, USA"],"affiliations":[{"raw_affiliation_string":"Knowledge Discovery Lab, GE Global Research, Niskayuna, NY, USA","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016713102","display_name":"Justin McHugh","orcid":null},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Justin McHugh","raw_affiliation_strings":["Knowledge Discovery Lab, GE Global Research, Niskayuna, NY, USA"],"affiliations":[{"raw_affiliation_string":"Knowledge Discovery Lab, GE Global Research, Niskayuna, NY, USA","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050284653","display_name":"Kareem S. Aggour","orcid":"https://orcid.org/0000-0002-7467-3836"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kareem S. Aggour","raw_affiliation_strings":["Knowledge Discovery Lab, GE Global Research, Niskayuna, NY, USA"],"affiliations":[{"raw_affiliation_string":"Knowledge Discovery Lab, GE Global Research, Niskayuna, NY, USA","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040348130","display_name":"Arvind Menon","orcid":"https://orcid.org/0009-0009-3895-6108"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arvind Menon","raw_affiliation_strings":["Combustion Control and Methods, GE Power & Water, Greenville, SC, USA"],"affiliations":[{"raw_affiliation_string":"Combustion Control and Methods, GE Power & Water, Greenville, SC, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109278826","display_name":"Steven Gustafson","orcid":null},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Steven M. Gustafson","raw_affiliation_strings":["Knowledge Discovery Lab, GE Global Research, Niskayuna, NY, USA"],"affiliations":[{"raw_affiliation_string":"Knowledge Discovery Lab, GE Global Research, Niskayuna, NY, USA","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089211670","display_name":"Timothy M. Healy","orcid":"https://orcid.org/0000-0002-1880-6913"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Timothy Healy","raw_affiliation_strings":["Combustion Control and Methods, GE Power & Water, Greenville, SC, USA"],"affiliations":[{"raw_affiliation_string":"Combustion Control and Methods, GE Power & Water, Greenville, SC, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5055810120"],"corresponding_institution_ids":["https://openalex.org/I4210134512"],"apc_list":null,"apc_paid":null,"fwci":2.0031,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.87991611,"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":"1103","last_page":"1112"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"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/T11719","display_name":"Data Quality and Management","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"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/T11986","display_name":"Scientific Computing and Data Management","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"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/T10215","display_name":"Semantic Web and Ontologies","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.8119350671768188},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.6873162984848022},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.485710471868515},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4830389618873596},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.447950541973114},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.4393695890903473},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.42860496044158936},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.3628459572792053},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3558697998523712},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.11191654205322266}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8119350671768188},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.6873162984848022},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.485710471868515},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4830389618873596},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.447950541973114},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.4393695890903473},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.42860496044158936},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.3628459572792053},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3558697998523712},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.11191654205322266},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2015.7363864","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7363864","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W315337712","https://openalex.org/W1529207010","https://openalex.org/W1888438911","https://openalex.org/W1969483458","https://openalex.org/W1973943275","https://openalex.org/W1987544386","https://openalex.org/W2012023115","https://openalex.org/W2066757687","https://openalex.org/W2069266360","https://openalex.org/W2110086534","https://openalex.org/W2125190835","https://openalex.org/W2151813316","https://openalex.org/W2154577614","https://openalex.org/W3151173849","https://openalex.org/W6631819344"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W4233347783","https://openalex.org/W2910064364","https://openalex.org/W3094550016","https://openalex.org/W2982650128"],"abstract_inverted_index":{"With":[0],"the":[1,113,138,146,165,206],"advent":[2],"of":[3,48,59,167,243],"Big":[4,132,169],"Data":[5,133,170],"technologies,":[6,67],"organizations":[7],"can":[8,25],"efficiently":[9],"store":[10],"and":[11,154,186,205,246,264],"analyze":[12],"more":[13],"data":[14,24,60,85,92,102,118,183,250],"than":[15],"ever":[16],"before.":[17],"However,":[18],"extracting":[19],"maximal":[20],"value":[21],"from":[22,52],"this":[23],"be":[26,62,74],"challenging":[27],"for":[28,44,228],"many":[29],"reasons.":[30],"For":[31],"example,":[32],"datasets":[33,68,143,204,219],"are":[34,70,88,106],"often":[35,89,137,157],"not":[36],"stored":[37,64,75],"using":[38,65,220],"human-understandable":[39],"terms,":[40],"making":[41],"it":[42,135],"difficult":[43],"a":[45,130,152,168,181,187,211],"large":[46],"set":[47],"users":[49],"to":[50,100,112,148,159,179,190,197,215,251,257],"benefit":[51],"them.":[53],"Further,":[54],"given":[55],"that":[56,69,140],"different":[57,66,101],"types":[58],"may":[61,73],"best":[63],"closely":[71],"related":[72],"separately":[76],"with":[77,173,210],"no":[78],"explicit":[79],"linkage.":[80],"Finally,":[81],"even":[82,128],"within":[83,129],"individual":[84],"stores,":[86],"there":[87],"inconsistencies":[90],"in":[91,151,259,262],"representations,":[93],"whether":[94],"introduced":[95],"over":[96,241],"time":[97],"or":[98],"due":[99],"producers.":[103],"These":[104],"challenges":[105],"further":[107],"compounded":[108],"by":[109,124],"frequent":[110],"additions":[111],"data,":[114],"including":[115],"new":[116],"raw":[117],"as":[119,121],"well":[120],"results":[122],"produced":[123,240],"large-scale":[125],"analytics.":[126],"Thus,":[127],"single":[131],"environment,":[134],"is":[136,255],"case":[139],"multiple":[141],"rich":[142],"exist":[144],"without":[145],"means":[147],"access":[149,184],"them":[150],"unified":[153,182],"cohesive":[155],"way,":[156],"leading":[158],"lost":[160],"value.":[161],"This":[162,223],"paper":[163],"describes":[164],"development":[166],"management":[171],"infrastructure":[172],"semantic":[174],"technologies":[175,194],"at":[176],"its":[177],"core":[178],"provide":[180],"layer":[185],"consistent":[188],"approach":[189],"analytic":[191],"execution.":[192],"Semantic":[193],"were":[195],"used":[196],"create":[198],"domain":[199],"models":[200],"describing":[201],"mutually":[202],"relevant":[203],"relationships":[207],"between":[208],"them,":[209],"graphical":[212],"user":[213],"interface":[214],"transparently":[216],"query":[217],"across":[218],"domain-model":[221],"terms.":[222],"prototype":[224,248],"system":[225,254],"was":[226],"built":[227],"GE":[229],"Power":[230,233],"&":[231],"Water's":[232],"Generation":[234],"Products":[235],"Engineering":[236],"Division,":[237],"which":[238],"has":[239],"50TB":[242],"gas":[244],"turbine":[245],"component":[247],"test":[249],"date.":[252],"The":[253],"expected":[256],"result":[258],"significant":[260],"savings":[261],"productivity":[263],"expenditure.":[265]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}