{"id":"https://openalex.org/W2914534520","doi":"https://doi.org/10.1109/bigdata.2018.8622297","title":"A Generic and Scalable Pipeline for Large-Scale Analytics of Continuous Aircraft Engine Data","display_name":"A Generic and Scalable Pipeline for Large-Scale Analytics of Continuous Aircraft Engine Data","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2914534520","doi":"https://doi.org/10.1109/bigdata.2018.8622297","mag":"2914534520"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2018.8622297","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622297","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 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/A5027132281","display_name":"Florent Forest","orcid":"https://orcid.org/0000-0001-6878-8752"},"institutions":[{"id":"https://openalex.org/I4210165303","display_name":"Safran (France)","ror":"https://ror.org/05sqf9v67","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210165303"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Florent Forest","raw_affiliation_strings":["Safran Aircraft Engines, Moissy-Cramayel, France"],"affiliations":[{"raw_affiliation_string":"Safran Aircraft Engines, Moissy-Cramayel, France","institution_ids":["https://openalex.org/I4210165303"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063545602","display_name":"J\u00e9r\u00f4me Lacaille","orcid":"https://orcid.org/0000-0003-0743-025X"},"institutions":[{"id":"https://openalex.org/I4210165303","display_name":"Safran (France)","ror":"https://ror.org/05sqf9v67","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210165303"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Jerome Lacaille","raw_affiliation_strings":["Safran Aircraft Engines, Moissy-Cramayel, France"],"affiliations":[{"raw_affiliation_string":"Safran Aircraft Engines, Moissy-Cramayel, France","institution_ids":["https://openalex.org/I4210165303"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023422312","display_name":"Mustapha Lebbah","orcid":"https://orcid.org/0000-0001-7245-6371"},"institutions":[{"id":"https://openalex.org/I4210156583","display_name":"Laboratoire d'Informatique de Paris-Nord","ror":"https://ror.org/05g1zjw44","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210091279","https://openalex.org/I4210156583","https://openalex.org/I4210159245"]},{"id":"https://openalex.org/I4210091279","display_name":"Universit\u00e9 Sorbonne Paris Nord","ror":"https://ror.org/0199hds37","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210091279"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Mustapha Lebbah","raw_affiliation_strings":["LIPN, Universit\u00e9 Paris 13, Villetaneuse, France"],"affiliations":[{"raw_affiliation_string":"LIPN, Universit\u00e9 Paris 13, Villetaneuse, France","institution_ids":["https://openalex.org/I4210156583","https://openalex.org/I4210091279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113698150","display_name":"Hanene Azzag","orcid":null},"institutions":[{"id":"https://openalex.org/I4210091279","display_name":"Universit\u00e9 Sorbonne Paris Nord","ror":"https://ror.org/0199hds37","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210091279"]},{"id":"https://openalex.org/I4210156583","display_name":"Laboratoire d'Informatique de Paris-Nord","ror":"https://ror.org/05g1zjw44","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210091279","https://openalex.org/I4210156583","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Hanene Azzag","raw_affiliation_strings":["LIPN, Universit\u00e9 Paris 13, Villetaneuse, France"],"affiliations":[{"raw_affiliation_string":"LIPN, Universit\u00e9 Paris 13, Villetaneuse, France","institution_ids":["https://openalex.org/I4210156583","https://openalex.org/I4210091279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5027132281"],"corresponding_institution_ids":["https://openalex.org/I4210165303"],"apc_list":null,"apc_paid":null,"fwci":0.6515,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.77983658,"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":"1918","last_page":"1924"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9829999804496765,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9829999804496765,"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"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9794999957084656,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9769999980926514,"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/spark","display_name":"SPARK (programming language)","score":0.7919396162033081},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7728005647659302},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7159318327903748},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6578147411346436},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.6014890670776367},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5756515860557556},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.49414995312690735},{"id":"https://openalex.org/keywords/data-visualization","display_name":"Data visualization","score":0.4397360682487488},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.41842490434646606},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3856593370437622},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.23316127061843872},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.19620370864868164},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.10668012499809265}],"concepts":[{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.7919396162033081},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7728005647659302},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7159318327903748},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6578147411346436},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.6014890670776367},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5756515860557556},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.49414995312690735},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.4397360682487488},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.41842490434646606},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3856593370437622},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.23316127061843872},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.19620370864868164},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.10668012499809265},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2018.8622297","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622297","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.49000000953674316,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W65738273","https://openalex.org/W146387788","https://openalex.org/W1697947569","https://openalex.org/W1990517717","https://openalex.org/W2000957129","https://openalex.org/W2020144903","https://openalex.org/W2060308201","https://openalex.org/W2072310234","https://openalex.org/W2088709281","https://openalex.org/W2107636931","https://openalex.org/W2135046866","https://openalex.org/W2137496824","https://openalex.org/W2153796935","https://openalex.org/W2160499362","https://openalex.org/W2163483784","https://openalex.org/W2172220707","https://openalex.org/W2173213060","https://openalex.org/W2409433313","https://openalex.org/W2592467372","https://openalex.org/W2598678042","https://openalex.org/W2608133416","https://openalex.org/W2782742812","https://openalex.org/W3144916094","https://openalex.org/W6606058172","https://openalex.org/W6655564063","https://openalex.org/W6668390860","https://openalex.org/W6680398621","https://openalex.org/W6734227256","https://openalex.org/W6736913509","https://openalex.org/W6747676321","https://openalex.org/W6792677905","https://openalex.org/W6929463649"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4394895745","https://openalex.org/W2960264696","https://openalex.org/W2766461310","https://openalex.org/W4247566972","https://openalex.org/W4388692845","https://openalex.org/W3202731209","https://openalex.org/W3211874991","https://openalex.org/W2799508461","https://openalex.org/W3191926225"],"abstract_inverted_index":{"A":[0],"major":[1],"application":[2],"of":[3,21,41,57,64,91,93,117,130],"data":[4,26,42,59],"analytics":[5,56],"for":[6,54,125,156,176],"aircraft":[7,65],"engine":[8,11,157],"manufacturers":[9],"is":[10,153,167],"health":[12,69],"monitoring,":[13],"which":[14],"consists":[15],"in":[16,134,178],"improving":[17],"availability":[18],"and":[19,27,39,51,75,86,110,127,140,174],"operation":[20],"engines":[22],"by":[23],"leveraging":[24],"operational":[25,58],"past":[28],"events.":[29],"Traditional":[30],"tools":[31],"can":[32,112],"no":[33],"longer":[34],"handle":[35],"the":[36,104,131],"increasing":[37],"volume":[38],"velocity":[40],"collected":[43],"on":[44,73,96],"modern":[45],"aircraft.":[46],"We":[47],"propose":[48],"a":[49,61,97,143,154,161],"generic":[50],"scalable":[52],"pipeline":[53,166],"large-scale":[55],"from":[60,89],"recent":[62],"type":[63],"engine,":[66],"oriented":[67],"towards":[68],"monitoring":[70,159],"applications.":[71],"Based":[72],"Hadoop":[74],"Spark,":[76],"our":[77,151],"approach":[78],"enables":[79],"domain":[80],"experts":[81],"to":[82,136,169],"scale":[83],"their":[84],"algorithms":[85,111,122],"extract":[87],"features":[88],"tens":[90],"thousands":[92],"flights":[94],"stored":[95],"cluster.":[98],"All":[99],"computations":[100],"are":[101,123],"performed":[102],"using":[103],"Spark":[105],"framework,":[106],"however":[107],"custom":[108],"functions":[109],"be":[113,170],"integrated":[114,124],"without":[115],"knowledge":[116],"distributed":[118],"programming.":[119],"Unsupervised":[120],"learning":[121],"clustering":[126],"dimensionality":[128],"reduction":[129],"flight":[132],"features,":[133],"order":[135],"allow":[137],"efficient":[138],"visualization":[139],"interpretation":[141],"through":[142],"dedicated":[144],"web":[145],"application.":[146],"The":[147],"use":[148,177],"case":[149],"guiding":[150],"work":[152],"methodology":[155],"fleet":[158],"with":[160],"self-organizing":[162],"map.":[163],"Finally,":[164],"this":[165],"meant":[168],"end-to-end,":[171],"fully":[172],"customizable":[173],"ready":[175],"an":[179],"industrial":[180],"setting.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
