Multi-relational and Concept Analysis based Knowledge extraction in the Industry 4.0: A systematic mapping

Autores
Leutwyler, Nicolás; Lezoche, Mario; Torres, Diego; Panetto, Hervé
Año de publicación
2023
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Smart Enterprises, Smart Manufacturing, and Cyber-Physical Systems are gaining traction in many industry areas. On top of that, the amounts of available data grow rapidly, and organizations are eager to exploit their advantages. To accomplish that, it is mandatory to have a wide variety of methods and algorithms for knowledge extraction in order to fit the different needs and problems of the industry. In this study, we review and dissect the current state of the art in knowledge extraction applied to smart enterprises, smart manufacturing, and cyber-physical systems. More specifically, we provide a classification of the characteristics of the available methods in the literature according to their applications, and point out areas of improvement.
Materia
Ciencias de la Computación e Información
Enterprise interoperability
AI-based enterprise systems
Systems interoperability
Cyber physical system
Smart factory
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/12037

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repository_id_str 9441
network_name_str CIC Digital (CICBA)
spelling Multi-relational and Concept Analysis based Knowledge extraction in the Industry 4.0: A systematic mappingLeutwyler, NicolásLezoche, MarioTorres, DiegoPanetto, HervéCiencias de la Computación e InformaciónEnterprise interoperabilityAI-based enterprise systemsSystems interoperabilityCyber physical systemSmart factorySmart Enterprises, Smart Manufacturing, and Cyber-Physical Systems are gaining traction in many industry areas. On top of that, the amounts of available data grow rapidly, and organizations are eager to exploit their advantages. To accomplish that, it is mandatory to have a wide variety of methods and algorithms for knowledge extraction in order to fit the different needs and problems of the industry. In this study, we review and dissect the current state of the art in knowledge extraction applied to smart enterprises, smart manufacturing, and cyber-physical systems. More specifically, we provide a classification of the characteristics of the available methods in the literature according to their applications, and point out areas of improvement.2023-07info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/12037enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:40:02Zoai:digital.cic.gba.gob.ar:11746/12037Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-29 13:40:03.158CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv Multi-relational and Concept Analysis based Knowledge extraction in the Industry 4.0: A systematic mapping
title Multi-relational and Concept Analysis based Knowledge extraction in the Industry 4.0: A systematic mapping
spellingShingle Multi-relational and Concept Analysis based Knowledge extraction in the Industry 4.0: A systematic mapping
Leutwyler, Nicolás
Ciencias de la Computación e Información
Enterprise interoperability
AI-based enterprise systems
Systems interoperability
Cyber physical system
Smart factory
title_short Multi-relational and Concept Analysis based Knowledge extraction in the Industry 4.0: A systematic mapping
title_full Multi-relational and Concept Analysis based Knowledge extraction in the Industry 4.0: A systematic mapping
title_fullStr Multi-relational and Concept Analysis based Knowledge extraction in the Industry 4.0: A systematic mapping
title_full_unstemmed Multi-relational and Concept Analysis based Knowledge extraction in the Industry 4.0: A systematic mapping
title_sort Multi-relational and Concept Analysis based Knowledge extraction in the Industry 4.0: A systematic mapping
dc.creator.none.fl_str_mv Leutwyler, Nicolás
Lezoche, Mario
Torres, Diego
Panetto, Hervé
author Leutwyler, Nicolás
author_facet Leutwyler, Nicolás
Lezoche, Mario
Torres, Diego
Panetto, Hervé
author_role author
author2 Lezoche, Mario
Torres, Diego
Panetto, Hervé
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias de la Computación e Información
Enterprise interoperability
AI-based enterprise systems
Systems interoperability
Cyber physical system
Smart factory
topic Ciencias de la Computación e Información
Enterprise interoperability
AI-based enterprise systems
Systems interoperability
Cyber physical system
Smart factory
dc.description.none.fl_txt_mv Smart Enterprises, Smart Manufacturing, and Cyber-Physical Systems are gaining traction in many industry areas. On top of that, the amounts of available data grow rapidly, and organizations are eager to exploit their advantages. To accomplish that, it is mandatory to have a wide variety of methods and algorithms for knowledge extraction in order to fit the different needs and problems of the industry. In this study, we review and dissect the current state of the art in knowledge extraction applied to smart enterprises, smart manufacturing, and cyber-physical systems. More specifically, we provide a classification of the characteristics of the available methods in the literature according to their applications, and point out areas of improvement.
description Smart Enterprises, Smart Manufacturing, and Cyber-Physical Systems are gaining traction in many industry areas. On top of that, the amounts of available data grow rapidly, and organizations are eager to exploit their advantages. To accomplish that, it is mandatory to have a wide variety of methods and algorithms for knowledge extraction in order to fit the different needs and problems of the industry. In this study, we review and dissect the current state of the art in knowledge extraction applied to smart enterprises, smart manufacturing, and cyber-physical systems. More specifically, we provide a classification of the characteristics of the available methods in the literature according to their applications, and point out areas of improvement.
publishDate 2023
dc.date.none.fl_str_mv 2023-07
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dc.language.none.fl_str_mv eng
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instname_str Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
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repository.name.fl_str_mv CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
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