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
- Institución
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
- OAI Identificador
- oai:digital.cic.gba.gob.ar:11746/12037
Ver los metadatos del registro completo
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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 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
https://digital.cic.gba.gob.ar/handle/11746/12037 |
url |
https://digital.cic.gba.gob.ar/handle/11746/12037 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-sa/4.0/ |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:CIC Digital (CICBA) instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires instacron:CICBA |
reponame_str |
CIC Digital (CICBA) |
collection |
CIC Digital (CICBA) |
instname_str |
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
instacron_str |
CICBA |
institution |
CICBA |
repository.name.fl_str_mv |
CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
repository.mail.fl_str_mv |
marisa.degiusti@sedici.unlp.edu.ar |
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1844618596077010944 |
score |
13.070432 |