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A collaboration context ontology to enhance human-related collaboration into Industry 4.0

Li, Siying; Abel, Marie-Hélène; Negre, Elsa (2022), A collaboration context ontology to enhance human-related collaboration into Industry 4.0, Cognition, Technology & Work, 24, 1, p. 75-91. 10.1007/s10111-021-00677-w

Type
Article accepté pour publication ou publié
Date
2022
Journal name
Cognition, Technology & Work
Volume
24
Number
1
Publisher
Springer International Publishing
Pages
75-91
Publication identifier
10.1007/s10111-021-00677-w
Metadata
Show full item record
Author(s)
Li, Siying cc
Heuristique et Diagnostic des Systèmes Complexes [Compiègne] [Heudiasyc]
Abel, Marie-Hélène cc
Heuristique et Diagnostic des Systèmes Complexes [Compiègne] [Heudiasyc]
Negre, Elsa
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
The advent of Industry 4.0 where humans and intelligent machines coexist, allows machines to assist humans on production lines. During such processes, humans work with other humans and/or machines to produce the required products, forming human-related collaborations. Therefore, Industry 4.0 goes beyond a digital ecosystem by being considered as a System of Information Systems which matches heterogeneous systems. This heterogeneity causes poor information interoperability, weakening the effectiveness of collaboration. This poses an issue: how to facilitate human-related collaborations on production lines into Industry 4.0? Addressing it requires better information interoperability and the definition of indicators that can be used to generate recommendations during collaborations. In this article, we focus on indicators of collaboration context and integrate them into a collaboration context ontology to enhance human-related collaborations into Industry 4.0. We then show how to use it in generating context-aware collaborator recommendations.
Subjects / Keywords
Human-related collaboration; Collaboration context ontology; Information interoperability; Industry 4.0; Context-aware recommendation
JEL
M54 - Labor Management

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