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  4. The industry 4.0 standards landscape from a semantic integration perspective
 
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2017
Conference Paper
Title

The industry 4.0 standards landscape from a semantic integration perspective

Abstract
Interoperability among actors, sensors, and heterogeneous systems is a crucial factor for realizing the Industry 4.0 vision, i.e., the creation of Smart Factories by enabling intelligent human-to-machine and machine-to-machine cooperation. In order to empower interoperability in Smart Factories, standards and reference architectures have been proposed. Standards allow for the description of components, systems, and processes, as well as interactions among them. Reference architectures classify, align, and integrate industrial standards according to their purposes and features. Industrial communities in Europe, the United States, and Asia have proposed various reference architectures. However, interoperability among analogous standards in these reference architectures is hampered due to different granularity representation of similar processes or production parts. In this paper, we survey the landscape of Industry 4.0 standards from a semantic perspective. To tackle the problem of interoperability between standards, we developed STO, an ontology for describing standards and their relations. Characteristics of I4.0 standards are described using STO, and these descriptions are exploited for classifying standards from different perspectives according to the reference architectures. Moreover, the semantics encoded in STO allows for the discovery of relations between I4.0 standards, and for mappings across reference architectures proposed by different industrial communities.
Author(s)
Grangel-González, Irlán  
Baptista, Paul
Halilaj, Lavdim  
Lohmann, Steffen  
Vidal, Maria-Esther  
Mader, Christian  
Auer, Sören  
Mainwork
ETFA 2017, 22nd IEEE International Conference on Emerging Technologies and Factory Automation  
Conference
International Conference on Emerging Technologies and Factory Automation (ETFA) 2017  
DOI
10.1109/ETFA.2017.8247584
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
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