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  4. Alligator: A deductive approach for the integration of industry 4.0 standards
 
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2016
Conference Paper
Title

Alligator: A deductive approach for the integration of industry 4.0 standards

Abstract
Industry 4.0 standards, such as AutomationML, are used to specify properties of mechatronic elements in terms of views, such as electrical and mechanical views of a motor engine. These views have to be integrated in order to obtain a complete model of the artifact. Currently, the integration requires user knowledge to manually identify elements in the views that refer to the same element in the integrated model. Existing approaches are not able to scale up to large models where a potentially large number of conflicts may exist across the different views of an element. To overcome this limitation, we developed Alligator, a deductive rule-based system able to identify conflicts between AutomationML documents. We define a Datalog-based representation of the AutomationML input documents, and a set of rules for identifying conflicts. A deductive engine is used to resolve the conflicts, to merge the input documents and produce an integrated AutomationML document. Our empirica l evaluation of the quality of Alligator against a benchmark of AutomationML documents suggest that Alligator accurately identifies various types of conflicts between AutomationML documents, and thus helps increasing the scalability, efficiency, and coherence of models for Industry 4.0 manufacturing environments.
Author(s)
Grangel-González, Irlán  
Collarana, Diego  
Halilaj, Lavdim  
Lohmann, Steffen  
Lange, Christoph  orcid-logo
Vidal, Maria-Esther  
Auer, Sören  
Mainwork
Knowledge engineering and knowledge management. 20th International Conference, EKAW 2016  
Conference
International Conference on Knowledge Engineering and Knowledge Management (EKAW) 2016  
DOI
10.1007/978-3-319-49004-5_18
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
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