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.