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Astract: OPC Unified Architecture (UA) is a powerful and versatile platform for hosting information from a large variety of domains. In some cases, the domain-specific information models provide overlapping information, such as (i) different views on a specific entity or (ii) different levels of detail of a single entity. Emerging from a multi-disciplinary engineering process, these different views can stem from various tools that have been used to deal with that entity, or from different stages in an engineering process, e.g., from requirements engineering over system design and implementation to operations. In this work, we provide a small but expressive set of OPC UA reference types that unobtrusively allow the persistent instantiation of additional knowledge with respect to relations between OPC UA nodes. We will show the application of these reference types on the basis of a rendezvous of AutomationML and ISA-95 in an OPC UA server.

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Astract: IEC 62264-2 and AutomationML can co-exist as separate views on the same production system, but there is some overlap with respect to the definitions of the entities in IEC 62264-2 and AutomationML. Therefore, a semantic alignment of entities as well as two methods for integration are proposed: (i) tagging AutomationML elements with IEC 622664-2 roles and (ii) referencing external IEC 62264-2 data.

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Astract: Matchmaking and cooperation are interactions widely available within internet-based platforms. However, to start using such functionality, one must first become a member of the platform. In an effort to push matchmaking and cooperation into the protocol stack of the Web, we demonstrate how an existing taxi service is integrated with a decentralized Web based infrastructure, the Web of Needs (WoN), using a chatbot that can be programmed declaratively to enter into certain types of agreements with users. The bot mediates between an existing Web service and users of the RDF-based WoN network.

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Astract: Research in areas of Internet of Things (IoT), Industry 4.0 (I4.0), Cyber Physical Systems (CPS), consider system-of-systems composed of hardware and software systems that seamless work together. Interoperability ranges from compatibility (systems do not disturb other systems, but no interaction between systems) to integration (systems that share a common model/worldview). Between these extremes a continuum of interoperability exists. In any case, interoperability is a model-driven approach - in contrast to data-driven or machine-learning approaches, for example. In general, models contain modules, connections, and behavior descriptions of different types. Integration and interoperability may not only be seen as (goal) states but also as processes. Changes in one system might trigger adaptation in other systems. However, in loose integration and interoperability settings there is more support for the evolution of models.

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Astract: Nowadays, manufacturers are increasingly able to collect and analyze data from sensors on manufacturing equipment, and also from other types of machinery, such as smart meters, pipelines, delivery trucks, etc. Nevertheless, many manufacturers are not yet ready to use analytics beyond a tool to track historical performance. However, just knowing what happened and why it happened does not use the full potential of the data and is not sufficient anymore. Manufacturers need to know what happens next and what actions to take in order to get optimal results. It is a challenge to develop advanced analytics techniques including machine learning and predictive algorithms to transform data into relevant information for gaining useful insights to take appropriate action. In the proposed research we target new analytic methods and tools that make insights not only more understandable but also actionable by decision makers. The latter requires that the results of data analytics have an immediate effect on the processes of the manufacturer. Thereby, data analytics has the potential to improve industrial processes by reducing maintenance costs, avoiding equipment failures and improving business operations. Accordingly, the overall objective of this project is to develop a set of tools - including algorithms, analytic machinery, planning approaches and visualizations - for industrial process improvements based on data analytics.