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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.

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Astract: OPC Unified Architecture (UA) is a powerful technology for modeling and instantiating domain-specific information in a standardized manner. Its initial application scenario is in the domain of automated production systems, that increasingly have to deal with variability information, (i) regarding the products being manufactured and (ii) regarding the production systems themselves. In this work we propose a non-intrusive OPC UA information model for the modeling and querying of variability information using feature models, which are a well-known paradigm in the management of software product lines. Our information model can be applied "aside" existing domain information without interfering with their internal structure.

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Astract: Resource-Event-Agent (REA) has been applied to various engineering and business domains, with a focus on transfer activities rather than transformation activities. In the context of smart manufacturing, vertical integration of IT systems (e.g., business applications and production control systems) is a key factor. In this work, we shed light on the integration of REA concepts into production environments by investigating properties of REA transformations and aligning them with concepts from an international standard for enterprise-control system integration (ISA-95).