Keywords: Ruby, EMF Profiles, UML Profiles, Transformations
Astract: Model Engineering gets more important in software development because of the increasing use of models. At the same it is important the adapt and extend existing models. But this is sometimes not possible. For example the model was developed from somebody else or the model is used in another project and it is necessary to keep the compatibility. Therefore several lightweight extension mechanism have been developed. For example UML profile for UML diagrams or EMF profiles for standard diagrams of the Eclipse Modeling Framework (EMF). They allow to extend an already existing model without changing the original one. But unfortunately they have some drawbacks. Only a few transformation languages have a support for lightweight extensions and if they do only very basic. ATL can only access the profile with the underlying Java API. With RubyTL it is not possible to process profiles at all. This thesis covers the development of an extension which enables RubyTL to process EMF and UML profiles. Thereby should the extension be not integrated into the RubyTL code. This will be done with model processors. They integrate the profile into the existing model. Due to the circumstance that the profile is now a complete part of the diagram it is possible that the transformation language can access the stereotypes. Furthermore should it be possible to use the model processors for other transformation languages, like ATL. The goal is to enable the use of UML and EMF profiles also for other transformation languages. But they do not get integrated into the language. The model processors are used from a command line interface (CLI). The feasibility of the approach is demonstrated by using transformations to apply and read profile information in RubyTL and ATL. The resulting ATL transformations are also compared with ATL transformations using the basic ATL support based on the Java API for UML.

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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.