As the range of data platforms is rapidly evolving, the goal of this research project is to investigate and prototype upcoming architectures and technologies and to assess their applicability and potentials for industrial enterprises.
Continue reading →Tim Waizenegger: Kubernetes Operator Pattern. Theory and Best Practices Florian Fritz: “Enterprise DevOps” in IBM Cloud Hyper Protect Services Pradeep Parameshwaran: Fully Homomorphic Encryption
Continue reading →IoT-Anwendungen können durch Domänenexperten mithilfe von Entwicklungsumgebungen modellgetrieben entwickelt werden. Beim Betrieb derartiger IoT-Anwendungen können Fehlerfälle auftreten, die nicht durch existierende Monitoringsysteme erkannt werden, z.B. falsch gemessene Sensorwerte. Ziel dieses Projektes ist es, derartige Fehlerfälle mittels Datenvalidierung schon bei der modellgetriebenen Anwendungsentwicklung zu berücksichtigen und so aktiv zu einer verbesserten Fehlertoleranz beizutragen.
Continue reading →This project deals with data characteristics that often occur in industrial use cases. Therefore, it investigates how a targeted data preparation can be used to address such data characteristics. If several of these data characteristics are present in combination, purely data-driven methods are usually not able to address them sufficiently. Therefore, it will be explored how existing domain knowledge of the industry partner can be used in a targeted way to enable more meaningful analysis results. This will then be investigated and evaluated on the basis of real use cases of the industry partner.
Continue reading →Das Ziel des Forschungsprojekts ist es, Datenzustände in einer Blockchain mithilfe von Ad-Hoc-Anfragen effizient zugreifbar zu machen und dabei gleichzeitig die Datenunversehrtheit (d. h. die ursprüngliche Originalität) von gelesenen Daten sicherzustellen. Daher werden im Rahmen dieses Forschungsprojekts effiziente Anfragekonzepte für Blockchain-Datenhistorien entwickelt.
Continue reading →While there are many concepts, techniques and tools for metadata management, most focus on sub-aspects, e.g., metadata management with semantic technologies. There is no common understanding of what comprehensive metadata management in an enterprise entails and how it can be implemented. It is the goal of this project to design concepts and techniques for comprehensive metadata management across the entire enterprise data landscape.
Continue reading →Initiatives like Industry 4.0 generate large amounts of heterogeneous data that need to be stored and managed. It is not always clear what benefits this data will later bring to the company. As a result, it is usually not possible to decide at the time of data collection what value the data will have. To avoid losing any potentially important information, all data are stored in their raw format in an enterprise-wide data lake. The goal of this project was to define a framework for an implementable data lake architecture.
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