Data Mesh is a new approach to managing, sharing and accessing data at scale that uses decentralisation to overcome the challenges of centralised approaches. Responsibility for the data is shifted to the domains that own the data. The aim of this research project is to investigate approaches for realising the data mesh concept in an industrial context.
Continue reading →Numerical simulations in engineering and science are often costly and time-consuming. While machine learning-based surrogate models offer a solution, they typically require large datasets. This project aims to develop adaptive models that can be repurposed for similar tasks, such as new PDE problems, using minimal data. By leveraging sparse data and techniques like transfer learning, meta-learning and few-shot learning, the project will enhance the efficiency and accuracy of these models, reducing the need for large datasets and enabling faster assessments.
Continue reading →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 →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 →As enterprises shift their business to be data-driven and incorporate initiatives such as industry 4.0, data lakes become increasingly popular as data management platforms for heterogeneous data. However, at the time of this project, data lakes were a new and thus not mature concept with various opposing definitions and only high-level considerations regarding its realization. The guiding question of this research project therefore is as follows: How can a data lake be set up and realized to support the needs of an enterprise?
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