The focus of the department is on the exploration and development of tools and systems alongside the data life cycle. The main areas of research are (1) the development of NLP methods for automated information extraction from documents, (2) the development of methods to simplify the automated exchange of heterogeneous data between different stake holders, (3) utilization of data outside of their initial acquisition context with the help of semantic metadata descriptions, (4) development of methods and systems to efficiently manage, visualize, and explore large volumes of raster data, timeseries data, and point cloud data in different execution environments and by leveraging modern hardware, and (5) the development of resource-efficient AI methods (i.e., Green AI) for resource-constrained execution environments.
Data is becoming the main driver in an increasing number of areas within both academia and industry. Integrating and leveraging data from both public and private sources leads to new discoveries, enables pioneering developments, and results in innovative products. ...[more]
Information Extraction and Interoperability
Information and data exchange is essential for communication in a wide variety of areas; for example, between companies regarding production data or between research institutions regarding measurement and survey data. ...[more]
Data Access and Processing
Modern database management systems nowadays have to cope with diverse challenges: (1) Data is becoming increasingly heterogeneous and is created in different volumes and velocities, (2) data access patterns are increasingly diverse and interactive , and (3) database management systems have to run in diverse execution environments. ...[more]