Research in databases, or more broadly in data management, addresses questions related to efficient and effective access to relevant data from a variety of sources. These sources include traditional databases, as well as other data repositories, distributed data, online data, and continuously generated data (streaming data). The group has expertise in accelerating complex query evaluation by developing efficient algorithmic strategies, designing algorithms that leverage the inherent parallelism of modern CPUs and GPUs, and creating exploratory queries that assist users in expressing their queries and retrieving relevant results. We work with diverse data types, including vector data, graph data, multimedia data, and more. We develop general methods applicable across various domains, while some methods specifically target particular properties such as spatio-temporal ordering or molecular characteristics. Our focus is on creating scalable solutions capable of handling large data volumes.

Data continues to grow, exceeding any humanly manageable quantities. We need systems that support access to relevant data. In order to find relevant data without annoying wait times, we need efficient solutions to retrieve data seamlessly as part of software applications or through standalone requests.

Making good use of available computational resources (which might otherwise go unused) is also important from a sustainability perspective. The same applies to avoiding wasteful computations.

Exploratory queries assist non-expert users in finding data, thereby democratizing data access.