Our team is giving a series of lectures and seminars with a focus on enterprise systems design and in-memory data management. Strong links to the industry ensure a close connection between theory and its implementation in the real world.
My Research field is Reinforcement Learning for Revenue Management I investigate improvements regarding stability and data efficiency of reinforcement learning algorithms when used on economic problems in order to allow the practical application of those tools even when strong constraints regarding the available information have to be considered.
Groeneveld, J., Herrmann, J., Mollenhauer, N., Dreessen, L., Bessin, N., Schulze-Tast, J., Kastius, A., Huegle, J., Schlosser, R.: Self-Learning Agents for Recommerce Markets. Business & Information Systems Engineering, accepted. (2023).
Kossmann, J., Kastius, A., Schlosser, R.: SWIRL: Selection of Workload-aware Indexes using Reinforcement Learning. 25th International Conference on Extending Database Technology (EDBT 2022). pp. 155–168 (2022).
"A Course in In-Memory Data Management" by Prof. Dr. h.c. Hasso Plattner. This book is the culmination of six years work of in-memory research. As such, it provides the technical foundation for combined transactional and analytical workloads inside one single database as well as examples of new applications that are now possible given the availability of the new technology. The book is available at Springer.