Prof. Dr. h.c. Hasso Plattner

Research Areas

Our research focuses on the technical aspects and design principles of enterprise systems with the goal to maximize performance, cost efficiency, and business value. We focus on optimizing data management on modern in-memory and cloud hardware, rethinking software engineering for enterprises, and enabling machine learning for data-driven decision support.

The trend towards storing more and more data in cloud-based databases requires providers to focus on self-optimization and cost-efficiency while it becomes even harder to understand data and workloads in its entirety. For that reason, we want to build a self-driving and autonomous in-memory database management system that finds (near-)optimal configurations for given workloads and data characteristics automatically.

Data of one business is usually processed by various applications and stored in multiple locations. Bringing this data together often means high integration cost and a laborious development experience. Therefore, we explore current shortcomings in data- and process-integrations to develop solutions narrowing the existing gaps and lowering the required effort to access business data in a unified way.   

Having access to data in a unified way, firms are able to focus on data-driven solutions. However, business accounts for complex dynamics and still should remain explainable. We want to build self-adapting decision support systems, which can (i) reveal the causal effects of certain decisions and (ii) optimize them automatically while balancing their short- and long-term impact. Specifically, our research group investigates how decision problems can be solved using quantitative methods of operations research and data science in order to improve automated decision-making in the areas of revenue management and business analytics.