The trend of enterprises storing more and more data in database systems requires providers to focus on self-optimization and cost-efficiency to stay competitive. Therefore, our research group investigates approaches for autonomous physical database design optimization and exploitation of cloud resource elascitiy.

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.