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.

If you are having questions regarding one of our publications, please contact the authors.

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.

Autonomous Data Management

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.

Enterprise Software Engineering

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.

Data-driven Decision Support

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.

Open Positions

We are accepting applications for Ph.D. and post-doc positions from exceptional candidates
We welcome highly motivated Bachelor and Master students to join our research projects

10.12.2021 | Workload-driven, Lazy Discovery of Data Dependencies for Query Opt... @CIDR 2022

Our latest paper by Jan Kossmann, Daniel Lindner, as well as Thorsten Papenbrock and Felix Naumann from the Information … > Go to article

27.08.2021 | New MOOC "Enterprise Goes Cloud" is Live on openSAP

Our new MOOC "Enterprise Goes Cloud" went online this Tuesday on openSAP.com. The sessions of Prof. Plattners lecture … > Go to article

19.07.2021 | Bachelor Project Presented Their Dashboard Built on SAP Graph

We are very proud to share the results of our Bachelor Project team consisting of Jannis Rosenbaum, Tim Kuffner, Jessica … > Go to article

04.05.2021 | "Data dependencies for query optimization: a survey" published in VLDBJ

Our latest paper by Jan Kossmann as well as Thorsten Papenbrock and Felix Naumann from the Information Systems Group … > Go to article

27.04.2021 | Successful PhD Defense of Ralf Teusner on Programming Education in Massive Open

We are happy to announce that Ralf Teusner successfully defended his thesis “Situational Interventions and Peer Feedback … > Go to article

Literature

"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.

Contact

Dr. Michael Perscheid

Chair Representative

Tel.: +49 (331) 5509-566

E-Mail: michael.perscheid(at)hpi.de

Office:

Room: V-2.12

Tel.: +49 (331) 5509-560

Fax: +49 (331) 5509-579

E-Mail: office-epic(at)hpi.de

Contact Details