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 hardware and cloud infrastructure, rethinking software engineering for enterprises, and enabling machine learning for data-driven decision support.

Autonomous Data Management

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.

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

03.06.2022 | COMPSYS 2022 Best Paper Award

Marcel Weisgut, Daniel Ritter, Martin Boissier, and Michael Perscheid received the COMPSYS 2022 Best Paper Award for … > Go to article

28.04.2022 | Paper on EPIC research group published in GI Datenbankspektrum

Our paper “Das Fachgebiet „Enterprise Platform and Integration Concepts“ am Hasso-Plattner-Institut” has been published … > Go to article

05.04.2022 | Paper on Robust Encoding Selection published in PVLDB

Our paper “Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems” has been published in … > Go to article

01.04.2022 | EPIC Group at Dagstuhl Seminar on Database Indexing and Query Processing

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

21.02.2022 | SWIRL: Selection of Workload-aware Indexes using RL accepted @ EDBT 2022

Our latest paper by Jan Kossmann, Alexander Kastius, and Rainer Schlosser proposes to use Reinforcement Learning for … > Go to article

20.12.2021 | ACM Senior Membership Award for Michael Perscheid

We’re happy to share that Michael Perscheid has been awarded with a Senior Membership Award from the Association for … > 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