Prof. Dr. h.c. Hasso Plattner

Open Master Theses in Autonomous Data Management

We are looking for interested students to tackle the following master thesis topics in the area of in-memory data management:


Autonomous Database Systems

Increasing volumes of data, varying workloads, and complex systems make database administration increasingly challenging for human database administrators. Autonomous or self-driving database systems utilize their knowledge of processed workloads, the stored data, and other runtime information to support database administrators in their tasks or to optimize the system's configuration without any human intervention. For example, such systems are capable of selecting indexes that substantially improve the system's runtime performance.

There are various challenges regarding autonomous approaches: (i) achieving robust and efficient optimization by relying on heuristics, optimization, or machine learning methods; (ii) integrating such approaches into database systems with acceptable implementation and runtime overhead; and (iii) mitigating trust issues of database administrators and users that are caused by non-explainable decisions made by autonomous systems.

There are several potential topics for master's theses available in the areas above.

Contact: Jan Kossmann


Workload-driven Replication

In replication schemes, replica nodes process queries on snapshots of the master. By analyzing the workload, we can identify query access patterns and replicate data depending to its access frequencies.  We offer to investigate how to optimize individual replication nodes in scale-out scenarios,

  • e.g., to lower the overall memory footprint by partial replication,
  • or to increase the analytical throughput by specialized indexes.

Contact: Stefan Halfpap