Prof. Dr. h.c. mult. 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:


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

Elastic Query Processing on Serverless Cloud Infrastructure

Enterprises increasingly run their analytical workloads in cloud environments, where they must provision both compute and storage resources before any query processing can begin. Infrastructure provisioning, however, can be difficult for these workloads because they are often unpredictable and ad-hoc in nature. To avoid underprovisioning and consequent performance disruption, cloud customers overprovision their resources conservatively, giving up on cost efficiency. Recently, cloud providers introduced means to allocate and bill fine-granular units of resources with function as a service (FaaS) platforms and shared cloud storage. We are building an elastic query processor called Skyrise that embraces this so called serverless cloud infrastructure for improved cost characteristics. In the context of the Skyrise project, we offer Master's thesis topics in the following broad areas:

  • Query optimization
  • Query execution

Contact: Thomas Bodner