Hasso-Plattner-Institut
Prof. Dr. h.c. Hasso Plattner
  
 

Master Thesis Topic Areas and General Information

We are happy that you are interested in writing a master thesis at our research group!

Please find our list of available master's thesis topics below. Should you be interested in any of those topics please feel free to contact the responsible research assistant for further information.

In case you have an idea for an individual topic that you think fits into our research areas, also do not hesitate to get in touch with us. We are open for suggestions!

In general, the process for finding, working on, and finishing a successful master thesis  in our research group looks as follows:

  1. Get in touch with us and discuss a potential topic and potential advisors
    (browsing the pages of our people section is a good start, stopping by is even better)
  2. Further specify the topic and start sketching out ideas
  3. Write a short research exposé (usually between 3 and 5 pages)
  4. Upon "acceptance", official registration of your master thesis and begin of the 6-month working period
  5. Regular meetings with your advisor(s)
  6. Mid-term presentation at our research group meeting (approx. 15 minutes + QA)
  7. Further ongoing meetings with your advisor(s)
  8. Submit your thesis
  9. Shortly after, thesis defense at our research group meeting (approx. 30 minutes + QA)

 


 

Currently we are offering theses in the two research areas In-Memory Datamanagement and Data-Driven Decision Making. To find out more about the specific topics, please follow the links below.

 

In-Memory Data Management

  • Transactional Optimizations for Hyrise
  • Autonomous Database Systems
  • Workload-driven Replication
  • Optimized Data Structures for In-Memory Trajectory Data
  • Enterprise Streaming Benchmark - Result Validation

 

Data-Driven Decision-Making

  • Causal Structure Learning in Heterogeneous Settings
  • Parallel Execution Strategies for Causal Structure Learning
  • Data-Driven Decision-Making in Dynamic Pricing / Index Selection
  • Data-Informed Agile Retrospectives
  • Combining Machine Learning and External Knowledge for Analyzing Gene Expression Profiles