The Chair of Data Systems deals with highly scalable, distributed systems for processing large amounts of data.

In many application areas, the analysis of large amounts of data plays an increasing role. Through new methods, such as in machine learning, data can be analyzed more and more effectively and flow into complex models that enable precise predictions. However, data processing requires more and more resources as the amount of data increases, which is a growing problem both in terms of economic and ecological aspects. Our research investigates such new applications and the frameworks used to realize them (e.g., ML frameworks such as TensorFlow or PyTorch). We are working on making data processing more efficient to achieve similar or even better results using fewer resources.

Our research areas are:

• Distributed systems
• Data management
  
• Machine learning
• Graph processing