Our research is centered around two main goals: scalability of data management solutions as well as data analytics and semantics (see the map below).
With the ever growing amount of data available in enterprises and on the Web and produced by devices and sensors, scalability in terms of data volume, number of server nodes, and number of users has become a significant challenge. In our research, we address this issue by developing new techniques for large-scale and distributed data management systems based on cloud technologies and distributed hash tables as well as by exploiting new hardware technologies for data processing, e.g. huge main memory and new storage media such as solid state disks. Furthermore, we work on approaches for simplifying the administration of database systems by performing system management tasks autonomously, such as physical design tuning.
A second challenge caused by huge amounts of available data is dealing with an information overload either by finding relevant information or combining the data to extract essential information. In our research, we focus on this problems in the context of data which is continuously produced by sensors from both the physical world and the Web. We work, for instance, on techniques for mining data streams as well as analyzing data in sensor networks.
Below you see a word cloud generated from a (preprocessed) selection of our publications using Wordle.
We offer lectures in several programs both at the bachelor and the master level:
- In addition to undergraduate courses such as Database Systems, Database Application Development and Algorithms and Programming,
- We offer courses on advanced topics of data management for CS students, e.g. Database System Implementation, Data Warehousing, Knowledge Discovery in Databases, and Distributed Data Management.