In modern information systems, the sheer amount as well as the complexity of the stored data are increasing at a high rate. To search and analyze these large amounts of complex objects, Data Mining and Complex Search, such as similarity search, offer new possibilities to maximize the utilization of available information.

Among other topics, our group is well-known for the development of various algorithms and access structures for high-dimensional indexing like the R*-tree, X-Tree or the IQ-Tree. In the area of data mining, the members of our group contributed various algorithms and methods such as DBSCAN, OPTICS or LOF which are among the state-of-the-art solutions for clustering and outlier detection.

Some of our current research projects are:

• Clustering and Outlier Detection in high-dimensional data
• Query Processing in Uncertain Databases
• Efficient Similarity Search and Data Mining in Medical Image Data
• Database and Search Technologies in Automotive Environments