Research at Computer Science 9 focuses on the aspects of data management and data exploration. In our modern world, more and more digital information is stored and processed. Telecommunication data, medical diagnostic data, environmental data, gene pools, structures of proteins and digital multimedia data are only a few of many examples for large databases storing complex objects.
Concerning the aspect of data management, we investigate the problem of storing large sets of complex objects in a way that the data can be searched and retrieved very efficiently. Data exploration aims at problems of data mining and knowledge discovery in large databases.
A central question our research group is interested in is how to find and extract the hidden knowledge from large databases. At this point we encounter the problem of Data Mining or Knowledge Discovery in Databases. To establish content based retrieval and similarity search, appropiate data structures are needed to represent the complex objects. Depending on the chosen data model it is possible to develop effective data management techniques while simultaneously achieving interactive response times for queries.
In addition to completely automatic methods, it is necessary to support data mining by interactive techniques. Interactive data mining methods help to improve the results by using visual representations and taking relevance feedback into account in order to include the cognitive abilities of human experts.
In current projects we collaborate with the following industrial partners:
NiSIS is a European Project to coordinate multi-disciplinary studies and research endeavours into the development and utilisation of intelligent paradigms in advanced information systems.The project is funded by the European Commission.
