For bachelor students we offer German lectures on database systems in addition with paper- or project-oriented seminars. Within a one-year bachelor project students finalize their studies in cooperation with external partners. For master students we offer courses on information integration, data profiling, search engines and information retrieval enhanced by specialized seminars, master projects and advised master theses.
Most of our research is conducted in the context of larger research projects, in collaboration across students, across groups, and across universities. We strive to make available most of our data sets and source code.
Detecting duplicates is a problem with a long tradition in many domains, such as customer relationship management and data warehousing. The problem is twofold: First define a suitable similarity measure, and second efficiently apply the measure to all pairs of objects. With the advent and pervasion of the XML data model, it is necessary to find new similarity measures and to develop efficient methods to detect duplicate elements in nested XML data.
A classical approach to duplicate detection in flat relational data is the sorted neighborhood method, which draws its efficiency from sliding a window over the relation and comparing only tuples within that window. We extend the algorithm to cover not only a single relation but nested XML elements. To compare objects we make use of XML parent and child relationships. For efficiency, we apply the windowing technique in a bottom-up fashion, detecting duplicates at each level of the XML hierarchy. Experiments show a speedup comparable to the original method data and they show the high effectiveness of our algorithm in detecting XML duplicates. [more]