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.
The Web Science group focuses on various topics related to the Web, such as Information Retrieval, Natural Language Processing, Data Mining, Knowledge Discovery, Social Network Analysis, Entity Linking, and Recommender Systems. The group is particularly interested in Text Mining to deal with the vast amount of unstructured and semi-structured information available on the Web.
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.
Similarity search refers to the task of finding objects that are similar to a given query in a set of objects. Common DBMS only provide means to efficiently find exact matches to a given query. In case of typing errors, omitted or transposed attribute values or other typical data quality problems in queries, exact search algorithms fail to find all relevant objects in the queried data set.
In this project, we survey existing and develop new algorithms for effective and efficient similarity search. Effective similarity search can be achieved by defining a similarity measure that is well-suited for the given domain. For efficient similarity search, an index structure is required that precomputes similarities of objects to answer queries as fast as possible.