TopX is a highly efficient and effective search engine for XML data that is used, for example, for several tracks of this year's INEX benchmark. However, for some diffcult queries, the results provided by TopX are not yet completely satisfying. Towards the solution of this problem, an extensible framework has been proposed that incorporates feedback from the user to generate a better, expanded query.

This framework has been developed independently of TopX and is not integrated with TopX' user interface. The effectiveness of these approaches has been demonstrated with benchmarks, not with real users. On the other hand, the efficiency of the implementation has never been an issue so far.

The goal of this thesis is a tight integration of the feedback framework and the TopX search engine. This includes several aspects:

1. the extension of the existing, browser-based interface to support explicit relevance feedback (and, as an optional extension, also implicit feedback derived from the user's actions), reevaluating the query when new feedback is available.
2. the modification of the existing TopX engine to support incremental expansion of queries, i.e., if a query that is already evaluated is expanded (based on feedback), the evaluation of the expanded query should reuse the partial results from the evaluation of the original query.
3. optionally, exploration and evaluation of structural feedback methods.
   

TopX and the feedback framework are implemented in Java, hence a thorough knowledge of Java is mandatory for this project.