Abstract : Top-k queries have two main advantages for peer-to-peer (P2P) data sharing virtual communities. First, they allow participants to rank the results for their queries based on the existing data in the system as well as on their own preferences. Second, they avoid overwhelming participants with too many results. However, existing top-k query processing techniques for P2P systems make users su er from long waiting times. This becomes even more problematic in overloaded P2P systems. In this thesis, we rst revisit the top-k query processing problem and introduce two new measures: the stabilization time and the cumulative quality gap. These two novel measures, in addition to existing measures, allow for better evaluating the behavior of top-k query processing techniques. We then propose a new family of top-k query processing techniques (ASAP) that allows to return high quality results as soon as possible. Finally, we study the problem of top-k query processing in overloaded systems. As a result, we propose a new approach, called QUAT, that relies on synthetic data descriptions of peers in order to allow peers to prioritize queries for which they can provide high quality results.