Dynamic clustering-based query answering in peer-to-peer systems

In this paper, we propose a new query answering model for P2P applications, which is termed as clustering-based query answering (CBQA). CBQA will retrieve the data objects that are in the same cluster of the query from the global dataset distributed over peers of a P2P system. We first present a framework that support clustering based query answering. Then we give three concrete algorithms for different clustering criteria, namely k-nearest-neighbor, distance-based, and density-based clustering, along with detailed analyses. Finally, implementation issues, especially dynamic neighbors selection to enable the scalability are addressed. Theoretical analysis shows that our method can guarantee to find desirable objects in the interested cluster with modest overhead.