  Data mining is used to extract hidden information from large databases. In
Peer-to-Peer context, a challenging problem is how to find the appropriate Peer
to deal with a given query without overly consuming bandwidth. Different
methods proposed routing strategies of queries taking into account the P2P
network at hand. An unstructured P2P system based on an organization of Peers
around Super-Peers that are connected to Super-Super-Peer according to their
semantic domains is considered. This paper integrates Decision Trees in P2P
architectures for predicting Query-Suitable Super-Peers representing a
community of Peers, where one among them is able to answer the given query. In
fact, by analyzing the queries' log file, a predictive model that avoids
flooding queries in the P2P networks constructed by predicting the appropriate
Super-Peer, and hence the Peer to answer the query. The proposed architecture
is based on a Decision Tree (Base-Knowledge - BK). The efficiency of these
architectures is discussed considering architecture without knowledge
(Baseline) using only the flooding queries method to answer queries. The
advantage of this knowledge based model is the robustness in Queries routing
mechanism and scalability in P2P Network.
