Andrea Marcozzi and Gianluca Mazzini, Lepida SpA, Italy

The emerging widespread use of Peer-to-Peer computing is making the P2P Data Mining a natural choice when data sets are distributed over such kind of systems. The huge amount of data stored within the nodes of P2P networks and the bigger and bigger number of applications dealing with them as p2p file-sharing, p2p chatting, p2p electronic commerce etc.., is moving the spotlight on this challenging field. In this paper we give an overview of two different approaches for implementing primitives for P2P Data Mining, trying then to show differences and similarities. The first one is based on the definition of Local algorithms; the second one relies on the Newscast model of computation.

distributetd data mining; local algorithms; gossiping