Benefiting from Data Mining Techniques in a Hybrid Peer-to-Peer Network

Abstract

The fast growing role of peer-to-peer networks in various distributed services and technologies as a well distributed, highly scalable and fault tolerant networking infrastructure together with large number of users inherent to these special networks has introduced P2P systems as a potential domain for data mining processes. Nevertheless, the distributed nature of P2P networks is in explicit contradiction with centralized characteristics of classical data analysis algorithms. Although there has recently been studies on Distributed Data Mining (DDM) as a possible solution, proposed DDM algorithms cover a small portion of the problem space and lack a theoretical proof of convergence. By considering the available potentials in well-known hybrid P2P architectures, we proposed a layered data-gathering and computing infrastructure on top of the hierarchical hybrid P2P networks. These layers provide optional computing and administrational capabilities for the entire network, without interrupting the underlying networkpsilas functionalities.