P2P Domain Classification Using Decision Tree

Abstract

The increasing interest in Peer-to-Peer systems (such as Gnutella) has inspired many research activities in this area. Although many demonstrations have been performed that show that the performance of a Peer-to-Peer system is highly dependent on the underlying network characteristics, much of the evaluation of Peer-to-Peer proposals has used simplified models that fail to include a detailed model of the underlying network. This can be largely attributed to the complexity in experimenting with a scalable Peer-to-Peer system simulator built on top of a scalable network simulator. A major problem of unstructured P2P systems is their heavy network traffic. 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. This paper considers an unstructured P2P system based on an organization of peers around Super-Peers that are connected to Super-SuperPeer according to their semantic domains; in addition to integrating Decision Trees in P2P architectures to produce Query-Suitable Super-Peers, representing a community of peers where one among them is able to answer the given query. By analyzing the queries log file, a predictive model that avoids flooding queries in the P2P network is constructed after predicting the appropriate Super-Peer, and hence the peer to answer the query. A challenging problem in a schema-based Peer-to-Peer (P2P) system is how to locate peers that are relevant to a given query. In this paper, architecture, based on (Super-)Peers is proposed, focusing on query routing. The approach to be implemented, groups together (Super-)Peers that have similar interests for an efficient query routing method. In such groups, called Super-Super-Peers (SSP), Super-Peers submit queries that are often processed by members of this group. A SSP is a specific Super-Peer which contains knowledge about: 1. its Super-Peers and 2. The other SSP. Knowledge is extracted by using data mining techniques (e.g. Decision Tree algorithms) starting from queries of peers that transit on the network. The advantage of this distributed knowledge is that, it avoids making semantic mapping between heterogeneous data sources owned by (Super-)Peers, each time the system decides to route query to other (Super-) Peers. The set of SSP improves the robustness in queries routing mechanism, and the scalability in P2P Network. Compared with a baseline approach,the proposal architecture shows the effect of the data mining with better performance in respect to response time and precision.