Data mining effect in peer-to-peer queries routing
Résumé
Data mining has been 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. We consider an unstructured P2P system based on an organization of peers around super-peers that are connected to meta-super-peer according to their semantic domains. 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, we construct a predictive model that avoids flooding queries in the p2p network by 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 with respect to a given query. In this paper, we propose an architecture, based on (super-) peers, and we focus on query routing. Our approach considers that (super-) peers having similar interests are grouped together for an efficient query routing method. In such groups, called Meta-Super-Peers (MSP), super-peers submit queries that are often processed by members of this group. A MSP is a specific super-peer which contains knowledge about: 1. its super-peers and 2. The others MSP. 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 MSP improves the robustness in queries routing mechanism and scalability in P2P Network. Compared with a baseline approach, our proposal architectures show the effect of the data mining with better performance with respect to response time and precision.