Queries mining for efficient routing in P2P communities

TL;DR

This paper introduces two novel query routing methods for schema-based P2P data-sharing systems that leverage mining algorithms and hypergraph context to significantly improve efficiency and scalability in data retrieval.

Contribution

The paper proposes two new query routing techniques utilizing mining algorithms and hypergraph context to enhance P2P network efficiency and reduce bandwidth consumption.

Findings

Proposed methods outperform baseline in efficiency and scalability.

Experimental results show significant reduction in network traffic.

Routing accuracy to relevant peers is improved.

Abstract

Peer-to-peer (P2P) computing is currently attracting enormous attention. In P2P systems a very large number of autonomous computing nodes (the peers) pool together their resources and rely on each other for data and services. Peer-to-peer (P2P) Data-sharing systems now generate a significant portion of Internet traffic. Examples include P2P systems for network storage, web caching, searching and indexing of relevant documents and distributed network-threat analysis. Requirements for widely distributed information systems supporting virtual organizations have given rise to a new category of P2P systems called schema-based. In such systems each peer exposes its own schema and the main objective is the efficient search across the P2P network by processing each incoming query without overly consuming bandwidth. The usability of these systems depends on effective techniques to find and retrieve data; however, efficient and effective routing of content-based queries is a challenging problem in P2P networks. This work was attended as an attempt to motivate the use of mining algorithms and hypergraphs context to develop two different methods that improve significantly the efficiency of P2P communications. The proposed query routing methods direct the query to a set of relevant peers in such way as to avoid network traffic and bandwidth consumption. We compare the performance of the two proposed methods with the baseline one and our experimental results prove that our proposed methods generate impressive levels of performance and scalability.