Efficient Local Algorithms for Distributed Data Mining in Large Scale Peer to Peer Environments: A Deterministic Approach

Peer-to-peer (P2P) systems such as Gnutella, Napster, e-Mule, Kazaa, and Freenet are increasingly becoming popular for many applications that go beyond downloading music files without paying for it. Examples include P2P systems for network storage, web caching, searching and indexing of relevant documents and distributed network-threat analysis. These environments are rich in data and this data, if mined, can provide valuable source of information. Mining the web cache of users, for example, may often give information about their browsing patterns leading to efficient searching, resource utilization, query routing and more. However, most of the off-the-shelf data analysis techniques are designed for centralized applications where the entire data is stored in a single location. These techniques do not work in a highly decentralized, distributed environment such as a P2P network. We need distributed data mining algorithms that are fundamentally local, scalable, decentralized, asynchronous and anytime to solve this problem. This research proposes em>DeFraL