MAPS: Approximate Publish/Subscribe Functionality in Peer-to-Peer Networks

Information filtering has been a research issue for years. In an information filtering scenario users information needs are expressed by user subscriptions, and users are notified about published documents or events that match these in-terests. The combination of the publish/subscribe scenario with the peer-to-peer (P2P) approach of autonomous peers makes high demands on the scalability and the efficiency of such a given highly distributed network. However, in many cases a subscriber is not interested in all the events that match his profile, but rather in a small representative set. In this paper, we present our approach of an approximate publish/subscribe system, that relaxes the assumption for receiving notifications from every information producer in the network. Our work builds upon distributed hash table technology to create and maintain a distributed global di-rectory that contains information about peers ’ publishing behavior and combines the current peer state and the pre-diction of the future publishing behavior of a peer to store a subscription only to the most promising peers in the net-work. Our experimental evaluation shows that approximate information filtering results satisfying recall level and is able to accommodate changes in peer publishing behaviour