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キーワード:
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要旨:
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
interests. 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 directory that contains information about
peers' publishing behavior and combines the current peer state and the
prediction of the future publishing behavior of a peer to store a subscription
only to the most promising peers in the network. Our experimental evaluation
shows that approximate information filtering results satisfying recall level
and is able to accommodate changes in peer publishing behaviour.
