Approximate Information Filtering in Peer-to-Peer Networks

Most approaches to information filtering taken so far have the underlying hypothesis of potentially delivering notifications from every information producer to subscribers. This exact publish/subscribe model creates an efficiency and scalability bottleneck, and might not even be desirable in certain applications. The work presented here puts forward MAPS, a novel approach to support approximate information filtering in a peer-to-peer environment. In MAPS a user subscribes to and monitors only carefully selected data sources, and receives notifications about interesting events from these sources only. This way scalability is enhanced by trading recall for lower message traffic. We define the protocols of a peer-to-peer architecture especially designed for approximate information filtering, and introduce new node selection strategies based on time series analysis techniques to improve data source selection. Our experimental evaluation shows that MAPS is scalable; it achieves high recall by monitoring only few data sources.