Efficient Search and Approximate Information Filtering in a Distributed Peer-to-Peer Environment of Digital Libraries

We present a new architecture for efficient search and approximate information filtering in a distributed {P}eer-to-{P}eer ({P2P}) environment of Digital Libraries. The {M}inerva{L}ight search system uses {P2P} techniques over a structured overlay network to distribute and maintain a directory of peer statistics. Based on the same directory, the {MAPS} information filtering system provides an approximate publish/subscribe functionality by monitoring the most promising digital libraries for publishing appropriate documents regarding a continuous query. In this paper, we discuss our system architecture that combines searching and information filtering abilities. We show the system components of {M}inerva{L}ight and explain the different facets of an approximate pub/sub system for subscriptions that is high scalable, efficient, and notifies the subscribers about the most interesting publications in the {P2P} network of digital libraries. We also compare both approaches in terms of common properties and differences to show an overview of search and pub/sub using the same infrastructure