Homogeneous Agent-Based Distributed Information Filtering
Cluster Computing
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
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Information services such as searching, retrieval, and filtering are playing a dominant role in our life during the current information age. One critical functionality of these information services is to obtain effective classification for input documents. Thesaurus(vocabulary)-based document representation followed by clustering constitutes a popular approach to document classification. However, two alternatives exist to construct the information classificationsystem. The first one uses a single, monolithic, huge thesaurus and classifies all documents by one centralized machine. The second one exploits distributed computing environmentsby allowing multiple agents with small thesauri to collaborate with each other over a computer network. The objective of this paper is to compare these two approaches (i.e., single-agent and multi-agent) in terms of various criteria including response time, quality of classification, and economic/privacy considerations. Two experimental studies, involving classification of Computer Science and Medline documents, are presented to compare the performanceof a single-agent system with that of a multi-agent system in real world settings. These results indicate that a collaborative multi-agent system constitutes a attractive methodologyfor classifying a large volume of information efficiently, when the thesaurus is large.