Fast, flexible filtering with phlat
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Meta methods for model sharing in personal information systems
ACM Transactions on Information Systems (TOIS)
A collaborative desktop tagging system for group knowledge management based on concept space
Expert Systems with Applications: An International Journal
Communication-Efficient Classification in P2P Networks
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Adaptive ensemble classification in p2p networks
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
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As the amount of user generated content grows, personal information management has become a challenging problem. Several information management approaches, such as desktop search, document organization and (collaborative) document tagging have been proposed to address this, however they are either inappropriate or inefficient. Automated collaborative document tagging approaches mitigate the problems of manual tagging, but they are usually based on centralized settings which are plagued by problems such as scalability, privacy, etc. To resolve these issues, we present P2PDocTagger, an automated and distributed document tagging system based on classification in P2P networks. P2P-DocTagger minimizes the efforts of individual peers and reduces computation and communication cost while providing high tagging accuracy, and eases of document organization/retrieval. In addition, we provide a realistic and flexible simulation toolkit -- P2PDMT, to facilitate the development and testing of P2P data mining algorithms.