Conference Proceeding Article
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.
Computer Sciences | Databases and Information Systems
Data Management and Analytics
Proceedings of the VLDB Endowment: 36th International Conference on Very Large Data Bases, September 13-17, 2010, Singapore
City or Country
ANG, Hock Hee; Gopalkrishnan, Vivek; NG, Wee Keong; and HOI, Steven C. H..
P2PDocTagger: Content management through automated P2P collaborative tagging. (2010). Proceedings of the VLDB Endowment: 36th International Conference on Very Large Data Bases, September 13-17, 2010, Singapore. 3, (1-2), 1601-1604. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2318
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