Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2010

Abstract

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.

Keywords

Collaborative tagging, Content management, Data mining algorithm, Personal information management, P2P network

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the VLDB Endowment: 36th International Conference on Very Large Data Bases, September 13-17, 2010, Singapore

Volume

3

Issue

1-2

First Page

1601

Last Page

1604

ISSN

1066-8888

Identifier

10.14778/1920841.1921049

Publisher

VLDB Endowment

City or Country

Stanford, CA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.14778/1920841.1921049

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