Publication Type

Conference Proceeding Article

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.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

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

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.14778/1920841.1921049

Comments

Paper presented at 36th International Conference on Very Large Data Bases, September 13-17, 2010, Singapore

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