Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Service-oriented distributed data mining

Language

English

Abstract

Data mining research currently faces two great challenges: how to embrace data mining services with just-in-time and autonomous properties and how to mine distributed and privacy-protected data. To address these problems, the authors adopt the business process execution language for Web services in a service-oriented distributed data mining (DDM) platform to choreograph DDM component services and fulfil global data mining requirements. They also use the learning-from-abstraction methodology to achieve privacy-preserving DDM. Finally, they illustrate how localized autonomy on privacy-policy enforcement plus a bidding process can help the service-oriented system self-organize.

Keywords

service-oriented architecture, data mining, privacy, distributed computing, Data mining, Distributed decision making, Data privacy, Data analysis, Performance analysis, Web services, Algorithm design and analysis, Computer architecture, Production systems, Data communication

Publication Date

8-2006

Source Publication Title

IEEE Internet Computing

Volume

10

Issue

4

Start Page

44

End Page

54

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Peer Reviewed

1

Funder

Research Grant Council Central Allocation HKBU 2/03C partially supports this work.

DOI

10.1109/MIC.2006.88

Link to Publisher's Edition

http://dx.doi.org/10.1109/MIC.2006.88

ISSN (print)

10897801

ISSN (electronic)

19410131

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