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Service-Oriented Distributed Data Mining
July/August 2006 (vol. 10 no. 4)
pp. 44-54
William K. Cheung, Hong Kong Baptist University
Xiao-Feng Zhang, Hong Kong Baptist University
Ho-Fai Wong, Hong Kong Baptist University
Jiming Liu, Hong Kong Baptist University
Zong-Wei Luo, E-Business Technology Institute, University of Hong Kong
Frank C.H. Tong, E-Business Technology Institute, University of Hong Kong
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 fulfill 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 plusa bidding process can help the service-oriented system self-organize.
Index Terms:
data mining, privacy, distributed computing, service-oriented architecture
Citation:
William K. Cheung, Xiao-Feng Zhang, Ho-Fai Wong, Jiming Liu, Zong-Wei Luo, Frank C.H. Tong, "Service-Oriented Distributed Data Mining," IEEE Internet Computing, vol. 10, no. 4, pp. 44-54, July-Aug. 2006, doi:10.1109/MIC.2006.88
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