Issue No. 04 - July/August (2006 vol. 10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MIC.2006.88
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.
data mining, privacy, distributed computing, service-oriented architecture
F. C. Tong, J. Liu, H. Wong, X. Zhang, Z. Luo and W. K. Cheung, "Service-Oriented Distributed Data Mining," in IEEE Internet Computing, vol. 10, no. , pp. 44-54, 2006.