Service-oriented distributed data mining

Citation data:

IEEE Internet Computing, ISSN: 1089-7801, Vol: 10, Issue: 4, Page: 44-54

Publication Year:
2006
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Citations 27
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Repository URL:
https://repository.hkbu.edu.hk/hkbu_staff_publication/6549
DOI:
10.1109/mic.2006.88
Author(s):
Cheung, W. K.; Zhang, Xian-Feng; Wong, Ho-Fai; Liu, Jiming; Luo, Zong-Wei; Tong, Frank C. H.
Publisher(s):
Institute of Electrical and Electronics Engineers (IEEE)
Tags:
Computer Science; 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; Computer Engineering
article description
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 serviceoriented 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 plus a bidding process can help the service-oriented system self-organize. © 2006 IEEE.