Journal
IEEE INTERNET COMPUTING
Volume 10, Issue 4, Pages 44-54Publisher
IEEE COMPUTER SOC
DOI: 10.1109/MIC.2006.88
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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 plus a bidding process can help the service-oriented system self-organize.
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