Department of Computer Science
Service-oriented distributed data mining
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.
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
Source Publication Title
IEEE Internet Computing
Institute of Electrical and Electronics Engineers (IEEE)
Research Grant Council Central Allocation HKBU 2/03C partially supports this work.
Link to Publisher's Edition
Cheung, W., Zhang, X., Wong, H., Liu, J., Luo, Z., & Tong, F. (2006). Service-oriented distributed data mining. IEEE Internet Computing, 10 (4), 44-54. https://doi.org/10.1109/MIC.2006.88