Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Service-Oriented Distributed Data Mining

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
6 Author(s)
Cheung, W.K. ; Dept. of Comput. Sci., Hong Kong Baptist Univ. ; Xiao-Feng Zhang ; Ho-Fai Wong ; Jiming Liu
more authors

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

Published in:

Internet Computing, IEEE  (Volume:10 ,  Issue: 4 )