Scalable and Privacy Preserving Distributed Data Analysis Over a Service-Oriented Platform

Kwok Wai CHEUNG*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingChapterpeer-review


Summary: Scalability and data privacy are two main challenges hindering distributed data analysis from being widely applied in many collaborative projects. In this chapter, we first review a recently proposed scalable and privacy-preserving distributed data analysis approach. The approach computes abstractions of distributed data which are then used for mining global data patterns. Then, we describe a service-oriented realization of the approach for data clustering and explain in detail how the analysis process is deployed in a BPEL platform for execution. In addition, lessons learned in the implementation exercise and future research directions regarding how distributed data analysis platforms can be built with even higher scalability and improved support for privacy preservation is also discussed.

Original languageEnglish
Title of host publicationData Mining Techniques in Grid Computing Environments
PublisherJohn Wiley & Sons Ltd.
Number of pages14
ISBN (Print)9780470512586
Publication statusPublished - 22 Jun 2009

Scopus Subject Areas

  • Computer Science(all)

User-Defined Keywords

  • BPEL process creation tools - Oracle BPEL designer or ActiveBPEL
  • Business Process Execution Language (BPEL)
  • Data analysis challenges
  • Extensible Stylesheet Language Transformation (XSLT)
  • Gaussian mixture model (GMM)
  • Modelling distributed data mining and workflow processes
  • Scalability and data privacy
  • Scalable and privacy preserving data mining paradigm


Dive into the research topics of 'Scalable and Privacy Preserving Distributed Data Analysis Over a Service-Oriented Platform'. Together they form a unique fingerprint.

Cite this