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We study different kinds of data distributions for improving the efficient, parallelized implementation of data mining in mobile cloud systems. Our formally-based approach ensures the correctness of the obtained parallel implementation. We apply our approach to parallel implementation of data mining algorithms in systems where a cloud is accessed via a mobile (wireless) network. Our approach derives a parallel implementation of a data mining algorithm that performs as much as possible computations at local servers of a mobile network, rather than transferring data for processing to a high-performance cluster in the cloud as it is done in the current cloud systems based on MapReduce. We implement our approach by extending the Java-based library DXelopes, and we illustrate our results with the popular data-mining Normal Bayes classifier training algorithm. Our experiments on real-world data sets confirm that our approach significantly reduces the network traffic and the application run time.

Efficient Distribution and Processing of Data for Parallelizing Data Mining in Mobile Clouds.