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

Ivan Kholod¹⁺, Andrey Shorov¹, and Sergei Gorlatch²

¹Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia
{iiholod, ashxz}@mail.ru

²University of Muenster, Muenster, Germany

gorlatch@uni-muenster.de

Abstract

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.

Keywords: mobile cloud; wireless networks; parallel algorithms; distributed algorithms;

distributed data mining; parallel data mining

+: Corresponding author: Ivan Kholod
Saint Petersburg Electrotechnical University ”LETI”, Saint Petersburg, Russia, Tel: +79217954258

Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA)

Vol. 11, No. 1, pp. 2-17, March 2020 [pdf]

Received: January 15, 2020; Accepted: March 10, 2020; Published: March 31, 2020

DOI: 10.22667/JOWUA.2020.03.31.002