Enabling Far-Edge Analytics: Performance Profiling of Frequent Pattern Mining Algorithms

Abstract

Far-edge analytics refers to the enablement of data mining algorithms in far-edge mobile devices that are part of mobile edge cloud computing (MECC) systems. Far-edge analytics enables data reduction in mobile environments, hence reducing the data transfer rate and bandwidth utilization cost for mobile-edge communication. In addition, far-edge analytics facilitates local knowledge availability to enable personalized mobile data stream mining applications. Existing literature mainly addresses classification and clustering problems in far-edge mobile devices, but the problem of frequent pattern mining (FPM) remains unexplored. This paper presents the results of an experimental study on the performance profiling of frequent pattern mining algorithms. We developed a real mobile application for performance analysis and profiling of 21 FPM algorithms with various real data sets in terms of execution time, storage complexity, sparsity, density, and data set size. According to the experimental results, large-sized data sets with high sparsity increase computational and storage cost in far-edge mobile devices. To address these issues, we propose a framework and discuss the relevant research challenges for seamless execution of FPM algorithms in MECC systems.