Enabling Far-Edge Analytics: Performance Profiling of Frequent Pattern Mining Algorithms
作者： Khubaib Amjad Alam , Rodina Ahmad , Kwangman Ko
DOI: 10.1109/ACCESS.2017.2699172
关键词:
摘要: Far-edge analytics refers to the enablement of data mining algorithms in far-edge mobile devices that are part edge cloud computing (MECC) systems. enables reduction environments, hence reducing transfer rate and bandwidth utilization cost for mobile-edge communication. In addition, facilitates local knowledge availability enable personalized stream applications. Existing literature mainly addresses classification clustering problems devices, but problem frequent pattern (FPM) remains unexplored. This paper presents results an experimental study on performance profiling algorithms. We developed a real application analysis 21 FPM with various sets terms execution time, storage complexity, sparsity, density, set size. According results, large-sized high sparsity increase computational devices. To address these issues, we propose framework discuss relevant research challenges seamless MECC
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