The need for high-performance Data Mining (DM) algorithms is being driven by the exponentially increasing data availability such as images, audio and video from a variety of domains, including social networks and the Internet of Things (IoT). Deep learning is an emerging field of pattern recognition and Machine Learning (ML) study right now. It offers computer simulations of numerous nonlinear processing layers of neurons that may be used to learn and interpret data at higher degrees of abstractions. Deep learning models, which may be used in cloud technology and huge computational systems, can inherently capture complex structures of large data sets. Heterogeneousness is one of the most prominent characteristics of large data sets, and Heterogeneous Computing (HC) causes issues with system integration and Advanced Analytics. This article presents HC processing techniques, Big Data Analytics (BDA), large dataset instruments, and some classic ML and DM methodologies. The use of deep learning to Data Analytics is investigated. The benefits of integrating BDA, deep learning, HPC (High Performance Computing), and HC are highlighted. Data Analytics and coping with a wide range of data are discussed.
Keywords
Heterogeneous Computing (HC), Internet of Things (IoT), Big Data Analytics (BDA), Data Mining (DM), Machine Learning (ML).
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Author(s) thanks to Dr.Jon Cotter for this research completion and support.
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Steve Blair
Steve Blair
Department of Mathematics & Computing, Lander University, Greenwood, SC 29649, United States.
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Cite this article
Steve Blair and Jon Cotter, “An Analysis of Data Processing for Big Data Analytics”, Journal of Computing and Natural Science, vol.1, no.4, pp. 130-138, October 2021. doi: 10.53759/181X/JCNS202101019.
