An Analysis of Data Processing for Big Data Analytics

Abstract

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.