Research in Big Data and Analytics: An Overview

Abstract

Big Data Analytics has been gaining much focus of attention lately as researchers from industry and academia are trying to effectively extract and employ all possible knowledge from the overwhelming amount of data generated and received. Traditional data analytic methods stumble in dealing with the wide variety of data that comes in huge volumes in a short period of time, demanding a paradigm shift in storage, processing and analysis of Big Data. Owing to its significance, several agencies including U.S. government have released huge funds for research in Big Data and allied fields in recent years. This paper presents a brief overview of research progress in various areas associated to Big Data Processing and Analytics and conclude with a discussion on research directions in the same area.

Privacy preservation in e-health cloud: taxonomy, privacy requirements, feasibility analysis, and opportunities

“…We find that there exists a natural tendency of big data to be applied to e-health cloud scenario [122,123,152]. In Table 4 we present big data basics and its relation to e health, after that we describe privacy and scalability challenge posed by the integration of today's highly demanding paradigms of cloud computing, big data and privacy.…”

Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review

“…Traditional fault diagnostic approaches need the artificial extraction of a considerable quantity of feature data, such as time domain features, frequency domain features, and time-frequency domain features [1][2][3], which adds to the fault diagnostic uncertainty and complexity. Traditional fault diagnosis methods are unable to meet the needs of the fault diagnosis in the context of big data due to the complex and efficient development of motors, which presents the data reflecting the operating status of motors with the characteristics of massive, diversified, 2 of 26 fast flowing speed, and low value density of "big data" [4][5][6]. Simultaneously, the advancement of artificial intelligence technology encourages the evolution of fault diagnosis technology from traditional to intelligent [7].…”

A Simplified Analytical Model Toward Big Data Analysis using Ridge Regression Method

“…But, the computing burden of big data analysis remains because the traditional analyses such as statistical methods have a limitation for analyzing big data. L. R. Nair et al [20] presented the research of in Big Data progresses in different dimensions including effective capture of data, discovering novel storage solutions and recovery techniques. Figure 3 gives an outline of Big Data analytics flow.…”