A Referenced Framework on New Challenges and Cutting-Edge Research Trends for Big-Data Processing Using Machine Learning Approaches

Abstract

Machine Learning and Big- Data Analytics is each growing computing fields. Big data is, in reality, gaining attraction in all regions of Science and engineering. While those big-data units have the clean capacity, successfully comprehending them will call for new thinking methods and inventive learning methodologies to cope with the severe issues. On the other hand, traditional machine learning strategies may not be able to fulfil the needs of real-time data processing for big -datasets because of the big-data age approaches. As a result, machine learning will reinvent itself for the big-data era. Where improvements in any discipline Big- Data analytics or machine Learning complement the opposite. This article offers a literature evaluation of the most recent advancement in ML (machine learning) approaches for large amounts of data processing. We focused on evaluating and analyzing the challenging situation and potential solutions associated with handling huge data sets with machine learning. This review article initially presents an overview of big-data and big-data analytics, followed by reviewing traditional machine-learning approaches as well as examining new aspects of machine-learning techniques for big-data processing. After that identifying the critical challenges of big-data processing correlated with the properties of big data 5Vs-volume, velocity, variety, veracity, and value, finally proposed a reference framework for big-data processing along with new emerging tools and technologies like Hadoop, distributed computing, and parallel computing along with the identification of open issues and new research trends associated with the processing of large amounts of data sets (Big-Data).

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