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An Overview of Big Data and Machine Learning Paradigms

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 915)

Abstract

Big Data is one of the most famous concepts in the world of new technology and decision making nowadays. It refers to a huge mass of varied and complex data that is gathered from different sources and exceeds the storage and processing capacity of traditional applications and whose analysis and exploitation must increasingly be done in real time. The value of information in Big Data is very important because it offers many benefits in forecasting accuracy, assisting in the design of new strategies and decision making. Thus, one of the major challenges is data analysis which requires new techniques and algorithms to search for hidden information, correlations and relationships in large amount of data. In this context, Machine Learning allows the use of Big Data full potential. In the first part of this paper, we will present an overview of Big Data, its characteristics and sources as well as its application areas. Then, we will discuss some of problems and challenges related to this concept. Examples of Big Data technologies and platforms will also be presented. In the second part, we will highlight some of the most promising Big Data Analytics methods, mainly Machine Learning. We conclude by proposing a taxonomy of Machine Learning techniques and algorithms in the context of Big Data.

Keywords

Big data IoT Data analytics Machine learning Hadoop MapReduce 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Laboratoire Informatique de Mohammedia (LIM), FSTMHassan II University of CasablancaCasablancaMorocco

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