Title:	A STUDY ON BIG DATA FRAMEWORKS AND MACHINE LEARNING TOOL KITS
Author(s):	Imad Sassi and Samir Anter
ISBN:	978-989-8533-92-0
Editors:	Ajith P. Abraham and Jörg Roth
Year:	2019
Edition:	Single
Keywords:	Big Data, Hadoop, Spark, Machine Learning, Deep Learning
Type:	Full Paper
First Page:	61
Last Page:	68
Language:	English
Cover:
Full Contents:	click to dowload
Paper Abstract:	Big Data is an extremely large amount of structured and unstructured data, gathered from a wide range of sources which often require a fast processing and real time analysis. In this new context, the performances of the traditional techniques are limited. However, to handle these bulky quantities of data, new technologies emerged, called Big Data technologies. In fact, the characteristics of Big Data made the exploration process of these data a painful task. This process is called Big Data Analytics. One of the important challenges of Big Data is to search new technologies or to improve and extend the existing platforms, infrastructures and standard techniques to manage the Big Data. Hadoop / MapReduce paradigm and the Spark framework are among the most prominent solutions for large-scale parallel distributed data processing alongside Machine Learning techniques, in particularly, Deep Learning for performing powerful statistical and predictive analysis. In this paper, we first, give an overview, a classification and a comparison of main Big Data technologies. Then, we focus in particular on Machine Learning platforms and libraries, especially those for Deep Learning. The results show that Spark is a general-purpose computation engine thanks to its very generalized solutions.
	Go Back