Abstract
Combined use of machine learning and large data allows us to analyze data and find explanatory models that would not be possible with traditional techniques, which is basic within the principles of symmetry. The present study focuses on the analysis of the scientific production and performance of the Machine Learning and Big Data (MLBD) concepts. A bibliometric methodology of scientific mapping has been used, based on processes of estimation, quantification, analytical tracking, and evaluation of scientific research. A total of 4240 scientific publications from the Web of Science (WoS) have been analyzed. Our results show a constant and ascending evolution of the scientific production on MLBD, 2018 and 2019 being the most productive years. The productions are mainly in English language. The topics are variable in the different periods analyzed, where “machine-learning” is the one that shows the greatest bibliometric indicators, it is found in most of motor topics and is the one that offers the greatest line of continuity between the different periods. It can be concluded that research on MLBD is of interest and relevance to the scientific community, which focuses its studies on the branch of machine-learning.
Highlights
The idea of Machine Learning was not unique in computing, but due to the consistently varying nature of necessities of the present world it has come up in unique forms
Some Machine Learning techniques for processing of Big Data are not efficient and are not adaptable to get together a high volume, value, velocity, and variety, it requests to rehash itself for handling of big data [2]
Machine learning is utilized in Web search, spam channels, advertisement situation, recommender frameworks, credit scoring, Symmetry 2020, 12, 495; doi:10.3390/sym12040495
Summary
The idea of Machine Learning was not unique in computing, but due to the consistently varying nature of necessities of the present world it has come up in unique forms. With the expansion of the web, a large amount of advanced data are being created, which implies that there are much more data accessible for machines to learn and analyze. Some Machine Learning techniques for processing of Big Data are not efficient and are not adaptable to get together a high volume, value, velocity, and variety, it requests to rehash itself for handling of big data [2]. Adaptability is a difficult problem with conventional calculations of machine learning [3]. In the event that a machine learning approach is utilized to address a calculation deficiency and a material science-based model is accessible, at that point numerical outcomes might be adequate in requests to process acceptable execution measures [2]. Machine learning is utilized in Web search, spam channels, advertisement situation, recommender frameworks, credit scoring, Symmetry 2020, 12, 495; doi:10.3390/sym12040495 www.mdpi.com/journal/symmetry
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