@misc{10481/62877,
year = {2020},
month = {3},
url = {http://hdl.handle.net/10481/62877},
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.},
publisher = {MDPI},
keywords = {Scientific production},
keywords = {Bibliometric analysis},
keywords = {Machine learning},
keywords = {Big Data},
keywords = {Web of Science},
title = {Machine Learning and Big Data in the Impact Literature. A Bibliometric Review with Scientific Mapping in Web of Science},
doi = {10.3390/sym12040495},
author = {López Belmonte, Jesús and Segura Robles, Adrián and Moreno Guerrero, Antonio José and Parra González, María Elena},
}