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Machine Learning and Big Data in the Impact Literature. A Bibliometric Review with Scientific Mapping in Web of Science

1
Department of Didactics and School Organization, University of Granada, 51001 Ceuta, Spain
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Department of Research Methods and Diagnosis in Education, University of Granada, 51001 Ceuta, Spain
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(4), 495; https://doi.org/10.3390/sym12040495
Received: 20 February 2020 / Revised: 14 March 2020 / Accepted: 16 March 2020 / Published: 27 March 2020
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data 2019)
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. View Full-Text
Keywords: scientific production; bibliometric analysis; machine learning; big data; web of science scientific production; bibliometric analysis; machine learning; big data; web of science
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López Belmonte, J.; Segura-Robles, A.; Moreno-Guerrero, A.-J.; Parra-González, M.E. Machine Learning and Big Data in the Impact Literature. A Bibliometric Review with Scientific Mapping in Web of Science. Symmetry 2020, 12, 495.

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