Abstract
Big Data has evolved from being an emerging topic to a growing research area in business, science and education fields. The Big Data concept has a multidimensional approach, and it can be defined as a term describing the storage and analysis of large and complex data sets using a series of advanced techniques. In this respect, the researches and professionals involved in this area of knowledge are seeking to develop a culture based on data science, analytics and intelligence. To this end, it is clear that there is a need to identify and examine the intellectual structure, current research lines and main trends. In this way, this paper reviews the literature on Big Data evaluating 23,378 articles from 2012 to 2017 and offers a holistic approach of the research area by using SciMAT as a bibliometric and network analysis software. Furthermore, it evaluates the top contributing authors, countries and research themes that are directly related to Big Data. Finally, a science map is developed to understand the evolution of the intellectual structure and the main research themes related to Big Data.
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Acknowledgements
The authors J. R. López-Robles, N. K. Gamboa-Rosales, H. Gamboa-Rosales and H. Robles-Berumen acknowledge the support by the CONACYT-Consejo Nacional de Ciencia y Tecnología (Mexico) and DGRI-Dirección General de Relaciones Exteriores (México) to carry out this study.
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López-Robles, J.R., Otegi-Olaso, J.R., Porto Gomez, I., Gamboa-Rosales, N.K., Gamboa-Rosales, H., Robles-Berumen, H. (2018). Bibliometric Network Analysis to Identify the Intellectual Structure and Evolution of the Big Data Research Field. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11315. Springer, Cham. https://doi.org/10.1007/978-3-030-03496-2_13
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DOI: https://doi.org/10.1007/978-3-030-03496-2_13
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