Abstract
This paper was explore a scientometric analysis of the research work in the emerging field of “Big Data” in recent years. Research on “Big Data” in the past few years, and in a short time has gained tremendous momentum. It is now considered one of the most important emerging research areas in computational science and related disciplines. By using the related literature in the Science Citation Index (SCI) database from 2006 to 2016, a scientometric approach was used to quantitatively assessing current research hotspots and trends. It shows that “Big Data” is a new emerging field with rapid development, the total of 2076 articles covered 131 countries (regions) and Top 3 countries (regions) were USA (731, 38.86%), China (373, 19.83%), England (93, 4.94%). In addition, Top 10 keywords are found to have citation bursts: epidemiology, scalability, social media, genomics, visualization, sequencing data, integration, intelligence, association, behavior. The results provided a dynamic view of the evolution of “Big Data” research hotpots and trends from various perspectives which may serve as a potential guide for future research.
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References
Graham-Rowe, D., Goldston, D., Doctorow, C., Waldrop, M., Lynch, C., Frankel, F., Reid, R., Nelson, S., Howe, D., Rhee, S.Y.: Big data: science in the petabyte era. Nature 455(7209), 1–136 (2008)
Dealing with data. Science 331(6018), 639–806 (2011
Jin, X., et al.: Significance and challenges of big data research. Big Data Res. 2, 59–64 (2015)
Ekbia, H., Mattioli, M., Kouper, I., Arave, G., Ghazinejad, A., Bowman, T., Suri, V.R., Tsou, A., Weingart, S., Sugimoto, C.R.: Big data, bigger dilemmas: a critical review. J. Assoc. Inf. Sci. Technol. 66(8), 1523–1545 (2015)
Al-Jarrah, O.Y., Yoo, P.D., Muhaidat, S., Karagiannidis, G.K., Taha, K.: Efficient machine learning for big data: a review. Big Data Res. 2(3), 87–93 (2015)
Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., Khan, S.U.: The rise of “Big Data” on cloud computing: review and open research issues. Inf. Syst. 47, 98–115 (2015)
Hilbert, M.: Big data for development: a review of promises and challenges. Dev. Policy Rev. 34(1), 135–174 (2016)
Liu, J.Z., Li, J., Li, W.F., Wu, J.Z.: Rethinking big data: a review on the data quality and usage issues. ISPRS J. Photogram. Remote Sens. 115, 134–142 (2016)
Chen, C.: CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol. 57(3), 359–377 (2006)
Rogosa, D., Brandt, D., Zimowski, M.: A growth curve approach to the measurement of change. Psychol. Bull. 92(3), 726 (1982)
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1979)
Schadt, E.E.: Computational solutions to large-scale data management and analysis. Nat. Rev. Genet. 11(9), 647–657 (2010)
Manyika, J.: Big data: the next frontier for innovation, competition, and productivity. Analytics (2011)
Schadt, E.E.: Computational solutions to large-scale data management and analysis. Nat. Rev. Genet. 11(9), 647–657 (2010)
Ranger, C.: Evaluating MapReduce for multi-core and multiprocessor systems. In: HPCA (2007)
Schatz, M.C.: Highly sensitive read mapping with MapReduce. Bioinformatics 25, 1363–1369 (2009)
Bell, G., Hey, T., Szalay, A.: Beyond the data deluge. Science 323(5919), 1297–1298 (2009)
Jacobs, A.: The pathologies of big data. Queue 7(6), 10 (2009)
Howe, D.: The future of biocuration. Nature 455(7209), 47–50 (2008)
Bengio, Y.: Learning deep architectures for AI. Found. Trends® Mach. Learn. 2, 11–55 (2009)
Kambatla, K.: Trends in big data analytics. J. Parallel Distrib. Comput. 74(7), 2561–2573 (2014)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: OSDI (2004)
Mayer-Schnberger, V., Cukier, K.: Big Data: A Revolution That Will Transform How We Live, Work and Think. Houghton Mifflin Harcourt, Boston (2013)
Lazer, D.: Big data. the parable of google flu: rraps in big data analysis. Science 343(6176), 1203 (2014)
Ginsberg, J.: detecting influenza epidemics using search engine query data. Nature 457(7232), 1012–1014 (2008)
Wu, X.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)
Boyd, D., Crawford, K.: Critical questions for big data. Inf. Commun. Soc. 15(5), 1–18 (2012)
Marx, V.: The big challenges of big data. Nature 498(7453), 255–260 (2013)
Murdoch, T.B., Detsky, A.S.: The inevitable application of big data to health care. JAMA, J. Am. Med. Assoc. 309(13), 1351–1352 (2013)
Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mobile Netw. Appl. 19(2), 171–209 (2014)
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Zeng, L., Li, Z., Wu, T., Yang, L. (2017). Mapping Knowledge Domain Research in Big Data: From 2006 to 2016. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_24
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DOI: https://doi.org/10.1007/978-3-319-61845-6_24
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