, Volume 105, Issue 2, pp 727–741 | Cite as

Scientometric mapping of research on ‘Big Data’

  • Vivek Kumar Singh
  • Sumit Kumar Banshal
  • Khushboo Singhal
  • Ashraf Uddin


This paper presents a scientometric analysis of research work done on the emerging area of ‘Big Data’ during the recent years. Research on ‘Big Data’ started during last few years and within a short span of time has gained tremendous momentum. It is now considered one of the most important emerging areas of research in computational sciences and related disciplines. We have analyzed the research output data on ‘Big Data’ during 2010–2014 indexed in both, the Web of Knowledge and Scopus. The analysis maps comprehensively the parameters of total output, growth of output, authorship and country-level collaboration patterns, major contributors (countries, institutions and individuals), top publication sources, thematic trends and emerging themes in the field. The paper presents an elaborate and one of its kind scientometric mapping of research on ‘Big Data’.


Big Data Big Data research Informetrics Scientometrics 



This work is supported by research grants from Department of Science and Technology, Government of India (Grant: INT/MEXICO/P-13/2012) and University Grants Commission of India (Grant: F. No. 41-624/2012(SR)).


  1. Ajiferuke, I., Burell, Q., & Tague, J. (1988). Collaborative coefficient: A single measure of the degree of collaboration in research. Scientometrics, 14(5), 421–433.CrossRefGoogle Scholar
  2. Alonso, S., Cabrerizo, F. J., Herrera-Viedma, E., & Herrera, F. (2010). hg-index: A new index to characterize the scientific output of researchers based on the h-and g-indices. Scientometrics, 82(2), 391–400.CrossRefGoogle Scholar
  3. Big Data: Science in the Petabyte Era. (2008). Nature, 455(7209), 1–136. Google Scholar
  4. Boyd, D., & Crawford, K. (2012). Critical Questions for Big Data. Information, Communication and Society, 15(5), 662–679. doi: 10.1080/1369118X.2012.678878.CrossRefGoogle Scholar
  5. Cocosila, M., Serenko, A., & Turel, O. (2011). Exploring the management information systems discipline: a scientometric study of ICIS, PACIS and ASAC. Scientometrics, 87(1), 1–16.CrossRefGoogle Scholar
  6. Dealing with Data. (2011). Science, 331(6018), 639–806.Google Scholar
  7. Egghe, L. (2006). Theory and practice of the g-index. Scientometrics, 69(1), 131–152.MathSciNetCrossRefGoogle Scholar
  8. Ekbia, H., Mattioli, M., Kouper, I., Arave, G., Ghazinejad, A., Bowman, T., et al. (2014). Big Data, bigger dilemmas: A critical review. Journal of the Association for Information Science and Technology. doi: 10.1002/asi.23294.Google Scholar
  9. Finardi, U. (2011). Time relations between scientific production and patenting of knowledge: the case of nanotechnologies. Scientometrics, 89(1), 37–50.CrossRefGoogle Scholar
  10. Gupta, B. M., Kshitij, A., & Verma, C. (2011). Mapping of Indian computer science research output, 1999–2008. Scientometrics, 86(2), 261–283.CrossRefGoogle Scholar
  11. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569–16572.CrossRefGoogle Scholar
  12. Howe, D., Costanzo, M., Fey, P., Gojobori, T., Hannick, L., Hide, W., & Rhee, S. Y. (2008). Big data: The future of biocuration. Nature, 455(7209), 47–50.CrossRefGoogle Scholar
  13. Jagadish, H. V. (2015). Big Data and science: Myths and reality. Big Data Research, 2, 49–52.CrossRefGoogle Scholar
  14. Jarić, I., Cvijanović, G., Knežević-Jarić, J., & Lenhardt, M. (2012). Trends in fisheries science from 2000 to 2009: A bibliometric study. Reviews in Fisheries Science, 20(2), 70–79.CrossRefGoogle Scholar
  15. Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and challenges of Big Data research. Big Data Research, 2, 59–64.CrossRefGoogle Scholar
  16. Karpagam, R., Gopalakrishnan, S., Babu, B. R., & Natarajan, M. (2012). Scientometric analysis of stem cell research: A comparative study of India and other countries. Collnet Journal of Scientometrics and Information Management, 6(2), 229–252.CrossRefGoogle Scholar
  17. Karpagam, R., Gopalakrishnan, S., Natarajan, M., & Babu, B. R. (2011). Mapping of nanoscience and nanotechnology research in India: A scientometric analysis, 1990–2009. Scientometrics, 89(2), 501–522.CrossRefGoogle Scholar
  18. Kumar, S., & Garg, K. C. (2005). Scientometrics of computer science research in India and China. Scientometrics, 64(2), 121–132.CrossRefGoogle Scholar
  19. Lawani, S. M. (1980). Quality, collaboration and citations in cancer research: A bibliometric study. Ph.D. Thesis. Florida: School of library science, Florida State University.Google Scholar
  20. Liesch, P. W., Håkanson, L., McGaughey, S. L., Middleton, S., & Cretchley, J. (2011). The evolution of the international business field: a scientometric investigation of articles published in its premier journal. Scientometrics, 88(1), 17–42.CrossRefGoogle Scholar
  21. Ma, R., Ni, C., & Qiu, J. (2008). Scientific research competitiveness of world universities in computer science. Scientometrics, 76(2), 245–260.CrossRefGoogle Scholar
  22. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung, A. (2011). Big Data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute: Technical Report.Google Scholar
  23. Onel, S., Zeid, A., & Kamarthi, S. (2011). The structure and analysis of nanotechnology co-author and citation networks. Scientometrics, 89(1), 119–138.CrossRefGoogle Scholar
  24. Park, H. W., & Leydesdorff, L. (2013). Decomposing social and semantic networks in emerging “Big Data” research. Journal of Informetrics, 7(3), 756–765.CrossRefGoogle Scholar
  25. Prathap, G. (2010). The 100 most prolific economists using the p-index. Scientometrics, 84(1), 167–172.CrossRefGoogle Scholar
  26. Singh, V. K., Uddin, A., & Pinto, D. (2015). Computer science research: The top 100 institutions in India and in the world. Scientometrics, 104(2), 529–553.CrossRefGoogle Scholar
  27. Singhal, K., Banshal, S. K., Uddin, A., & Singh, V. K. (2014). The information technology knowledge infrastructure and research in South Asia. Journal of Scientometric Research, 3(4), 134–142.Google Scholar
  28. Subramanyam, K. (1983). Bibliometric studies of research collaboration: A review. Journal of Information Science, 6(1), 33–38.CrossRefGoogle Scholar
  29. Uddin, A., & Singh, V. K. (2014). Mapping the computer science research in SAARC countries. IETE Technical Review, 31(4), 287–296.CrossRefGoogle Scholar
  30. Wu, Z., & Chin, O. B. (2014). From Big Data to data science: A multi-disciplinary perspective. Big Data Research, 1, 1.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2015

Authors and Affiliations

  • Vivek Kumar Singh
    • 1
  • Sumit Kumar Banshal
    • 1
  • Khushboo Singhal
    • 1
  • Ashraf Uddin
    • 1
  1. 1.Department of Computer ScienceSouth Asian UniversityNew DelhiIndia

Personalised recommendations