International Conference on Conceptual Modeling

Advances in Conceptual Modeling pp 173-183 | Cite as

Research on Big Data

Characterizing the Field and Its Dimensions
  • Jacky Akoka
  • Isabelle Comyn-Wattiau
  • Nabil Laoufi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9382)

Abstract

Big Data has emerged as a significant area of study for both practitioners and researchers. Big Data is a term for massive data sets with large structure. In 2012, Big Data passed the top of the Gartner Hype Cycle, attesting the maturity level of this technology and its applications. The aim of this paper is to examine whether the Big Data research community reached the same level of maturity. For this purpose, we provide a framework identifying existing and emerging research areas of Big Data. This framework is based on five dimensions, including the SMACIT perspective. Current and past research in Big Data are analyzed using a bibliometric study of publications based on more than a decade of related academic publications. The results have shown that even if significant contributions have been made by the research community, attested by a continuous increase in the number of scientific publications that address Big Data, it lags behind entreprises’ expectations.

Keywords

Big data Bibliometric study Framework Artefact Usage Analytics 

References

  1. 1.
    Gantz, J., Reinsel, D.: Extracting value from chaos. IDC iView, pp 1–12 (2011)Google Scholar
  2. 2.
    Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., et al.: Big Data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute (2011)Google Scholar
  3. 3.
    Intel IT Center: Planning Guide: Getting Started with Hadoop, Steps IT Managers Can Take to Move Forward with Big Data Analytics (2012)Google Scholar
  4. 4.
    Davenport, T., Barth, P.: Bean, R: How Big Data is different. MIT Sloan Mgt Rev. 54(1), 43–46 (2012)Google Scholar
  5. 5.
    Jin, X., Wah, B.W., Cheng, X., Wang, Y.: Significance and challenges of Big Data research. J. Big Data Res. 2(2), 59–64 (2015)CrossRefGoogle Scholar
  6. 6.
    Team, O.R.: Big Data Now: Current Perspectives from O’Reilly Radar. O’Reilly Media, Sebastopol (2011)Google Scholar
  7. 7.
  8. 8.
    Laney D.: 3-D data management: controlling data volume, velocity and variety. META Group Research Note (2001)Google Scholar
  9. 9.
    Sagiroglu, S., Sinanc, D.: Big data: a review. In: IEEE International Conference on CTS (2013)Google Scholar
  10. 10.
    Chen, H., Chiang, R.H.L., Storey, V.C.: Business intelligence and analytics: from Big Data to big impact. MIS Q. 36(4), 1165–1188 (2012)Google Scholar
  11. 11.
    Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mob. Netw Appl 19, 171–209 (2014)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Zhu, Y.Q., Chen, H.G.: Social media and human need satisfaction: Implications for social media marketing. Bus. Horiz. 58, 335–345 (2015)CrossRefGoogle Scholar
  13. 13.
    ComScore. It’s a social world: Top 10 need-to-knows about social networking and where it’s headed. http://www.comscore.com/. Accessed 10 May 2013
  14. 14.
    Mangold, W.G., Faulds, D.J.: Social media: the new hybrid element of the promotion mix. Bus. Horiz. 52(4), 357–365 (2009)CrossRefGoogle Scholar
  15. 15.
  16. 16.
    Irfan, R., Bickler, G., Khan, S.U., Kolodziej, J., Li, H., Chen, D., Wang, L., Hayat, K., Madani, S.A., Nazir, B., Khan, I.A., Ranjan, R.: Survey on social networking services. IET Netw. 2(4), 224–234 (2013)CrossRefGoogle Scholar
  17. 17.
    King, I., Li, J., Chan, K.T.: A Brief Survey of Computational Approaches in Social Computing. In: Proceedings of International Joint Conference Neural Networks, Atlanta, Georgia, USA (2009)Google Scholar
  18. 18.
    O’Leary, D.: Exploiting Big Data from mobile device sensor-based apps: challenges and benefits. MIS Q. Executive 12(4), 179 (2014)Google Scholar
  19. 19.
    Laurila, J.K., Gatica-Perez, D., Aad, I., Blom, J., Bornet, O.: Pervasive and Mobile Computing, vol. 9, pp. 752–777 (2013)Google Scholar
  20. 20.
    Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nat. 4, 53–779 (2008)Google Scholar
  21. 21.
    Bellavista, P., Montanari, R., Das, S.K.: Mobile social networking middleware: a survey. Pervasive Mob. Comput. 9, 437–453 (2013)CrossRefGoogle Scholar
  22. 22.
    Kambatla, K., Kollias, G., Kumar, V., Grama, A.: Trends in Big Data analytics. J. Parallel Distrib. Comput. 74, 2561–2573 (2014)CrossRefGoogle Scholar
  23. 23.
    Gandomi, A., Haider, M.: Beyond the hype: Big Data concepts, methods, and analytics. Int. J. Inf. Manage. 35, 137–144 (2015)CrossRefGoogle Scholar
  24. 24.
    Labrinidis, A., Jagadish, H.V.: Challenges and opportunities with Big Data. Proc VLDB Endowment 5(12), 2032–2033 (2012)CrossRefGoogle Scholar
  25. 25.
    Polash, F., Abuhussein, A., Shiva, S.: A Survey of Cloud Computing Taxonomies: Rationale and Overview. In: 9th International Conference on Internet Technology and Secured Transactions (2014)Google Scholar
  26. 26.
    Assunçao, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A.S., Buyya, R.: Big Data computing and clouds: Trends and future directions. J. Parallel Distrib. Comput. 79–80, 3–15 (2015)CrossRefGoogle Scholar
  27. 27.
    Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54, 2787–2805 (2010)CrossRefMATHGoogle Scholar
  28. 28.
    Perera, C., Liu, C.H., Jayawardena, S., Chen, M.: A survey on internet of things from industrial market perspective. IEEE Access 2, 1660–1679 (2015)CrossRefGoogle Scholar
  29. 29.
    Chen, M., Mao, S., Zhang, Y., Leung, V.C.M.: Big Data : Related Technologies. Challenges and Future Prospects. SpringerBriefs in Computer Science. Springer, Cambridge (2014)CrossRefGoogle Scholar
  30. 30.
    Agrawal D, Bernstein P, Bertino E, Davidson S, Dayal U, Franklin M, Gehrke J, Haas L, Halevy A, Han J et al.: Challenges and opportunities with Big Data. A community white paper developed by researches across the United States (2012)Google Scholar
  31. 31.
    Prat, N., Comyn-Wattiau, I., Akoka, J.: Artifact evaluation in information systems design science research – a holistic view. In: PACIS 2014 Proceedings, Paper 23 (2014)Google Scholar
  32. 32.
    March, S.T., Smith, G.F.: Design and natural science research on information technology. Decis. Support Syst. 15(4), 251–266 (1995)CrossRefGoogle Scholar
  33. 33.
    Cuzzocrea, A., Song, I.Y., Davis, K.: Analytics over large-scale multidimensional data: the Big Data revolution. In: Proceedings of the ACM 14th International Workshop on Data Warehousing and OLAP, pp. 101–103. ACM, New York, USA (2011)Google Scholar
  34. 34.
    Bizer, C., Boncz, P., Brodie, M.L., Erling, O.: The meaningful use of Big Data: four perspectives. SIGMOD 40(4), 56–60 (2011)CrossRefGoogle Scholar
  35. 35.
    Jacobs, A.: The pathologies of Big Data. Commun. ACM 52(8), 36 (2009)CrossRefGoogle Scholar
  36. 36.
    Madden, S.: From databases to Big Data. IEEE Comput. 16(3), 4–6 (2012)CrossRefGoogle Scholar
  37. 37.
    Goes, P.B.: Big Data and IS research methods. MIS Q. 38(3), 3–8 (2014)Google Scholar
  38. 38.
    Hansmann, T., Niemeyer, P.: Big Data - characterizing an emerging research field using topic models. In: IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) (2014)Google Scholar
  39. 39.
    Pospiech, M., Felden, C.: Big Data: a state-of-the-art. In: Americas Conference on Information Systems (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jacky Akoka
    • 1
    • 2
  • Isabelle Comyn-Wattiau
    • 1
    • 3
  • Nabil Laoufi
    • 1
  1. 1.CEDRIC-CNAMParisFrance
  2. 2.TEM-Institut Mines TelecomEvryFrance
  3. 3.ESSEC Business SchoolCergy-PontoiseFrance

Personalised recommendations