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Big Data: Who, What and Where? Social, Cognitive and Journals Map of Big Data Publications with Focus on Optimization

  • Ali Emrouznejad
  • Marianna Marra
Chapter
Part of the Studies in Big Data book series (SBD, volume 18)

Abstract

Contemporary research in various disciplines from social science to computer science, mathematics and physics, is characterized by the availability of large amounts of data. These large amounts of data present various challenges, one of the most intriguing of which deals with knowledge discovery and large-scale data-mining. This chapter investigates the research areas that are the most influenced by big data availability, and on which aspects of large data handling different scientific communities are working. We employ scientometric mapping techniques to identify who works on what in the area of big data and large scale optimization problems.

References

  1. 1.
    Abadi, D., Agrawal, R., Ailamaki, A., Balazinska, M., Bernstein, P. A.: The Beckman report on database research. Sigmod Rec. 43, 61–70 (2014). doi: 10.1145/2694428.2694441
  2. 2.
    Biegler, L.T., Nocedal, J., Schmid, C., Ternet, D.: Numerical experience with a reduced Hessian method for large scale constrained optimization. Comput. Optim. Appl. 15, 45–67 (2000). doi: 10.1023/A:1008723031056 MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Burke, J.V., Curtis, F.E., Wang, H., Wang, J.: Iterative reweighted linear least squares for exact penalty subproblems on product sets. SIAM J. Optim. (2015)Google Scholar
  4. 4.
    Byrd, R.H., Curtis, F.E., Nocedal, J.: An inexact SQP method for equality constrained optimization. SIAM J. Optim. 19, 351–369 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Byrd, R. H., Curtis, F., E.Nocedal, J.: An inexact Newton method for nonconvex equality constrained optimization. Math Program 122(2), 273-299 (2008). doi: 10.1007/s10107-008-0248-3 MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Byrd, R.H., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput. 16, 1190–1208 (1995). doi: 10.1137/0916069 MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Byrd, R.H., Nocedal, J., Zhu, C.: Towards a discrete Newton method with memory for large-scale optimization. Nonlinear Optim. Appl. 1–13 (1996a)Google Scholar
  8. 8.
    Byrd, R.H., Nocedal, J., Zhu. C.: Nonlinear Optimization and Applications. Springer, Boston (1996b)Google Scholar
  9. 9.
    Calero-Medina, C., Noyons, E.C.M.: Combining mapping and citation network analysis for a better understanding of the scientific development: the case of the absorptive capacity field. J. Informetr. 2, 272–279 (2008). doi: 10.1016/j.joi.2008.09.005 CrossRefGoogle 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, 1165–1188 (2012)Google Scholar
  11. 11.
    Cox, M., Ellsworth, D.: Application-controlled demand paging for out-of-core visualization, pp. 235–ff (1997)Google Scholar
  12. 12.
    Crane, D.: Invisible Colleges: Diffusion of Knowledge in Scientific Communities. The University of Chicago Press, Chicago (1972)Google Scholar
  13. 13.
    Curtis, F.E., Nocedal, J., Wächter, A.: A matrix-free algorithm for equality constrained optimization problems with rank-deficient Jacobians. SIAM J. Optim. 20, 1224–1249 (2010). doi: 10.1137/08072471X MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    De Stefano, D., Giordano, G., Vitale, M.P.: Issues in the analysis of co-authorship networks. Qual. Quant. 45, 1091–1107 (2011). doi: 10.1007/s11135-011-9493-2 CrossRefGoogle Scholar
  15. 15.
    Emrouznejad, A., Marra, M.: Ordered weighted averaging operators 1988−2014: a citation-based literature survey. Int. J. Intell. Syst. 29, 994–1014 (2014). doi: 10.1002/int.21673 CrossRefGoogle Scholar
  16. 16.
    Glänzel, W., Schubert, A.: Analyzing scientific networks through co-authorship. Handbook of Quantitative Science and Technology Research, pp. 257–276. Kluwer Academic Publishers, Dordrech (2004)Google Scholar
  17. 17.
    Goldberg, D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc, Boston, MA (1989)zbMATHGoogle Scholar
  18. 18.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. The MIT Press, Cambridge, MA (1975)Google Scholar
  19. 19.
    Khoury, M.J., Lam, T.K., Ioannidis, J.P.A., Hartge, P., Spitz, M.R., Buring, J.E., Chanock, S.J., Croyle, R.T., Goddard, K.A., Ginsburg, G.S., Herceg, Z., Hiatt, R.A., Hoover, R.N., Hunter, D.J., Kramer, B.S., Lauer, M.S., Meyerhardt, J.A., Olopade, O.I., Palmer, J.R., Sellers, T.A., Seminara, D., Ransohoff, D.F., Rebbeck, T.R., Tourassi, G., Winn, D.M., Zauber, A., Schully, S.D.: Transforming epidemiology for 21st century medicine and public health. Cancer Epidemiol. Biomarkers Prev. 22, 508–516 (2013). doi: 10.1158/1055-9965.EPI-13-0146 CrossRefGoogle Scholar
  20. 20.
    Lampe, H.W., Hilgers, D.: Trajectories of efficiency measurement: a bibliometric analysis of DEA and SFA. Eur. J. Oper. Res. 240, 1–21 (2014). doi: 10.1016/j.ejor.2014.04.041 CrossRefGoogle Scholar
  21. 21.
    Lane, J., Stodden, V., Bender, S., Nissenbaum, H.: Privacy, Big Data, and the Public Good. Cambridge University Press, New York (2014). doi:http://dx.doi.org/10.1017/CBO9781107590205
  22. 22.
    Lazer, D., Kennedy, R., King, G., Vespignani, A.: The parable of Google flu: traps in big data analysis. Science (80-.). 343, 1203–1205 (2014). doi: 10.1126/science.1248506
  23. 23.
    Lazer, D., Kennedy, R., King, G., Vespignani, A.: Twitter: Big data opportunities response. Science 345(6193), 148–149 (2014). doi: 10.1126/science.345.6193
  24. 24.
    Lee, J.-D., Baek, C., Kim, H.-S., Lee, J.-S.: Development pattern of the DEA research field: a social network analysis approach. J. Product. Anal. 41, 175–186 (2014). doi: 10.1007/s11123-012-0293-z CrossRefGoogle Scholar
  25. 25.
    Leydesdorff, L., Carley, S., Rafols, I.: Global maps of science based on the new Web-of-Science categories. Scientometrics 94, 589–593 (2013). doi: 10.1007/s11192-012-0784-8 CrossRefGoogle Scholar
  26. 26.
    Li, F., Xu, L.Da, Jin, C., Wang, H.: Structure of multi-stage composite genetic algorithm (MSC-GA) and its performance. Expert Syst. Appl. 38, 8929–8937 (2011). doi: 10.1016/j.eswa.2011.01.110 CrossRefGoogle Scholar
  27. 27.
    Matheson, G.O., Klügl, M., Engebretsen, L., Bendiksen, F., Blair, S.N., Börjesson, M., Budgett, R., Derman, W., Erdener, U., Ioannidis, J.P.A., Khan, K.M., Martinez, R., Mechelen, W. Van, Mountjoy, M., Sallis, R.E., Sundberg, C.J., Weiler, R., Ljungqvist, A.: Prevention and management of non-communicable disease: the IOC consensus statement. Clin. J. Sport Med. 1003–1011 (2013). doi: 10.1136/bjsports-2013-093034
  28. 28.
    Mayer-Schönberger, V., Cukier, K.: Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt (2013)Google Scholar
  29. 29.
    Mocanu, D., Baronchelli, A., Perra, N., Gonçalves, B., Zhang, Q., Vespignani, A: The Twitter of Babel: mapping world languages through microblogging platforms. PloS one 8, (2013). doi: 10.1371/journal.pone.0061981
  30. 30.
    Oh, W., Choi, J.N., Kim, K.: Coauthorship dynamics and knowledge capital: the patterns of cross-disciplinary collaboration in Information Systems research. J. Manag. Inf. Syst. 22, 266–292 (2006). doi: 10.2753/MIS0742-1222220309 CrossRefGoogle Scholar
  31. 31.
    Pudovkin, A.I., Garfield, E.: Algorithmic procedure for finding semantically related journals. J. Am. Soc. Inf. Sci. Technol. 53, 1113–1119 (2002). doi: 10.1002/asi.10153 CrossRefGoogle Scholar
  32. 32.
    Rafols, I., Porter, A.L., Leydesdorff, L.: Science overlay maps: a new tool for research policy and library management. J. Am. Soc. Inf. Sci. Technol. 61, 1871–1887 (2010). doi: 10.1002/asi.21368 CrossRefGoogle Scholar
  33. 33.
    Reijmers, T., Wehrens, R., Daeyaert, F., Lewi, P., Buydens, L.M.: Using genetic algorithms for the construction of phylogenetic trees: application to G-protein coupled receptor sequences. Biosystems 49, 31–43 (1999). doi: 10.1016/S0303-2647(98)00033-1 CrossRefGoogle Scholar
  34. 34.
    Rotolo, D., Rafols, I., Hopkins, M., Leydesdorff, L.: Scientometric mapping as a strategic intelligence tool for the governance of emerging technologies (Digital Libraries) (2013)Google Scholar
  35. 35.
    Sebbah, S., Jaumard, B.: Differentiated quality-of-recovery in survivable optical mesh networks using p-structures. IEEE/ACM Trans. Netw. 20, 798–810 (2012). doi: 10.1109/TNET.2011.2166560 CrossRefGoogle Scholar
  36. 36.
    Sebbah, S., Jaumard, B.: An efficient column generation design method of p-cycle-based protected working capacity envelope. Photonic Netw. Commun. 24, 167–176 (2012). doi: 10.1007/s11107-012-0377-8 CrossRefGoogle Scholar
  37. 37.
    Sebbah, S., Jaumard, B.: PWCE design in survivablem networks using unrestricted shape p-structure patterns. In: 2009 Canadian Conference on Electrical and Computer Engineering, pp. 279–282. IEEE (2009). doi: 10.1109/CCECE.2009.5090137
  38. 38.
    Varian, H.R.: Big data: new tricks for econometrics. J. Econ. Perspect. 28, 3–28 (2014). doi: 10.1257/jep.28.2.3 CrossRefGoogle Scholar
  39. 39.
    Vespignani, A.: Predicting the behaviour of techno-social systems. Science 325(5939), 425–428 (2009). doi: 10.1126/science.1171990
  40. 40.
    Waltman, L., van Eck, N.J.: A new methodology for constructing a publication-level classification system of science. J. Am. Soc. Inf. Sci. Technol. 63, 2378–2392 (2012). doi: 10.1002/asi.22748 CrossRefGoogle Scholar
  41. 41.
    Wang, H., Wu, Z., Rahnamayan, S.: Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems. Soft. Comput. 15, 2127–2140 (2010). doi: 10.1007/s00500-010-0642-7 CrossRefGoogle Scholar
  42. 42.
    Yang, C., Liu, C., Zhang, X., Nepal, S., Chen, J.: A time efficient approach for detecting errors in big sensor data on cloud. IEEE Trans. Parallel Distrib. Syst. 26, 329–339 (2015). doi: 10.1109/TPDS.2013.2295810 CrossRefGoogle Scholar
  43. 43.
    Yang, C., Liu, C., Zhang, X., Nepal, S., Chen, J.: Querying streaming XML big data with multiple filters on cloud. In: 2013 IEEE 16th International Conference on Computational Science and Engineering, pp. 1121–1127. IEEE (2013). doi: 10.1109/CSE.2013.163
  44. 44.
    Zhang, J., Wong, J.-S., Li, T., Pan, Y.: A comparison of parallel large-scale knowledge acquisition using rough set theory on different MapReduce runtime systems. Int. J. Approx. Reason. 55, 896–907 (2014). doi: 10.1016/j.ijar.2013.08.003 CrossRefGoogle Scholar
  45. 45.
    Zhang, X., Liu, C., Nepal, S., Yang, C., Dou, W., Chen, J.: A hybrid approach for scalable sub-tree anonymization over big data using MapReduce on cloud. J. Comput. Syst. Sci. 80, 1008–1020 (2014). doi: 10.1016/j.jcss.2014.02.007 MathSciNetCrossRefzbMATHGoogle Scholar
  46. 46.
    Zhang, X., Liu, C., Nepal, S., Yang, C., Dou, W., Chen, J.: SaC-FRAPP: a scalable and cost-effective framework for privacy preservation over big data on cloud. Concurr. Comput. Pract. Exp. 25, 2561–2576 (2013). doi: 10.1002/cpe.3083 CrossRefGoogle Scholar
  47. 47.
    Zhong, Y., Zhang, L., Xing, S., Li, F., Wan, B.: The big data processing algorithm for water environment monitoring of the three Gorges reservoir area. Abstr. Appl. Anal. 1–7 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Aston Business SchoolAston UniversityBirminghamUK
  2. 2.Essex Business SchoolEssex UniversitySouthend-on-SeaUK

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