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The Scientific World Journal
Volume 2015, Article ID 453597, 9 pages
http://dx.doi.org/10.1155/2015/453597
Research Article

Distilling Big Data: Refining Quality Information in the Era of Yottabytes

1Anna University, Regional Office, Madurai 625007, India
2Department of Computer Science and Engineering, Anna University, Regional Office, Madurai 625007, India
3Department of Computer Science and Engineering, Annamalai University, Chidambaram 608002, India

Received 15 June 2015; Revised 1 August 2015; Accepted 9 August 2015

Academic Editor: Venkatesh Jaganathan

Copyright © 2015 Sivaraman Ramachandramurthy et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. R. Trifu and M. L. Ivan, “Big data: present and future,” Database Systems Journal, vol. 5, no. 1, 2014. View at Google Scholar
  2. http://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm.
  3. http://www.emc.com/leadership/digital-universe/2014iview/high-value-data.htm.
  4. A. Dasgupta, Big Data: The Future Is in Analytics, Geospatial World, 2013.
  5. M. J. Bates, “After the dot-bomb: getting web information retrieval right this time,” First Monday, vol. 7, no. 7, 2002. View at Google Scholar · View at Scopus
  6. M. Schroeck, R. Shockley, J. Smart, D. Romero-Morales, and P. Tufano,, “Analytics: the real-world use of big data. How innovative enterprises extract value from uncertain data,” Tech. Rep. GBE03519-USEN-00, IBM Institute for Business Value in Collaboration with Saïd Business School at the University of Oxford, 2012. View at Google Scholar
  7. R. Y. Wang and D. M. Strong, “Beyond accuracy: what data quality means to data consumers,” Journal of Management Information Systems, vol. 12, no. 4, p. 5, 1996. View at Google Scholar
  8. R. W. Zmud, “An empirical investigation of the dimensionality of the concept of information,” Decision Sciences, vol. 9, no. 2, pp. 187–195, 1978. View at Publisher · View at Google Scholar
  9. Y. Wand and R. Y. Wang, “Anchoring data quality dimensions in ontological foundations,” Communications of the ACM, vol. 39, no. 11, pp. 86–95, 1996. View at Google Scholar
  10. W. H. DeLone and E. R. McLean, “Information systems success: the quest for the dependent variable,” Information Systems Research, vol. 3, no. 1, pp. 60–95, 1992. View at Publisher · View at Google Scholar · View at Scopus
  11. I. Claverie-Berge, “Solutions Big Data IBM,” http://www-05.ibm.com/fr/events/netezzaDM_2012/Solutions_Big_Data.pdf.
  12. W. Q. Meeker and Y. Hong, “Reliability meets big data: opportunities and challenges,” Quality Engineering, vol. 26, no. 1, pp. 102–116, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. S. Uddin, T. Al Amin, H. Le, T. Abdelzaher, B. Szymanski, and T. Nguyen, “On diversifying source selection in social sensing,” in Proceedings of the 9th International Conference on Networked Sensing Systems (INSS '12), pp. 1–8, Antwerp, Belgium, June 2012.
  14. D. Wang, T. Abdelzaher, L. Kaplan, and C. C. Aggarwal, “On quantifying the accuracy of maximum likelihood, estimation of participant reliability in social sensing,” in Proceedings of the 8th International Workshop on Data Management for Sensor Networks (DMSN '11), 2011.
  15. C. C. Aggarwal and T. Abdelzaher, “Social sensing,” in Managing and Mining Sensor Data, pp. 237–297, Springer, New York, NY, USA, 2013. View at Publisher · View at Google Scholar
  16. R. Mishra and R. Sharma, “Big data: opportunities and challenges,” International Journal of Computer Science and Mobile Computing, vol. 4, no. 6, pp. 27–35, 2015. View at Google Scholar
  17. M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, “Deep learning applications and challenges in big data analytics,” Journal of Big Data, vol. 2, article 1, 2015. View at Publisher · View at Google Scholar
  18. T. Lukoianova and V. Rubin, “Veracity roadmap: is big data objective, truthful and credible?” Classification Research Online, vol. 24, no. 1, 2014. View at Publisher · View at Google Scholar
  19. R. Viertl, “Foundations of fuzzy Bayesian inference,” Journal of Uncertain Systems, vol. 2, no. 3, pp. 187–191, 2008. View at Google Scholar
  20. O. Osoba, S. Mitaim, and B. Kosko, “Bayesian inference with adaptive fuzzy priors and likelihoods,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 41, no. 5, pp. 1183–1197, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. M. A. Gil, “Bayesian decision making with previous probabilistic uncertainty and actual fuzzy imprecision,” Kybernetes, vol. 17, no. 3, pp. 52–66, 1988. View at Publisher · View at Google Scholar
  22. P. V. Golubtsov and S. S. Moskaliuk, “Fuzzy logic, informativeness and bayesian decision-making problems,” Hadronic Journal, vol. 26, no. 5, pp. 589–630, 2003. View at Google Scholar
  23. O. V. Verevka and I. N. Parasyuk, “Mathematical fundamentals of constructing fuzzy Bayesian inference techniques,” Cybernetics and System Analysis, vol. 38, no. 1, pp. 89–99, 2002. View at Google Scholar
  24. Bayesian Perceptual Psychology, http://www.philosophy.ucsb.edu/docs/faculty/papers/bayesian.pdf.
  25. D. Wang, T. Abdelzaher, H. Ahmadi et al., “On Bayesian interpretation of fact-finding in information networks,” in Proceedings of the 14th International Conference on Information Fusion (Fussion '11), Chicago, Ill, USA, July 2011.
  26. M. D. Assunção, R. N. Calheiros, S. Bianchi, M. A. Netto, and R. Buyya, “Big data computing and clouds: trends and future directions,” Journal of Parallel and Distributed Computing, vol. 79-80, pp. 3–15, 2015. View at Publisher · View at Google Scholar