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From KDD to KUBD: Big Data Characteristics Within the KDD Process Steps

  • Naima Lounes
  • Houria Oudghiri
  • Rachid Chalal
  • Walid-Khaled Hidouci
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)

Abstract

Big Data is the current challenge for the computing field not only because of the volume of data involved but also for the amazing promises to analyze and interpret massive data to generate useful and strategic knowledge in various fields such as security, sales and education. However, the massive volume of data in addition to other characteristics of Big Data such as the variety, velocity, and variability require a whole new set of techniques and technologies, which are not yet available, to effectively extract the desired knowledge. The KDD (Knowledge Discovery in Databases) process has achieved excellent results in the classical database context and that is why we examine the possibility of adapting it to the Big Data context to take advantage of its strong and effective data processing techniques. We introduce therefore a new process KUBD (Knowledge Unveiling in Big Data) inspired from the KDD process and adapted to the Big Data context.

Keywords

Big Data KDD Data preprocessing Data analytics Data mining Knowledge management 

Notes

Acknowledgments

Appreciation goes to the friends Samia Boulkrinat and Nadia El-Allia for their support, advice and availability during the elaboration of this paper.

References

  1. 1.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. American Association for Artificial Intelligence, Fall 1996Google Scholar
  2. 2.
    Piatetsky-Shapiro, G.: Data mining and knowledge discovery 1996 to 2005: overcoming the hype and moving from “university” to “business” and “analytics”, Kdnuggets 2007, Brookline, MA 2143, USA (2007)Google Scholar
  3. 3.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: Knowledge discovery and data mining: towards a unifying framework. In: KDD Proceedings (1996)Google Scholar
  4. 4.
    Maimon, O., Rokach, L. (eds.): Data Mining and Knowledge Discovery. Handbook, pp. 1–15. Springer, Heidelberg (2010)zbMATHGoogle Scholar
  5. 5.
    Big data. https://fr.wikipedia.org/wiki/Big_data. Accessed 26 Nov 2017
  6. 6.
    Owais, S.S., Hussein, N.S.: Extract five categories CPIVW from the 9 V’s characteristics of the big data. Int. J. Adv. Comput. Sci. Appl. 7(3), 254–258 (2016)Google Scholar
  7. 7.
    Ali-ud-din Khan, M., Uddin, M.F., Gupta, N.: Seven V’s of big data understanding big data to extract value. In: Proceedings of 2014 Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1) (2014)Google Scholar
  8. 8.
    Rodríguez-Mazahua1, L., Rodríguez-Enríquez, C.-A., Sánchez-Cervantes, J.L., Cervantes, J., García-Alcaraz, J.L., Alor-Hernández, G.: A General Perspective of Big Data: Applications, Tools, Challenges and Trends. Springer Science+Business Media, New York (2015)CrossRefGoogle Scholar
  9. 9.
    Che, D., Safran, M., Peng, Z.: From Big Data to Big Data Mining: Challenges, Issues, and Opportunities. Springer, Heidelberg (2013)Google Scholar
  10. 10.
    Chen, M., Mao, S., Liu, Y.: Big Data: A Survey. Springer Science+Business Media, New York (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Naima Lounes
    • 1
  • Houria Oudghiri
    • 1
  • Rachid Chalal
    • 1
  • Walid-Khaled Hidouci
    • 1
  1. 1.Ecole nationale Supérieure d’InformatiqueOued-SmarAlgeria

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