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)


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.


Big Data KDD Data preprocessing Data analytics Data mining Knowledge management 



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


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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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