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Personalized Online Analytical Processing in Big Data Context Using User Profile and Search Context

Author

Listed:
  • Menaceur Sadek

    (Laboratory of Mathematics, Informatics and Systems (LAMIS) University of Larbi Tebessi, Tebessa, Algeria)

  • Makhlouf Derdour

    (Computer Sciences Department University of Larbi Tebessi, Tebessa, Algeria)

  • Bouramoul Abdelkrim

    (MISC Lab & Fundamental Computer Science and its Applications Department Constantine2 University, Constantine, Algeria)

Abstract

This article is part of the field of analysis and personalization of large data sets (Big Data). This aspect of analysis and customization has become a major issue that has generated a lot of questions in recent years. Indeed, it is difficult for inexperienced or casual users to extract relevant information in a Big Data context, for volume, the velocity and the variability of data make it difficult for the user to capture, manage and process data by methods and traditional tools. In this article, the authors propose a new approach for personalizing OLAP analysis in a Big Data context by using context and user profile. The proposed approach is based on five complementary layers namely: Extern layer, layer for the formulation of the contexts defined in the system, profiling and querying layer and layer for the construction of personalized OLAP cubes and a final one for multidimensional analysis cubes. The conducted experiment has shown that taking context and user profile into account improves the results of online analytical processing in the context of Big Data.

Suggested Citation

  • Menaceur Sadek & Makhlouf Derdour & Bouramoul Abdelkrim, 2017. "Personalized Online Analytical Processing in Big Data Context Using User Profile and Search Context," International Journal of Strategic Information Technology and Applications (IJSITA), IGI Global, vol. 8(4), pages 67-80, October.
    • Handle: RePEc:igg:jsita0:v:8:y:2017:i:4:p:67-80
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