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

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

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) and Bouramoul Abdelkrim (MISC Lab & Fundamental Computer Science and its Applications Department Constantine2 University, Constantine, Algeria)
DOI: 10.4018/IJSITA.2017100106
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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.
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Today, OLAP analysis from big data is entering a new phase called personalized data. Modern Big Data systems, such as Hadoop are used as an alternative for data warehousing (Hollingsworth, 2012), (Kuldeep et al., 2014). It collects and stores inherently complex data streams due to volume, velocity, value, variety, variability and veracity (Russom, 2011). According to these reasons, the traditional OLAP implementation, namely, the RDBMS based ROLAP system, seems to be inadequate, because the new massive data architectures and analytics tools go beyond SQL Datawarehouses and OLAP engines (Jie et al., 2015). On the other hand, the work (Cuzzocrea et al., 2013) highlights the raised problems and research tendencies in the field of data warehousing and OLAP analysis on Big Data. On the basis of this observation, we seek through this study to design a new architecture to solve problems when calculating OLAP data cubes.

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