The evolution and democratization of technologies have created a veritable explosion of information, this, of course, gives rise to an urgent need to analyze and deal with the huge masses of data. In fact, the problems raised by the accumulation of data (storage, processing time, heterogeneity, capture rate/generation, etc.) become stronger because the data are massive, complex and varied. It is clear that the representation of information has the capacity to synthesize and condense data, and constitutes an efficient approach for analysis. Nevertheless, it remains ineffective and does not solve these problems. Besides that, conventional visualization techniques are rarely adapted to manage and process this mass of information. To face the complex big data challenges, various types of technologies have been developed. This paper talks about the visualization layer. This layer is located just above the Data Sources, Ingestion, Hadoop storage and Hadoop Platform Management layers for which we have already proposed a meta-modeling. It has a very important role at the level of Big Data Structure. In a continuous effort, we shall present in this paper a universal meta-modeling for the visualization layer and its relationship with the other layers of a Big Data system.

In Web 3.0, semantic data gives machines the ability to understand and process data. Resource Description Framework (RDF) is the liagna franca of Semantic Web. While Big Data handles the problematic of storing and processing massive data, it still does not provide a support for RDF data. In this paper, we present a new Big Data semantic web comprised of a classical Big Data system with a semantic layer. As a proof of concept of our approach, we use Mobile-learning as a case study. The architecture we propose is composed of two main parts: a knowledge server and an adaptation model. The knowledge server allows trainers and business experts to represent their expertise using business rules and ontology to ensure heterogeneous knowledge. Then, in a mobility environment, the knowledge server makes it possible to take into account the constraints of the environment and the user constraints thanks to the RDF exchange format. The adaptation model based on RDF graphs corresponds to combinatorial optimization algorithms, whose objective is to propose to the learner a relevant combination of Learning Object based on its contextual constraints. Our solution guarantees scalability, and high data availability through the use of the principle of replication. The results obtained in the system evaluation experiments, on a large number of servers show the efficiency, scalability, and robustness of our system if the amount of data processed is very large.

E-learning is a very dynamic field, constantly growing, which refers to an educa-tional content or learning experiences provided or made by means of digital tech-nologies. The development of this area has a direct impact on the quality of edu-cation and cost reduction. E-learning is now dominated by learning management systems such as Moo-dle, to improve the interoperability of these systems; the use of data / resources is a major requirement, this need to push the e-Learning community to seek new approaches to improve this interoperability of systems. On the other hand, se-mantic Web technologies offers standards like RDF allows to describe Web re-sources with their metadata. We propose in this work an approach based on Se-mantic Web technology to improve the interoperability of e-Learning systems like Moodle.