Towards a New Scalable Big Data System Semantic Web Applied on Mobile Learning

Towards a New Scalable Big Data System Semantic Web Applied on Mobile Learning

Mobile Learning

Semantic Web

e-learning

Big Data

RDF

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.

Banane, Mouad

Belangour, Abdessamad

International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 01 (2020); pp. 126-140

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2020-01-20

Copyright (c) 2020 mouad banane, abdessamad belangour

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eng

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info:eu-repo/semantics/publishedVersion

Peer-reviewed Article