Volume: 01, Issue: ICCIT- 1441, Page No.: 35 – 39, 9 th & 10 th Sep. 2020 2020 International Conference on Computing and Information Technology, University of Tabuk, Kingdom of Saudi Arabia. Volume: 01, Issue: ICCIT- 1441, Page No.: 35 – 39, 9 th & 10 th Sep. 2020 A new scalable semantic Web system based on Big Data: A use case in the mobile learning Mouad Banane Dept. of Computer science, Laboratory of Information Technology Faculty of Science, Hassan II University Casablanca, Morocco mouad.banane-etu@etu.univh2c.ma Allae Erraissi Dept. of Computer science, Laboratory of Information Technology Faculty of Science, Hassan II University Casablanca, Morocco erraissi.allae@gmail.com Abdessamad Belangour Dept. of Computer science, Laboratory of Information Technology Faculty of Science, Hassan II University Casablanca, Morocco belangour@gmail.com Abstract— The semantic web allows machines to understand the meaning of data and to make better use of it.. 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. Keywords—Big Data, Semantic Web, Mobile Learning. I. INTRODUCTION In view of the rapid emergence of new mobile technologies and the growth of the offerings and needs of moving society in training, work is on the increase to identify new relevant learning platforms to improve and facilitate the process of learning. “Distance learning”[1]. The next step in distance learning is, of course, the port of e-Learning to new mobile systems. This is called M-Learning[2,3] (mobile learning). The search for information in the field of m-learning can be defined as an activity whose purpose is to locate and deliver learning contents to a learner according to his need for information and its context. Until now, the learning environment was either defined by a pedagogical framework or imposed by the learning content. Recent years have been marked by the rise of mobile learning or m-learning, driven by the continued development of new mobile technologies. Learning becomes situated, contextual, and personal. This phenomenon encourages the evolution of learning methods to adapt to this new type of learning. New uses in the field of learning have multiplied in different ways. In the context of learning within companies, we seek to develop an M-Learning system whose main issues are: (1) learning at work whatever the time, place, delivery device, the technological constraints of the learning process and adapted to the learner's profile; (2) learning without breaking. In this paper, we propose a scalable and powerful Big Data recommendation sys-tem based on Semantic Web technologies. This system is composed of two main layers, in the semantic knowledge layer we use an M-Learning domain ontology, and in the storage layer, we use a document-oriented NoSQL database named MongoDB for data management. This system is a case of using our RDFMongo [4] solution, which presents a complete system for managing massive semantic web data. This paper is organized as follows. Section 1 introduces the notion and emergence of mobile learning technologies and the standard representation of semantic data (RDF). We make a brief state of the art on existing work that deals with the topic of using Semantic Web technologies in the area of M- Learning in Section 3. Section 4 is devoted to the main contribution of our work with the syntax and architecture of our solution. Section 6 focuses on evaluating the performance of the solution implemented on real datasets from databases and standards. Finally, we give the conclusion and perspectives to our work. II. RELATED WORK The goal of mobile learning research is to build a learning environment where activities need to adapt to the learner's mobility situation using new digital technologies. The representation of the abstract model in a specific format is called binding. Today there are two bindings of the LOM schema: either of the XML binding or the RDF binding: The XML binding is easy to implement, however, it remains insufficient for the representation of all the elements of LOM since it does not allow to express the semantics of these elements. The RDF binding defines a set of RDF constructs that facilitate the introduction of LOM metadata into the web and is complemented by RDFS for defining classes, properties, and so on. The advantage of this second type of binding is that it adds semantics to the elements of LOM, except that it is not expressive enough to define all the constraints of LOM. Consider the "Title" and "Entry" elements of the "General" category that are mandatory elements in the LOM. Using RDF and RDFS one cannot specify that a property is mandatory or constrain its use at one time for a resource. As a second example, RDF and RDFS do not allow to express the inverse of a relation: thus, to say that a LOx "has a part" a LO y, will not allow inducing that the LO y "is part of "LOx. This lack of expressiveness leads us to think of the use of another more powerful formalism. In order to determine which language is the most appropriate for solving the expressiveness problem, we have focused on identifying the necessary description logic (LD). The LD is a family of formalisms to represent knowledge in a structured and formal way. A fundamental characteristic of these languages is that they have a formal descriptive semantics. We start from a minimal logic ALC and we add to this logic the constructors necessary to define all the constraints of LOM.