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

Abstract

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.

Exploiting Cloud Computing and Web Services to Achieve Data Consistency, Availability, and Partition Tolerance in the Large-Scale Pervasive Systems

“…Replication is a crucial process for effectively support shared data in a variety of distributed environments (e.g., grid, mobile, cloud, and P2P environments) [25,[28][29][30]. It intends to provide several positive outcomes in these environments, including improve data availability, enhance fault tolerance, minimize bandwidth consumption, reduce data access latency, and decrease data transfer cost and time [28,31,32,54]. There are many replication approaches attempted for such environments.…”

A Comparative Study of Microservices-based IoT Platforms

“…However, the management of QoS parameters is not integrated. In [28,29], the authors propose a system for managing semantic data in a Cloud / Big Data context, which can be easily used with a Spark query tool [30,31].…”

Devops and Microservices Based Internet of Things Meta-Model

“…A methodological component is directly executable since it is a service that is written with SOA standards, while the composition of services is expressed informally at a high level of abstraction using intentional Map. Banana et al [34] proposed a metamodel of Hive [35] and Spark [36], as well as another metamodel [37] of MongoDB query language. The IoT is responsible for the collection and / or creation of a large volume of data.…”

“…The IoT is responsible for the collection and / or creation of a large volume of data. This enormous volume of data, known by the term "Big Data" [38,39,57,58], allows, on the one hand, to have an incredible wealth in terms of information allowing the offer of advanced services. On the other hand, this volume of data creates new challenges to be considered such as securing, processing, and real-time accessibility of this data [40,41].…”

“…Each package represents a Meta-model. The objective of this Meta-model is to generate an Internet system of objects based on microservices and supported by Devops technologies: Docker, Ansible & Kubernetes.In future works, we will use this Meta-model to realize a model transformation using ATL Language [54,55,56,57], and Model Driven Engineering approach [58,59]…”