International audience

The technological revolution, the huge sharing of data via social networks, web and mobile applications and IoT devices are generating a huge volume of data every day, commonly referred to as “Big Data”. To cope with Big Data and the challenges associated with their specific features, the last decade, a multitude of technologies and platforms have emerged to harness their potential. The community is still seeking a comprehensive and up-to-date comparative study of these tools. Such an experimentally-derived study is essential for enabling informed decision-making, fostering innovation, and ensuring that organizations can make the best choices when implementing Big Data solutions. In this paper, a multi-purpose experimental study was conducted. The primary objective is to provide an overview of today’s most popular Big Data tools, and to evaluate their interoperability. The second is to test performance by varying different technical constraints. The aim of these tests is twofold: i) To compare the resource consumption requirements of these tools, ii) To evaluate the impact of resource variation of one tool on the performance of another one in the same Big Data pipeline.

Machine Learning (ML) is a science that forces computers to learn and behave like humans. As these systems interact with data, networks, and people, they automatically become smarter so that they can eventually solve or predict a practical issue in the world for us. The use of ML can be a giant leap for cannot simply be integrated as the top layer. This requires redefining workflow, architecture, data collection and storage, analytics, and other modules. This paper aims to discuss the issue of machine learning technique for analysis data of mobile user. First, we identified the machine learning benefits and drawbacks, challenges, advantages of using Machine Learning. Then, we propose a generic model of analytic mobile user data using ML, the model is centered on the machine learning component, which interacts with two other components, including mobile user data, and system. The interactions go in both directions. For instance, mobile user data serves as inputs to the learning component and the latter generates outputs; system architecture has impact on how learning algorithms should run and how efficient it is to run them, and simultaneously meeting. Mobile user data goes through several stages: prepossessing which includes the steps we need to follow to transform or encode the data so that it can be easily analyzed by the machine. Then, modelling in this step we will be clustering and classification the data obtained. Finally, evaluation, various measures of performance, accuracy, recall, precision, and F-measure were used to analyze the results of the naive Bayes, SVM, and K-nearest neighbor classification algorithms.

Privacy and Security in big data and smart cities play a major role to ensure better quality of citizens life. Privacy emphasizes on the data being collected, shared, and used in the right manner, and security focuses on protecting the data from intruders’ attack, and exploitation of data for other purposes such as criminal behavior. This paper aims to discuss the issue of privacy and security of mobile users in big data and smart cities. First, based on big data privacy and security challenges, classification, and models, we identify the privacy and security requirements for a mobile user. Then, we propose a generic architecture and an algorithm for the management of privacy and security of a mobile user in a smart environment. This architecture groups the main components required for implementing the proposed algorithm that ensure the privacy and security of mobile users in smart cities.

Most researches on ubiquitous computing focus on context capture and adaptation. The lack of a well-defined context model inhibits identification, reasoning and reuse of context and context-aware elements. In this paper we propose the development of model-driven context-aware applications. We present the COMODE framework for modeling context-aware applications in the context of MDE (model driven engineering). By concerns separation (business and contextual ones) in individual models and by transformation techniques context can be provided, modeled and adapted independently of business logic and platform details. We also present in this paper our proposition of a context metamodel and the parameterized transformation to apply context into business models.