Model and Data Engineering 

Abstract: Students' performance prediction in distance higher education has been widely researched over the past decades. Machine learning techniques and especially supervised learning have been used in numerous studies to identify in time students that are possible to fail in final exams. The identification of in case failure as soon as possible, could lead the academic staff to develop learning strategies aiming to improve students' overall performance. In this paper, we investigate the effectiveness of semi-supervised techniques in predicting students' performance in distance higher education. Several experiments take place in our research comparing to the accuracy measures of familiar semi-supervised algorithms. As far as, we are aware various researches deal with students' performance prediction in distance learning by using machine learning techniques and especially supervised methods, but none of them investigate the effectiveness of semi-supervised algorithms. Our results confirm the advantage of semi-supervised methods and especially the satisfactory performance of Tri-Training algorithm.

Abstract: Recent research initiatives dedicated to formal modeling, functional correctness and security analysis of IoT systems, are generally limited to, model abstract behavioral patterns and look forward possible attacks beneath gauging and providing feasible attacks. This research considers the complementary problem by looking for more accurate attacks in IoT by capturing richer behaviors -technical, physical, and social- including their quantitative features. We propose IoT-SEC framework that establishes an adequate semantics to the IoT’s components and their interactions including social actors that behave differently than automated processes. For security analysis, we develop a general approach based on a library of attack trees from where we generate automatically the monitor, the security policies and requirements to harden the IoT model and to check how well the model is secure. We use PRISM model checker to analyze the functionality and to check security of the IoT model. Precisely this contribution ensures the functionality of IoT systems by analyzing their functional correctness.

Abstract: The work presented in this paper aims to build OLAP cubes from big data warehouses implemented by using the columnar NoSQL model. The use of NoSQL models is motivated by the inability of the relational model, usually used to implement data warehousing, to allow data scalability easily. Indeed, the columnar NoSQL model is suitable for storing and managing massive data, especially for decisional queries. However, the column-oriented NoSQL DBMS do not offer online analysis operators (OLAP). Our main contribution is to define a new cube operator called MC-CUBE (MapReduce Columnar CUBE), which allows building columnar NoSQL cubes by taking into account the no relational and distributed aspects when data warehouses are stored.

Abstract: Twitter, currently the leading microblogging social network, has attracted a great body of research works. This paper proposes a data analysis framework to discover groups of similar twitter messages posted on a given event. By analyzing these groups, user emotions or thoughts that seem to be associated with specific events can be extracted, as well as aspects characterizing events according to user perception. To deal with the inherent sparseness of micro-messages, the proposed approach relies on a multiple-level strategy that allows clustering text data with a variable distribution. Clusters are then characterized through the most representative words appearing in their messages, and association rules are used to highlight correlations among these words. To measure the relevance of specific words for a given event, text data has been represented in the Vector Space Model using the TF-IDF weighting score. As a case study, two real Twitter datasets have been analysed.