Yassine Jamoussi

A Discrete-Events Simulation Approach for Evaluation of Service-Based Applications

One of the promises of the service-oriented architecture(SOA) is to build complex added-value services in order to enhance and extend existing ones. service-based applications(SBAs) are asked not only to perform required functionalities,but also to deliver expected level of Quality of Service (QoS). Dealing with QoS management of such distributed applications, which are executed in dynamic environments,raises the need to consider context characteristics.This paper proposes a discrete-events simulation approach which assures the evaluation of SBAs performance under different context status. The main contributions of this paper are: (i) the discrete-events modeling approach for SBAs, (ii) the context-based model for SBAs considered in the discrete-events simulation model, and (iii) the evaluation of a set of QoS metrics by simulation.

Comprehensive classification of collaboration approaches in E-learning

Abstract There are a number of approaches to learning such as traditional approaches (teacher-centered) and collaborative approaches (learner-centered). Nowadays, the concepts of collaboration and social interactions are the major trends in education. Therefore, many researchers embrace these concepts to offer the educational field enhanced learning environments which are supported by communication and collaboration techniques. The adaptation causes the existence of varied approaches which are addressing the collaborative learning techniques. As a result, there is a need for a mechanism to study those approaches and highlight their eminence. The aim of this paper is to give a comprehensive overview about the state-of-art in collaborative learning, especially by integrating social media tools. To do so, the study adopts a classification framework based on four different views (subject, purpose, method, and tool). The framework has been used to compare ten collaborative e-learning approaches. The finding indicates the potential of all approaches in developing an online learning environment for remote collaborative learning despite the lack of fulfilling all the requirements highlighted in the four views.

Recommendation in Interactive Web Services Composition: A State-of-the-Art Survey

With the increasing adoption of Web services, designing novel approaches for recommending relevant Web services has become of paramount importance especially to support many practical applications such as Web services composition. In this paper, a survey aiming at encompassing the state-of-the-art of interactive Web services composition recommendation approaches is presented. Both Web services composition and recommender systems concepts are introduced and their particular challenges are also discussed. Moreover, the need of using recommendation techniques to support Web services composition is also highlighted. The most relevant approaches dedicated to address this need are presented, categorized and compared.

An extracting model for constructing actions with improved part-of-speech tagging from social networking texts

The recent viral growth of social network systems such as Twitter, Facebook and MySpace have created many interesting and challenging problems to the research community, which enable to perform context aware-reasoning. Social networking is a set of social actors (individuals or organizations) that are connected to provide a set of interaction. We consider, in this paper, the problem of information extraction from social networking specially Twitter and Facebook. To extract text from social networking, we need several lexical features and large scale word clustering. We attempt to expand existing tokenizer and to develop our own tagger in order to support the incorrect words currently in existence in Facebook and Twitter. Our goal in this work is to benefit of the lexical features developed for Twitter and online conversational text in previous works, and to develop an extraction model for constructing a huge knowledge based on actions.

A scheme for extracting information from collaborative social interaction tools for personalized educational environments

Personalized e-learning systems are mainly structured based on two basic models: domain model and user model. When considering personalization in a collaborative environment with user-generated content via Web 2.0 technologies, the task of generating the models is very challenging. This paper presents an approach to extract information related to the domain model and user model for the purpose of personalization. The extraction is using chat conversations using different social media tools during collaborative learning as sources for the text analysis. Text mining technique has been incorporated to clean the collected data and extract the required information.