The work presented in this paper aims to develop a system for information retrieval which has the particularity of being adapted to the Arabic language and providing personalized results based on the user's preferences / interests. For this reason, we proposed a query expansion method based on both semantic knowledge from existing ontologies for the Arabic language and information from the users’ profiles. The proposed method is interesting for several reasons. Personalized information related to the users’ profiles to enrich the query should be considered since it is entirely appropriate and justified to improve traditional IR systems. Moreover, this idea has been little exploited for IR systems in Arabic. In addition, the coupling between information from the user's profiles and that contained in the ontologies is also an original and interesting track to develop. The suggested query expansion method has been implemented and tested on 150 queries for 50 users or 7500 queries of evaluation. The results of this evaluation are promising.

We proposed, in previous works, a business process execution language for data-driven workflow modeling, verification, and execution; the proposed model can present both control flow and shared data in workflow processes. It can be analyzed to verify its correctness before its deployment. In this paper, we are going to improve the semantics of our approach to allow a better integration between data and control flow, and between the workflow and its external environment (such as relational databases and XML documents). We are also going to extend our approach to generate multidimensional data models, namely OLAP hypercubes, from data handled by the workflow, in order to provide synthetic view of data for decision makers. 

We proposed, in previous works, a business process execution language for data-driven workflow modeling, verification, and execution; the proposed model can present both control flow and shared data in workflow processes. It can be analyzed to verify its correctness before its deployment. In this paper, we are going to improve the semantics of our approach to allow a better integration between data and control flow, and between the workflow and its external environment (such as relational databases and XML documents). We are also going to extend our approach to generate multidimensional data models, namely OLAP hypercubes, from data handled by the workflow, in order to provide synthetic view of data for decision makers. 

We proposed, in previous works, a business process execution language for data-driven workflow modeling, verification, and execution; the proposed model can present both control flow and shared data in workflow processes. It can be analyzed to verify its correctness before its deployment. In this paper, we are going to improve the semantics of our approach to allow a better integration between data and control flow, and between the workflow and its external environment (such as relational databases and XML documents). We are also going to extend our approach to generate multidimensional data models, namely OLAP hypercubes, from data handled by the workflow, in order to provide synthetic view of data for decision makers. 

We proposed, in previous works, a business process execution language for data-driven workflow modeling, verification, and execution; the proposed model can present both control flow and shared data in workflow processes. It can be analyzed to verify its correctness before its deployment. In this paper, we are going to improve the semantics of our approach to allow better integration between data and control flow, and between the workflow and its external environment (such as relational databases and XML documents). We are also going to extend our approach to generate multidimensional data models, namely OLAP hypercubes, from data handled by the workflow, in order to provide synthetic view of data for decision makers.

Web services are rapidly gaining acceptance as a fundamental technology in the web fields. They are becoming the cutting edge of communication between the different applications all over the web. Because of today's wide diversity of devices together with the variety of the user's preferences, context-aware web services are becoming a fundamental challenge that must be targeted. This issue is a part of the Human Computer Interaction (HCI) discipline and it aims at adapting the web service behavior according to the user's context such as his specific work environment, language, type of Internet connection, devices and preferences. Many solutions have been proposed in this area. Nevertheless, the adaptation was carried out only at the runtime and it partially covered the user's general context. In this paper, we introduce a new context-aware approach that provides adaptive web services. Our approach allows to express requirements by taking into account potential user's profile in addition to the functional one. While the latter ensures the description of the web service-functionalities, adaptation expresses the ability of a service to be self-adapted to runtime context changes. Our approach deals with adaptation from the very beginning of the modeling step of a web service. Furthermore, it upgrades description and publication usual methods in order to support profile specification.

Several challenges accompanied the growth of online social networks, such as grouping people with similar interest. Grouping like-minded people is of a high importance. Indeed, it leads to many applications like link prediction and friend or product suggestion, and explains various social phenomenon. In this paper, we present two methods of grouping like-minded people based on their textual posts. Compared to three baseline methods K-Means, LDA and the Scalable Multistage Clustering algorithm (SMSC), our algorithms achieves relative improvements on two corpora of tweets.