Abstract The widespread use of e-learning applications has put emphasis on the importance of having applications more personalized and adaptable to every learner needs. The one size fits all is no more working. Every learner should be delivered the right learning material that suits its learning context in the right time. The challenge is to incorporate the recommendation system in e-learning platforms in order... The widespread use of e-learning applications has put emphasis on the importance of having applications more personalized and adaptable to every learner needs. The one size fits all is no more working. Every learner should be delivered the right learning material that suits its learning context in the right time. The challenge is to incorporate the recommendation system in e-learning platforms in order to offer to learners a successful learning experience. In response to this challenge, in this paper, we propose semantic web architecture of a context recommendation system in e-learning by means of which the learners will be offered learning content based on their profiles, activities and social interactions. The proposed architecture is a re-engineering of classical web architecture of current e-learning platforms. It’s based on semantic web technologies. It comprises ontology that guarantee a shareable and reusable modeling of the learning context and OWL Rules filtering that will be used as recommendation technique. Read More Read Less

Abstract Memories have always been a considerable importance of a persons life and experiences. A digital human memory as a field of study focuses on encapsulating this phenomenon, in digital form, during the thread of a lifetime. By spreading hardware everywhere, massive amount of data is being generated together by people and the surrounding environment. With all this demountable information available, successfully exploring, researching... Memories have always been a considerable importance of a persons life and experiences. A digital human memory as a field of study focuses on encapsulating this phenomenon, in digital form, during the thread of a lifetime. By spreading hardware everywhere, massive amount of data is being generated together by people and the surrounding environment. With all this demountable information available, successfully exploring, researching and collating, together, to form a human digital memory, is a new challenge and requires novel and efficient algorithmic solutions. The main goal of this work is going to propose a new method to automatically create rich and dynamic human digital memory in egocentric dataset from the lifelogging images of a person. For this purpose, we will propose a technique using Convolutional Neural Network (CNN) model. For validation, we will apply the proposed method on the Egocentric Dataset of University of Barcelona (EDUB) of 4912 daily images acquired by four persons. Read More Read Less

Abstract Cloud Computing provides a flexible environment for customers to host and process their information through an outsourced infrastructure. This information was habitually located on local servers. Many applications dealing with massive data is routed to the cloud. Data Warehouse (DW) also benefits from this new paradigm to provide analytical data online and in real time. DW in the Cloud benefited of its advantages... Cloud Computing provides a flexible environment for customers to host and process their information through an outsourced infrastructure. This information was habitually located on local servers. Many applications dealing with massive data is routed to the cloud. Data Warehouse (DW) also benefits from this new paradigm to provide analytical data online and in real time. DW in the Cloud benefited of its advantages such flexibility, availability, adaptability, scalability, virtualization, etc. Improving the DW performance in the cloud requires the optimization of data processing time. The classical optimization techniques (indexing, materialized views and partitioning) are still essential for DW in the cloud. However, the DW is partitioned before being distributed across multiple servers (nodes) in the Cloud. When query containing multiple joins or ask voluminous data stored on multiple nodes, inter-node communication increases and consequently the DW performance degrades. In this paper, we propose an approach for improving the performance of DW in the cloud. Our approach is based on selection of temporary materialized views through a web service. For this purpose we use an algorithm allows to identify the queries list sent to the DW, and adds a materialized view for each new costly frequent query. This technique is based on managing temporary materialized views in order to optimize the frequent queries load by respecting the total cost. An experimental study on a cloud DW is carried out and a comparative tests show the satisfaction of our approach. Read More Read Less