Hafed Zarzour

srCE: a collaborative editing of scalable semantic stores on P2P networks

Commutative Replicated Data Type CRDT is a convergence philosophy invented as a new generation of technique that ensures consistency maintenance of replica in collaborative editors without any difficulty over Peer-to-Peer P2P networks. This technique has been successfully applied to different data representation types in scalable collaborative editing for linear, tree document structure and semi-structured data types but not yet on set data type ensuring Causality, Consistency and Intention CCI preservation criteria. In this paper, we propose a srCE approach, a novel CRDT for a set structure to facilitate the collaborative and concurrent editing of Resource Description Framework RDF stores in large scale by different members of virtual community. Our approach ensures CCI model and is not tied to a specific case and therefore can be applied for any document that complies to set structure. A prototype implementation using Friend of a Friend FOAF data sets with and without the srCE model illustrates significant improvement in scalability and performance.

Conflict-Free Collaborative Decision-Making over Mind-Mapping

As the integration of disaster management, distributed computing, and collaborative technologies, collaborative disaster management systems can overcome the challenges, such as data replication and updating, real-time information dissemination, distributed resources sharing, and collaborative decision-making. Collaborative decision-making systems over Mind-Mapping are modeled by a set of sites connected by a communication network with each site hosting a replica of shared data. When a site executes a modification, it generates a corresponding operation that is performed immediately on its local copy, and then is propagated to all sites in order to be executed remotely. However, if the consistency is not properly guaranteed it can lead to divergences, practically, in the case of concurrent decisions over the shared Mind-Mapping. This paper aims to provide a solution to the problem of consistency in collaborative decision-making over Mind- Mapping. This solution allows distributed users to work together to reach a common goal without central servers and coordination among them is achieved in a Peer-to-Peer manner. A prototype developed about nuclear disaster scenario demonstrates the effectiveness of our approach.

p2pCoSU: A P2P Sparql/update for collaborative authoring of triple-stores

An important topic within Computer Supported Collaborative Work is collaborative editing or authoring system, which has been an interesting research area by the release of Web 2.0 products including Social Networks, Wikipedia, CMS, Google Docs, Blogs and many, many more. SPARQL/UPDATE is emerging as a reaction to this challenge. However, the current standard allows only a single authoring of triple-stores and does not provide transparent mechanism to support collaborative authoring. Furthermore, maintaining consistency between distributed triple-stores executing concurrent operations is a difficult problem. To solve this problem, this paper proposes a novel p2pCoSU solution that supports collaborative authoring for P2P semantic triple-stores and ensures causality, consistency and intention preservation criteria. We evaluate and compare the performance of the proposed p2pCoSU and related approaches by simulation; the results show that our solution is efficient and scalable.

Achieving consistency in collaborative image annotation systems

Collaborative image annotation is a useful strategy for assigning a set of labels, or keywords to an image, taking into account its content. While existing collaborative image annotation frameworks facilitate sharing, indexing, and understanding of huge number of images, newly-developed methods let group of users manage dynamically-updating data. Conflict-free Replicated Data Type method, used in several collaborative systems, offers to distributed participants through the web a real support to concurrently annotate copies of shared image file regardless of their order. A major benefit of this method, therefore, is to provide a good framework for representing semantic stores and a good mechanism for solving the concurrent updating problem without complex control in collaborative image annotation process. In this paper, we propose a novel optimistic replication approach for collaborative replicated image annotation stores that ensures eventual consistency. We describe how Open Annotation Collaboration can be extended to further support real collaboration between users allowing them to work together effectively to achieve common goals.

Effects of a linked data-based annotation approach on students’ learning achievement and cognitive load

ABSTRACT In this study, a linked data-based annotation approach is proposed. A learning system has been developed based on the approach by providing an annotating function, a linked data enrichment function, a sharing function and faceted search function. To evaluate the effectiveness of this innovative approach, an experiment was carried out in which two classes of students participated. The first class served as the experimental group, in which the students learned with the proposed approach, and the second class served as the control group, in which the students learned with the conventional annotation approach. The experiment results show that the experimental group significantly outperformed the control group. Moreover, the cognitive load of the students in the experimental group was significantly lower than the ones in the control group. This implies that the linked data-based annotation approach not only reduced the students’ cognitive load, but also improved their learning achievement.

An investigation into whether learning performance can be improved by CAALDT

Abstract In this study, a collaborative annotation approach based on Linked Data technology (CAALDT) is proposed for improving students’ learning. An experiment was conducted to evaluate the effectiveness of the proposed approach by comparing the learning performance of the students who learned with CAALDT and those who learned with the conventional collaborative annotation approach. From the experimental results, it was found that the proposed approach improved not only the students’ learning achievements, but also their learning motivation and attitudes. Moreover, it was found that the CAALDT approach provided more challenging learning opportunities that enabled students to review the annotations of their peers or teacher as well as to expose their annotations as Linked Data, therefore contributing to the expansion of the learning contents.

Facial Emotion Detection in Massive Open Online Courses

Recently, the Massive Open Online Course (MOOC) has appeared as a new emerging method of online teaching with the advantages of low cost and unlimited participation as well as open access via the web. However, the use of facial emotion detection in MOOCs is still unexplored and challenging. In this paper, we propose a new innovative approach for facial emotion detection in MOOCs, which provides an adaptive learning content based on students’ emotional states and their profiles. Our approach is based on three principles: (i) modeling the learner using the MOOC (ii) using of pedagogical agents during the learning activities (iii) capturing and interpreting the facial emotion of the students. The proposed approach was implemented and tested in a case study on the MOOC.

Smart cities based on web semantic technologies

A good communication and interaction between citizens and the administration is important and crucial, also can greatly help in improving the quality of urban life of citizen. In this paper, we propose a semantic data model for managing and resolving the problems that exist in cities such as water leak, street faults, broken street lights, and potholes. The main idea is to focus on the best practices of linked open data to describe all issues, and then integrate them in the dataset provided by DBpedia. Hence, our approach is based on the standards of the World Wide Web Consortium.

Using commutative replicated data type for collaborative video annotation

Collaborative video annotation system is groupware system which enables a virtual community of participants to share and annotate the same digital video file from geographically dispersed nodes interconnected via the network. The video annotation process allows participants to browse videos, add, delete or update annotations. However, the existing systems are mainly concerned with the indexing, annotating, storing and sharing of video data. Hence, they only provide a basis for implementing simple way for video annotation and do not treat concurrent annotation aspects during the collaborative work. In this paper, we describe AV-Store an original method that combines both advantages of collaborative video annotation and semantic web technologies, and complies with eventual consistency condition when concurrent annotations are performed. The main idea of this approach is to define a new data type where all concurrent annotations commute. The commutativity aims at assuring the consistency of all replicas if participants perform the same sequence of annotations in different orders.

An Improved Collaborative Filtering Recommendation Algorithm for Big Data

With the increase of volume, velocity, and variety of big data, the traditional collaborative filtering recommendation algorithm, which recommends the items based on the ratings from those like-minded users, becomes more and more inefficient. In this paper, two varieties of algorithms for collaborative filtering recommendation system are proposed. The first one uses the improved k-means clustering technique while the second one uses the improved k-means clustering technique coupled with Principal Component Analysis as a dimensionality reduction method to enhance the recommendation accuracy for big data. The experimental results show that the proposed algorithms have better recommendation performance than the traditional collaborative filtering recommendation algorithm.