Modeling and exploitation of the traces of interactions in the collaborative working environment

Les sciences humaines et le progrès social ne peuvent pas se poursuivre sans collaboration. Avec le développement rapide des technologies de l'information et la popularité des appareils intelligents, le travail collaboratif est beaucoup plus simple et plus fréquents que jamais. Les gens peuvent travailler ensemble sans tenir compte de leur emplacement/ location géographique ou de la limitation de temps. Les environnements de travail de collaboration basés sur le Web sont conçus et consacrés à supporter/soutenir le travail individuel et le travail en groupe dans divers domaines: la recherche, les affaires, l'éducation, etc. N'importe quelle activité dans un système d'information produit un ensemble de traces. Dans un contexte de travail collaboratif, de telles traces peuvent être très volumineuses et hétérogènes. Pour un Environnement de Travail Collaboratif (ETC) typique Basé sur le Web, les traces sont principalement produites par des activités collaboratives ou des interactions collaboratives et peuvent être enregistrées. Les traces modélisées ne représentent pas seulement la connaissance, mais aussi l'expérience acquise par les acteurs via leurs interactions mutuelles ou les interactions qu'ils ont avec le système. Avec la complexité croissante de la structure de groupe et les besoins fréquents de collaboration, les interactions existantes deviennent de plus en plus difficiles à saisir et à analyser. Or, pour leurs travaux futurs, les gens ont souvent besoin de récupérer des informations issues de leurs activités de collaboration précédentes. Cette thèse se concentre sur la définition, la modélisation et l'exploitation des différentes traces dans le contexte d'Environnement de Travail Collaboratif et en particulier aux Traces Collaboratives dans l'espace de travail partagé de groupe (ou l'espace de travail collaboratif). Un modèle de traces de collaboration qui peuvent efficacement enrichir l'expérience du groupe et aider à la collaboration de groupe est proposé et détaillé. Nous présentons ensuite et définissons un type de filtre complexe comme un moyen possible d'exploiter ces traces. Plusieurs scénarios de base d'exploitation des traces collaboratives sont présentés. Pour chacun d'entre eux, nous présentons leurs effets et les avantages procurés par ces effets dans l'environnement de travail collaboratif. En effet, un cadre de l'exploitation des traces général est introduit et nous expliquons mis en œuvre dans un ETC. Trois approches collaboratives générant des traces sont discutées à l'aide d'exemples: l'Analyse SWOT, l'intégration de modèle de maturité de la capacité (CMMI) et le Système de Recommandation de Groupe. Une expérimentation de ce modèle a été réalisée dans le cadre de la plate-forme collaborative E-MEMORAe2.0. Cette expérience montre que notre modèle de trace collaborative et le cadre d'exploitation proposé pour l'environnement de travail collaboratif peuvent faciliter à la fois le travail personnel et de groupe. Notre approche peut être appliquée comme un moyen générique pour traiter différents sujets et problèmes, qu'il s'agisse de collaboration ou de l'exploitation des traces laissées dans un ECT.Human science and social progress cannot continue without collaboration. With the rapid development of information technologies and the popularity of smart devices, collaborative work is much simpler and more common than ever. People can work together irrespective of their geographical location or time limitation. In recently years, Web-based Collaborative Working Environments (CWE) are designed and devoted to support both individual and group work to a greater extent in various areas: research, business, learning and etc. Any activity in an information system produces a set of traces. In a collaborative working context, such traces may be very voluminous and heterogeneous. For a typical Webbased Collaborative Working Environment, traces are mainly produced by collaborative activities or interactions and can be recorded. The modeled traces not only represent knowledge but also experience concerning the interactive actions among the actors or between actors and the system. With the increasing complexity of group structure and frequent collaboration needs, the existing interactions become more difficult to grasp and to analyze. And for the future work, people often need to retrieve more information from their previous collaborative activities. This thesis focuses on defining, modeling and exploiting the various traces in the context of CWE, in particular, Collaborative Traces (CTs) in the group shared/collaborativeworkspace. A model of collaborative traces that can efficiently enrich group experience and assist group collaboration is proposed and detailed. In addition, we introduce and define a type of complex filter as a possible means to exploit the traces. Several basic scenarios of collaborative traces exploitation are presented describing their effects and advantages in CWE. Furthermore, a general traces exploitation framework is introduced and implemented in CWE. Three possible traces based collaborative approaches are discussed with comprehensive examples: SWOT Analysis, Capability Maturity Model Integration (CMMI) and Group Recommendation System. As a practical experience we tested our model in the context of the E-MEMORAe2.0 collaborative platform. Practical cases show that our proposed CT model and the exploitation framework for CWE can facilitate both personal and group work. This approach can be applied as a generic way for addressing different types of collaboration and trace issues/problems in CWE.COMPIEGNE-BU (601592101) / SudocSudocFranceF

PETRA: Process Evolution using a TRAce-based system on a maintenance platform

To meet increasing needs in the field of maintenance, we studied the dynamic aspect of process and services on a maintenance platform, a major challenge in process mining and knowledge engineering. Hence, we propose a dynamic experience feedback approach to exploit maintenance process behaviors in real execution of the maintenance platform. An active learning process exploiting event log is introduced by taking into account the dynamic aspect of knowledge using trace engineering. Our proposal makes explicit the underlying knowledge of platform users by means of a trace-based system called “PETRA”. The goal of this system is to extract new knowledge rules about transitions and activities in maintenance processes from previous platform executions as well as its user (i.e. maintenance operators) interactions. While following a Knowledge Traces Discovery process and handling the maintenance ontology IMAMO, “PETRA” is composed of three main subsystems: tracking, learning and knowledge capitalization. The capitalized rules are shared in the platform knowledge base in order to be reused in future process executions. The feasibility of this method is proven through concrete use cases involving four maintenance processes and their simulation

A Web-based System for Observing and Analyzing Computer Mediated Communications

Tracking data of user's activities resulting from Computer Mediated Communication (CMC) tools (forum, chat, etc.) is often carried out in an ad-hoc manner, which either confines the reusability of data in different purposes or makes data exploitation difficult. Our research works are biased toward methodological challenges involved in designing and developing a generic system for tracking user's activities while interacting with asynchronous communication tools like discussion forums. We present in this paper, an approach for building a Web-based system for observing and analyzing user activity on any type of discussion forums

Recommender Systems for Online and Mobile Social Networks: A survey

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area

CARTE: An Observation Station to Regulate Activity in a Learning Context

This chapter discusses the introduction of a new concept called "regulation" into a use model, which is part of a theoretical observation model called trace-based system (TBS). This concept defines a retroaction mechanism in an observation station. We present the results of experiments, in a learning context, with a prototype observation station called Collection, activity Analysis and Regulation based on Traces Enriched (CARTE)

DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments

With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST