A grid-based approach for processing group activity log files

The information collected regarding group activity in a collaborative learning environment requires classifying, structuring and processing. The aim is to process this information in order to extract, reveal and provide students and tutors with valuable knowledge, awareness and feedback in order to successfully perform the collaborative learning activity. However, the large amount of information generated during online group activity may be time-consuming to process and, hence, can hinder the real-time delivery of the information. In this study we show how a Grid-based paradigm can be used to effectively process and present the information regarding group activity gathered in the log files under a collaborative environment. The computational power of the Grid makes it possible to process a huge amount of event information, compute statistical results and present them, when needed, to the members of the online group and the tutors, who are geographically distributed.Peer ReviewedPostprint (author's final draft

E-Learning for Teachers and Trainers : Innovative Practices, Skills and Competences

Reproduction is authorised provided the source is acknowledged.Final Published versio

21st Century Simulation: Exploiting High Performance Computing and Data Analysis

This paper identifies, defines, and analyzes the limitations imposed on Modeling and Simulation by outmoded paradigms in computer utilization and data analysis. The authors then discuss two emerging capabilities to overcome these limitations: High Performance Parallel Computing and Advanced Data Analysis. First, parallel computing, in supercomputers and Linux clusters, has proven effective by providing users an advantage in computing power. This has been characterized as a ten-year lead over the use of single-processor computers. Second, advanced data analysis techniques are both necessitated and enabled by this leap in computing power. JFCOM's JESPP project is one of the few simulation initiatives to effectively embrace these concepts. The challenges facing the defense analyst today have grown to include the need to consider operations among non-combatant populations, to focus on impacts to civilian infrastructure, to differentiate combatants from non-combatants, and to understand non-linear, asymmetric warfare. These requirements stretch both current computational techniques and data analysis methodologies. In this paper, documented examples and potential solutions will be advanced. The authors discuss the paths to successful implementation based on their experience. Reviewed technologies include parallel computing, cluster computing, grid computing, data logging, OpsResearch, database advances, data mining, evolutionary computing, genetic algorithms, and Monte Carlo sensitivity analyses. The modeling and simulation community has significant potential to provide more opportunities for training and analysis. Simulations must include increasingly sophisticated environments, better emulations of foes, and more realistic civilian populations. Overcoming the implementation challenges will produce dramatically better insights, for trainees and analysts. High Performance Parallel Computing and Advanced Data Analysis promise increased understanding of future vulnerabilities to help avoid unneeded mission failures and unacceptable personnel losses. The authors set forth road maps for rapid prototyping and adoption of advanced capabilities. They discuss the beneficial impact of embracing these technologies, as well as risk mitigation required to ensure success

Stakeholder involvement, motivation, responsibility, communication: How to design usable security in e-Science

e-Science projects face a difficult challenge in providing access to valuable computational resources, data and software to large communities of distributed users. Oil the one hand, the raison d'etre of the projects is to encourage members of their research communities to use the resources provided. Oil the other hand, the threats to these resources from online attacks require robust and effective Security to mitigate the risks faced. This raises two issues: ensuring that (I) the security mechanisms put in place are usable by the different users of the system, and (2) the security of the overall system satisfies the security needs of all its different stakeholders. A failure to address either of these issues call seriously jeopardise the success of e-Science projects.The aim of this paper is to firstly provide a detailed understanding of how these challenges call present themselves in practice in the development of e-Science applications. Secondly, this paper examines the steps that projects can undertake to ensure that security requirements are correctly identified, and security measures are usable by the intended research community. The research presented in this paper is based Oil four case studies of c-Science projects. Security design traditionally uses expert analysis of risks to the technology and deploys appropriate countermeasures to deal with them. However, these case studies highlight the importance of involving all stakeholders in the process of identifying security needs and designing secure and usable systems.For each case study, transcripts of the security analysis and design sessions were analysed to gain insight into the issues and factors that surround the design of usable security. The analysis concludes with a model explaining the relationships between the most important factors identified. This includes a detailed examination of the roles of responsibility, motivation and communication of stakeholders in the ongoing process of designing usable secure socio-technical systems such as e-Science. (C) 2007 Elsevier Ltd. All rights reserved