A GRID-BASED E-LEARNING MODEL FOR OPEN UNIVERSITIES

E-learning has grown to become a widely accepted method of learning all over the world. As a result, many e-learning platforms which have been developed based on varying technologies were faced with some limitations ranging from storage capability, computing power, to availability or access to the learning support infrastructures. This has brought about the need to develop ways to effectively manage and share the limited resources available in the e-learning platform. Grid computing technology has the capability to enhance the quality of pedagogy on the e-learning platform. In this paper we propose a Grid-based e-learning model for Open Universities. An attribute of such universities is the setting up of multiple remotely located campuses within a country. The grid-based e-learning model presented in this work possesses the attributes of an elegant architectural framework that will facilitate efficient use of available e-learning resources and cost reduction, leading to general improvement of the overall quality of the operations of open universities

Electronic Publishing in academic contexts: quality control and usage scenarios

Ziel der Arbeit ist die Entwicklung eines qualitätsorientierten Beschreibungsmodells für Nutzungsszenarien in E-Publishing und E-Learning. Wichtige Trends wie die Transparenz und Offenheit von Formaten, Strukturen und Prozessen werden einbezogen (Open...-Bewegung), ebenso die Frage nach kollaborativen Ansätzen in der Wissenschafts- und Bildungskommunikation (Web 2.0 und Community-basierte Ansätze). Zentral ist dabei neben der qualitätsorientierten Herangehensweise vor allem die Erarbeitung eines integrierten Ansatzes für deren Unterstützung und qualitätsorientierte Entwicklung und die Identifikation verschiedenener Nutzungsszenarien. Zunächst wird ein detaillierter Blick auf das elektronische Publizieren und Lernen im wissenschaftlichen – vorwiegend universitären – Umfeld geworfen. Der Markt der Bildungsökonomie wird untersucht, Produktion, Distribution, Evaluation und Nutzung werden kritisch beleuchtet. Nach einer aktuellen Darstellung des Qualitätsbegriffs folgt die Entwicklung eines umfassenden und konsistenten Modells zur qualitätsorientierten Beschreibung und Bewertung von internetbasierten Publishing- und Learning-Services und -Produkten. Dieses wird dann angewandt auf diverse Nutzungsszenarien wie z.B. Blended Learning, Print-on-Demand, Collaborative Publishing, Digitalisierungsprojekte oder Sicherstellung der Langzeitverfügbarkeit. Dabei bettet die Arbeit das Thema breit in einen Rahmen aus Informationswirtschaft, Informatik, E-Business, Wissensmanagement, E-Learning, Wirtschaft und Qualitätsmanagement ein. Zentrales Ergebnis ist ein sehr breit anwendbares Beschreibungs- und Bewertungsmodell für digitale Content-Angebote und -Dienstleistungen. Ein abschließendes Kapitel entwickelt eine Typologie der Geschäftsmodelle einschlägiger Service-Anbieter und wirft einen Blick auf zukunfts- und konfliktträchtige Themen wie eScience und Grid, Plagiate oder mandatorische Open Access-Policies.The work's main purpose is the development of a quality-centered description model for usage scenarios in E-publishing and E-Learning. Important trends like transparency and openness of formats, structures and processes are integrated (Open...-Movement) as well as aspects of collaborative attempts in scientific and educational communication (Web 2.0 and community-based scenarios). Beside the quality-oriented approach, the central idea is the development of an integrated model for description and evaluation of digital content products and services, for their support and development, and the identification of different usage scenarios. At first a detailed look is directed at electronic publishing and learning in the scientific - mainly academic - sphere. The market and economic circumstances of higher education are examined, production, distribution, evaluation and reception are critically analyzed. A topical representation of the concepts of quality is followed by the development of a comprehensive and consistent model for quality-oriented description and assessment of internet-based publishing and learning services and products. This model then is applied to various usage scenarios such as blended learning, print-on-demand, collaborative publishing, digitization projects or long term preservation. The subject is examined on a broad basis, covering e-business, information business, knowledge management, e-learning and quality management, information technology and computer science. Central result is a very broadly applicable description and assessment model for digital content products, services and service providers. A final chapter draws a typology system of appropriate business models and looks at promising or controversial subjects like eScience and Grid, plagiarism or mandatory Open Access policies

Grid Global Behavior Prediction

Complexity has always been one of the most important issues in distributed computing. From the first clusters to grid and now cloud computing, dealing correctly and efficiently with system complexity is the key to taking technology a step further. In this sense, global behavior modeling is an innovative methodology aimed at understanding the grid behavior. The main objective of this methodology is to synthesize the grid's vast, heterogeneous nature into a simple but powerful behavior model, represented in the form of a single, abstract entity, with a global state. Global behavior modeling has proved to be very useful in effectively managing grid complexity but, in many cases, deeper knowledge is needed. It generates a descriptive model that could be greatly improved if extended not only to explain behavior, but also to predict it. In this paper we present a prediction methodology whose objective is to define the techniques needed to create global behavior prediction models for grid systems. This global behavior prediction can benefit grid management, specially in areas such as fault tolerance or job scheduling. The paper presents experimental results obtained in real scenarios in order to validate this approach

The Knowledge Life Cycle for e-learning

In this paper, we examine the semantic aspects of e-learning from both pedagogical and technological points of view. We suggest that if semantics are to fulfil their potential in the learning domain then a paradigm shift in perspective is necessary, from information-based content delivery to knowledge-based collaborative learning services. We propose a semantics driven Knowledge Life Cycle that characterises the key phases in managing semantics and knowledge, show how this can be applied to the learning domain and demonstrate the value of semantics via an example of knowledge reuse in learning assessment management

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

Intrusion Detection System using Bayesian Network Modeling

Computer Network Security has become a critical and important issue due to ever increasing cyber-crimes. Cybercrimes are spanning from simple piracy crimes to information theft in international terrorism. Defence security agencies and other militarily related organizations are highly concerned about the confidentiality and access control of the stored data. Therefore, it is really important to investigate on Intrusion Detection System (IDS) to detect and prevent cybercrimes to protect these systems. This research proposes a novel distributed IDS to detect and prevent attacks such as denial service, probes, user to root and remote to user attacks. In this work, we propose an IDS based on Bayesian network classification modelling technique. Bayesian networks are popular for adaptive learning, modelling diversity network traffic data for meaningful classification details. The proposed model has an anomaly based IDS with an adaptive learning process. Therefore, Bayesian networks have been applied to build a robust and accurate IDS. The proposed IDS has been evaluated against the KDD DAPRA dataset which was designed for network IDS evaluation. The research methodology consists of four different Bayesian networks as classification models, where each of these classifier models are interconnected and communicated to predict on incoming network traffic data. Each designed Bayesian network model is capable of detecting a major category of attack such as denial of service (DoS). However, all four Bayesian networks work together to pass the information of the classification model to calibrate the IDS system. The proposed IDS shows the ability of detecting novel attacks by continuing learning with different datasets. The testing dataset constructed by sampling the original KDD dataset to contain balance number of attacks and normal connections. The experiments show that the proposed system is effective in detecting attacks in the test dataset and is highly accurate in detecting all major attacks recorded in DARPA dataset. The proposed IDS consists with a promising approach for anomaly based intrusion detection in distributed systems. Furthermore, the practical implementation of the proposed IDS system can be utilized to train and detect attacks in live network traffi