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Chapter and Conference Paper
Secure and Task Abortion Aware GA-Based Hybrid Metaheuristics for Grid Scheduling
In traditional distributed computing the users and owners of the computational resources usually belong to the same administrative domain. Therefore security and reliability of the resources are not concerned ...
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Chapter and Conference Paper
A GA(TS) Hybrid Algorithm for Scheduling in Computational Grids
The hybridization of heuristics methods aims at exploring the synergies among stand alone heuristics in order to achieve better results for the optimization problem under study. In this paper we present a hybr...
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Chapter and Conference Paper
A Compendium of Heuristic Methods for Scheduling in Computational Grids
Scheduling in large scale distributed computing environments such as Computational Grids, is currently receiving a considerable attention of researchers. Despite that scheduling in such systems has much in com...
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Chapter and Conference Paper
Supporting Effective Monitoring and Knowledge Building in Online Collaborative Learning Systems
This paper aims to report on an experience of using an innovative groupware tool to support real, collaborative learning. We base the success of on-line collaborative learning on extracting relevant knowledge ...
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Chapter and Conference Paper
Enabling Efficient Real Time User Modeling in On-Line Campus
User modelling in on-line distance learning is an important research field focusing on two important aspects: describing and predicting students’ actions and intentions as well as adapting the learning process...
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Chapter and Conference Paper
Efficient Embedding of Information and Knowledge into CSCL Applications
This study aims to explore two crucial aspects of collaborative work and learning: the importance of enabling CSCL applications, on the one hand, to capture and structure the information generated by group act...
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Chapter and Conference Paper
Using Parallelism in Experimenting and Fine Tuning of Parameters for Metaheuristics
We show that parallel implementations of metaheuristics are efficient tools for both experimenting and fine tuning of parameters.