Abstract
Knowledge is central to the modern economy and society. Indeed, the knowledge society has transformed the concept of knowledge and is more and more aware of the need to overcome the lack of knowledge when has to make options or address its problems and dilemmas. One's knowledge is less based on exact facts and more on hypotheses, perceptions or indications. Even when we use new computational artefacts and novel methodologies for problem solving, like the use of Group Decision Support Systems (GDSSs), the question of incomplete information is in most of the situations marginalized. On the other hand, common sense tells us that when a decision is made it is impossible to have a perception of all the information involved and the nature of its intrinsic quality. Therefore, something has to be made in terms of the information available and the process of its evaluation. It is under this framework that a Multi-valued Extended Logic Programming language will be used for knowledge representation and reasoning, leading to a model that embodies the Quality-of-Information (QoI) and its quantification, along the several stages of the decision-making process. In this way, it is possible to provide a measure of the value of the QoI that supports the decision itself. This model will be here presented in the context of a GDSS for VirtualECare, a system aimed at sustaining online healthcare services.

Abstract
The main purpose of this article is twofold to analyze: (a) the long-term relation among the commodities prices and between spot electricity market price and commodity prices, and (b) the short-term dynamics among commodity prices and between electricity prices and commodity prices. Data between 2002 and 2005 from the Spanish electricity market was used. Econometric methods were used in the analysis of the commodity spot price, namely the vector autoregression model, the vector error correction model and the granger causality test. The co-integration approach was used to analyze the long-term relationship between the common stochastic trends of four fossil fuel prices. One of the findings in the long-term relation is that the prices of fuel and the prices of Brent are intertwined, though the prices of Brent ten to "move" to reestablish the price equilibrium. Another finding is that the price of electricity is explained by the evolution of the natural gas series.

Abstract

Abstract
Many rely now on public cloud infrastructure-as-a-service for database servers, mainly, by pushing the limits of existing pooling and replication software to operate large shared-nothing virtual server clusters. Yet, it is unclear whether this is still the best architectural choice, namely, when cloud infrastructure provides seamless virtual shared storage and bills clients on actual disk usage. This paper addresses this challenge with Resilient Asynchronous Commit (RAsC), an improvement to a well-known shared-nothing design based on the assumption that a much larger number of servers is required for scale than for resilience. Then we compare this proposal to other database server architectures using an analytical model focused on peak throughput and conclude that it provides the best performance/cost trade-off while at the same time addressing a wide range of fault scenarios.

Abstract
One of the most hard and expensive tasks commonly attributed to e-learning is to create fresh new content. Generally, educators try to reuse pre-existent material which suffers some modification before being given to students. A repository of such educational material is therefore of great use. In this article we propose a digital repository in which content ingestion includes an unsupervised classification mechanism which organizes documents into clusters of semantic proximity. This mechanism can be afterwards enhanced with a social classification process based on tagging. We present a methodology for the evaluation of our proposal with and without using tags. © 2011 IADIS.

Abstract
The uncertainties related to when and where electric vehicles will charge in the future requires the development of stochastic based approaches to identify the corresponding load scenarios. This paper describes a tool based on Monte Carlo techniques to be used for distribution grid planning, providing a characterization of possible grid operation conditions, regarding voltage profiles, branch loading, grid peak power and energy losses. A medium voltage network based on real data is used to illustrate the application of the developed methodology.