Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: ABSTRACTThe Eurovision contest has been the reference on european song contests for the past 50 years. Countries in the European Union can shows the rest of the participants their current music tendencies. This phenomena has been studied in domains like physic and social sciences to find correlations between contests and current political and socio-economy trends in EU. The inclusion of web and social technologies some years ago, have caused a disruption in the traditional voting system whereby the audience is encouraged to participate by casting votes for their favorite song. As a result, this system yields new, relevant information that may be extrapolated to social and political tendencies in Europe with a higher degree accuracy than by data collected using the previous jury-based system. This paper provides an initial data analysis in crowd behavior to assess the impact of the televote system, in the Eurovision voting dynamic, by focusing on two distinct five years periods that can successfully contrast each voting scheme. Analyzing these periods separately, we can observe results from the televoting contests and then compare to the jury to see if there is a change in voting patterns. Finally, we study the underlying community structure of the voting network using the Cluster Percolation Method and Edge Betweenness to discover stable core communities spanning a number of years in the contest. The clusters obtained using these algorithms are then used to compare how these stable communities have evolving during the considered periods.

Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: In virtual world, the user (or avatar) can move and interact within an artificial world with a high degree of freedom. The movements and iterations of the avatar can be monitorized, and hence this information can be analysed to obtain interesting behavioural patterns. Usually, only the information related to the avatars conversations (textual chat logs) are directly available for processing. However, these open platforms allow to capture other kind of information like the exact position of an avatar in the VW, what they are looking at (eye-gazing) or which actions they perform inside these worlds. This paper studies how this information, can be extracted, processed and later used by clustering methods to detect behaviour or group formations in the world. To detect the behavioural patterns of the avatars considered, clustering techniques have been used. These techniques, using the correct data preprocessing and modelling, can be used to automatically detect hidden patterns from data.

The logistic sector raises a number of highly challenging problems. Probably one of the most important ones is the shipping planning, i.e., plan the routes that the shippers have to follow to deliver the goods. In this paper we present an AI-based solution that has been designed to help a logistic company to improve its routes planning process. In order to achieve this goal, the solution uses the knowledge acquired by the company drivers to propose optimized routes. Hence, the proposed solution gathers the experience of the drivers, processes it and optimizes the delivery process. The solution uses Data Mining to extract knowledge from the company information systems and prepares it for analysis with a Case-Based Reasoning (CBR) algorithm. The CBR obtains critical business intelligence knowledge from the drivers experience that is needed by the planner. The design of the routes is done by a Genetic Algorithm (GA) that, given the processed information, optimizes the routes following several objectives, such as minimize the distance or time. Experimentation shows that the proposed approach is able to find routes that improve, in average, the routes made by the human experts.

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