OERopoly : A Game to Generate Collective Intelligence around OER

Collective Intelligence (CI ) is a phenomenon that emerges at the crossroads of three worlds: Open Educational Resources (OER), Web 2.0 technologies and Online Learning Communities. Building CI for the OER movement means capturing the richness of information, experiences, knowledge and resources, that the movement is constantly generating, in a way that they can be shared and reused for the benefit of the movement itself. The organisation of CI starts from collecting the knowledge and experiences of OER's practitioners and scholars in new creative forms, and then situating this knowledge in a collective 'pot' from where it can be leveraged with new 'intelligent' meanings and toward new 'intelligent' goals. This workshop is an attempt to do so by engaging participants in a CI experience, in which they will contribute to, and at the same time take something from, the existing CI around OER, Web 2.0 technologies and Online Learning Communities.

Análisis de datos de accidentes de tráfico mediante soluciones big data y business intelligence

Este proyecto de final de carrera corresponde al área de inteligencia artificial y representa un caso de uso que pretende utilizar datos reales referentes a accidentes de tráfico (datos de accidentes, muertos, heridos, etc.) y analizarlas conjuntamente con datos que puedan tener una posible relación con los accidentes como el parque de vehículos, las temperaturas de la zona de los accidentes, etc. con la finalidad de poder obtener las posibles relaciones causa-efecto.

Ambient intelligence application based on environmental measurements performed with an assistant mobile robot

This paper proposes the use of an autonomous assistant mobile robot in order to monitor the environmental conditions of a large indoor area and develop an ambient intelligence application. The mobile robot uses single high performance embedded sensors in order to collect and geo-reference environmental information such as ambient temperature, air velocity and orientation and gas concentration. The data collected with the assistant mobile robot is analyzed in order to detect unusual measurements or discrepancies and develop focused corrective ambient actions. This paper shows an example of the measurements performed in a research facility which have enabled the detection and location of an uncomfortable temperature profile inside an office of the research facility. The ambient intelligent application has been developed by performing some localized ambient measurements that have been analyzed in order to propose some ambient actuations to correct the uncomfortable temperature profile.