The tremendous popularity of web-based social media is attracting the attention of the industry to take profit from the massive availability of sentiment data, which is considered of a high value for Business Intelligence (BI). So far, BI has been mainly concerned with corporate data with little or null attention to the external world. However, for BI analysts, taking into account the Voice of the Customer (VoC) and the Voice of the Market (VoM) is crucial to put in context the results of their analyses. Recent advances in Sentiment Analysis have made possible to effectively extract and summarize sentiment data from these massive social media. As a consequence, VoC and VoM can be now listened from web-based social media (e.g., blogs, reviews forums, social networks, and so on). However, new challenges arise when attempting to integrate traditional corporate data and external sentiment data. This paper deals with these issues and proposes a novel semantic data infrastructure for BI aimed at providing new opportunities for integrating traditional and social BI. This infrastructure follows the principles of the Linked Open Data initiative.

Light pollution and nature preservation, are new trends in which the European cities are involved as they evolve into Smart Cities. Internet of Things are changing the way that sensors and management control systems are designed and implemented. In this article, our main objective is to present an Outdoor Light Desktop Central Management architecture using current IoT (Internet of Things) and GIS technologies to improve the energy efficiency, user experience and safety feeling at the same time we are going to decrease light pollution of LED lamps. The challenge is to provide a lighting control system to suit each zone, from residential areas and public spaces to industrial parks, and each context. Furthermore, the design of the technological multi-platform able to operate with any kind of electrical device will be useful in the area of outdoor lighting.

Visual textures have been shown to elicit specific emotional responses, influencing how individuals perceive and interact with their environment. Previous studies have explored the relationship between colors and emotions in abstract art; however, the role of textures in emotional perception remains less understood. This study investigates the relationship between emotions and textures in abstract art, and explores whether emotions evoked by abstract paintings can be predicted based on their textures. Using CLIP (Contrastive Language-Image Pretraining), we automatically extracted texture features from a dataset of abstract artworks, which were also annotated with the emotions they evoked by viewers. A Bayesian model was employed to assess the relationship between textures and emotions, while statistical analyses were used to evaluate correlations between texture categories and emotional responses. Our findings contribute to a deeper understanding of the emotional impact of textures in abstract art and provide insights into computational methods for emotion prediction based on visual features.