Energy optimization is crucial for environmental sustainability, as it reduces resource consumption, minimizes greenhouse gas emissions, and promotes the use of renewable energy. Efficient energy use helps combat climate change and preserves natural ecosystems for future generations. In this paper, a system to support the distribution of photovoltaic energy for Emilia Romagna Energy Communities is proposed. The system will manage and integrate large amounts of data and offer innovative services based on them for calculating climate and energy forecasts. To enable more reliable production estimates and efficient energy storage and distribution, the system will use a platform for managing and integrating data from Regional Climate Models. It will incorporate Machine Learning and Deep Learning models for accurate climate forecasts and optimize energy flows by considering consumption profiles, production forecasts, and storage characteristics. The application background, the proposed methodology, and the current challenges related to the domain will be discussed, with a particular focus on data sources and management operations.

The use of big data has grown rapidly in recent years, finding its way into various fields of use, from medicine to industry, from traffic flow optimisation to environmental protection. In the field of waste management, the European REthinkWASTE 1 project provided the opportunity for research and testing of new methods for intelligent waste management, leading to a better understanding of the problems in this area and solutions to solve them. This paper describes the results obtained in the REThinkWASTE project by exploiting MOMIS (Mediator EnvirOnment for Multiple Information Sources) [4], the open-source data integration system developed by UniMoRe and DataRiver. First, the architecture of the REThinkWASTE data integration and analysis platform and the choices made during the problem analysis phase are described. Next, the technologies used for Key Performance Indicator extraction are described and compared with the alternatives evaluated. The aim of the paper is to provide a valid example of the effectiveness of Big Data management and analysis technologies in a real-world scenario.

We propose the demonstration of Keymantic , a system for keyword-based searching in relational databases that does not require a-priori knowledge of instances held in a database. It finds numerous applications in situations where traditional keyword-based searching techniques are inapplicable due to the unavailability of the database contents for the construction of the required indexes.

In this paper, we follow the role-based access control (RBAC) approach and extend it to provide for the dynamic association of roles with users. In our framework, privileges associated with resources are assigned depending on the attribute values of the resources, attribute values associated with users determine the association of users with privileges, and a location mapping function between physical and logical locations allows to enable/disable roles depending on the logical location of the users and thus preserve the privacy of the location. We use Semantic Web technologies and a graphical user interface based on the Google Maps API.