Nowadays, lots of service providers offer predictive services that show in advance a condition or occurrence about the future. As a consequence, it becomes necessary for service customers to select the predictive service that best satisfies their needs. The QuPreSS reference model provides a standard solution for the selection of predictive services based on the quality of their predictions. QuPreSS has been designed to be applicable in any predictive domain (e.g., weather forecasting, economics, and medicine). This paper presents Mercury, a tool based on the QuPreSS reference model and customized to the weather forecast domain. Mercury measures weather predictive services' quality, and automates the context-dependent selection of the most accurate predictive service to satisfy a customer query. To do so, candidate predictive services are monitored so that their predictions can be eventually compared to real observations obtained from a trusted source. Mercury is a proof-of-concept of QuPreSS that aims to show that the selection of predictive services can be driven by the quality of their predictions. Throughout the paper, we show how Mercury was built from the QuPreSS reference model and how it can be installed and used.
The landscape of teaching and learning has changed in recent years because of the application of Information and Communications technology. Among the most representative innovations in this regard are Learning Management Systems. Despite of their popularity in institutional contexts and the wide set of tools and services that they provide to learners and teachers, they present several issues. Learning Management Systems are linked to an institution and a period of time, and are not adapted to learners' needs. In order to address these problems Personal Learning Environments are defined, but it is clear that these will not replace Learning Management Systems and other institutional contexts. Both types of environment should therefore coexist and interact. This paper presents a service-based framework to facilitate such interoperability. It supports the export of functionalities from the institutional to the personal environment and also the integration within the institution of learning outcomes from personal activities. In order to achieve this in a flexible, extensible and open way, web services and interoperability specifications are used. In addition some interoperability scenarios are posed. The framework has been tested in real learning contexts and the results show that interoperability is possible, and that it benefits learners, teachers and institutions.