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Current topics in e-Learning Management Systems encourage innovative technologies to use digital libraries to exploit Learning Objects metadata in order to facilitate dissemination and re-process of their content. Digital repositories are mostly implemented as distributed computing systems architectures dealing with major technological and modeling issues that hinder interoperability between heterogeneous databases impeding data accessibility and reusability. Learning Object metadata are lack of interactivity and address operational deficiencies. Though, a content-structure and systematic approach needs to be established. A promising method to employ learning object metadata adaptation is to use semantic Web technologies. Expressing semantics in Learning material can heal interoperability issues and improve information retrieval by providing knowledge representation inherent in xml syntax and applying inference rules. This work develops an ontology-web model for learning object metadata. Detecting semantic relations on educational material aims to assess in an abstract level its tutoring content to improve the learning procedure.

The emergence of the Internet of Things paves the way for enhancing the monitoring capabilities of enterprises by means of extensive use of physical and virtual sensors generating a multitude of data. The generated real-time data streams provide the basis for anticipating future undesired events and thus enable enterprises to decide and act ahead of time, i.e. in a proactive manner. Proactive information systems can benefit from the use of context-awareness in order to provide more reliable recommendations. In this paper we propose a probabilistic model for context-aware proactive event-driven decision making that deals with the uncertainty that is inherent in the contextual elements' values. We validated our approach in an oil and gas industry scenario and we conducted a comparative analysis.

We implemented a recommendation system that presents persuasive messages to commuters using a route planning application. Our aim is to nudge users towards following routes that are environmentally friendly with lower CO2 emissions than those they usually take. Our implementation leverages contextual information and transportation user profiles while the messages are defined based on a set of persuasive strategies. The recommender is integrated in a route planning application for android based smartphones and was used in a trial in the cities of Vienna and Dublin. Users' qualitative feedback showed that the feature was found interesting and provided insights for future improvements.