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Working with Information and Communications Technologies (ICT) is challenging, especially for learners, as ICT control most of their work environment. These learners need to be supported with solutions that are up-to-date with the technological challenges, and can keep up with the fast-paced evolving ICT. Acquiring new knowledge is important for the progress of learners, but it is getting harder to obtain with the overwhelming amount of resources produced by many Information Systems (ISs) in the learning environment. In order to help learners manage resources, we are following the approach of System of Information Systems (SoIS) to support learners. The SoIS gives the opportunity to explore how individual and organisational learning can be enhanced through a combination of different ISs in the learning environment. In this context, we look into the orchestration of SoIS that can provide the ability to index, share, annotate, and recommend important resources in the learning environment. In that sense, this paper presents the approach of SoIS to support learners with a case study at the University of Technology of Compiègne. [ABSTRACT FROM AUTHOR]

Improving energy efficiency in order to reduce CO2 emissions is a permanent challenge in the European space. Smart metering could help for improving energy efficiency by offering information about the way in which the energy is used. Smart metering will be based on large volumes of sensor data, since energy monitoring will bring together sensor data from various critical areas. The main purpose of this paper was to present the selection mechanism for a scalable storage solution, based on the requirements of the DEHEMS (Digital Environment Home Energy Management System) project. With regular sensor readings coming at every 6 seconds, there is an impressive amount of data collected even for the minimal target of about 250 households, 10 sensors per user. With these huge data streams that are non-stationary time-series data, collected at discrete intervals, the DEHEMS project has to offer a solution for storing and retrieving sensor data in a responsive way. We have tested both collection speed and aggregation speed for reasonable data streams of sensor data. The tests were performed on various database models, with their associated representations, including relational databases, key-value stores, column stores, self-tuning databases, as well as time-series enabled database systems. These experiments confirmed that column stores and keyvalue stores perform better than relational databases, while time-series databases outperform all the others. [ABSTRACT FROM AUTHOR]