Due to the popularization of technology, non-expert people can now use more and more advanced devices and applications. Such systems produce data streams as well as persistent data with heterogeneous schemas and dynamics. This thesis is focused on monitoring data coming from those systems to help users to understand and to perform diagnosis on them. We propose an algebraic model Astral able to treat data coming from streams or relations without semantic ambiguity. The engine Astronef has been developed on top of a service-oriented component framework to enable a large adaptability. It embeds a query builder which can select a composition of components to provide an efficient query plan. Its extension Asteroid interfaces with a DBMS in order to manage persistent data in an integrated manner. Our contributions have been confronted to practice with the deployment of a monitoring system for the digital home and with a performance study. Finally, we extend our approach with an operator to personalize the results by introducing a top-k preference model.
