Real-Time Analysis of Localization Data Streams for Ambient Intelligence Environments

In this paper we describe a novel methodology for performing realtime analysis of localization data streams produced by sensors embedded in ambient intelligence (AmI) environments. The methodology aims to handle different types of real-time events, detect interesting behavior in sequences of such events, and calculate statistical information using a scalable streamprocessing engine (SPE) that executes continuous queries expressed in a stream-oriented query language. Key contributions of our approach are the integration of the Borealis SPE into a large-scale interactive museum exhibit system that tracks visitor positions through a number of cameras; the extension and customization of Borealis to support the types of real-time analysis useful in the context of the museum exhibit as well as in other AmI applications; and the integration with a visualization component responsible for rendering events received by the SPE in a variety of human readable forms.