Discovering trend clusters in sensor data streams

Nowadays sensors are deployed everywhere in order to support real-time data applications. They periodically gather information along a number of attribute dimensions (e.g., temperature and humidity). Applications typically require monitoring these data, fast computing aggregates, predicting unknown data, or issuing alarms. To this aim, this paper introduces a recently defined spatio-temporal pattern, called trend cluster, and its multiple applications to summarize, interpolate and detect outliers in sensor network data. As an example, we illustrate the application of trend cluster discovery to air climate data monitoring