Abstract
In this chapter a relational framework able to model and analyse the data observed by nodes involved in a sensor network is presented. In particular, we propose a powerful and expressive description language able to represent the spatio-temporal relations appearing in sensor network data along with the environmental information. Furthermore, a general purpose system able to elicit hidden frequent temporal correlations between sensor nodes is presented. The framework has been extended in order to take into account interval-based temporal data by introducing some operators based on a temporal interval logic. A preliminary abstraction step with the aim of segmenting and labelling the real-valued time series into similar subsequences is performed exploiting a kernel density estimation approach. The prposed framework has been evaluated on real world data collected from a wireless sensor network.
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Esposito, F., Basile, T.M.A., Di Mauro, N., Ferilli, S. (2010). A Relational Approach to Sensor Network Data Mining. In: Soro, A., Vargiu, E., Armano, G., Paddeu, G. (eds) Information Retrieval and Mining in Distributed Environments. Studies in Computational Intelligence, vol 324. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16089-9_10
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