Relational Temporal Data Mining for Wireless Sensor Networks

Wireless sensor networks (WSNs) represent a typical domain where there are complex temporal sequences of events. In this paper we propose a relational framework to model and analyse the data observed by sensor nodes of a wireless sensor network. In particular, we extend a general purpose relational sequence mining algorithm to take into account temporal interval-based relations. Real-valued time series are discretized into similar subsequences and described by using a relational language. Preliminary experimental results prove the applicability of the relational learning framework to complex real world temporal data.

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