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 tackle 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. Key words: Wireless Sensor Networks, Relational Sequence Mining, Temporal Patterns

• Type: Conference
• Year: 2009
• Where: AIIA
• Authors: Teresa Maria Altomare Basile, Nicola Di Mauro, Stefano Ferilli, Floriana Esposito
• Keywords: AIIA 2009, Artificial Intelligence, Complex Temporal Sequences, Relational Sequence Mining, Wireless Sensor Networks