T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data

Albert Ali Salah (1) , Eric Pauwels (2) , Romain Tavenard (3) , Theo Gevers (1)

1 UvA - Universiteit van Amsterdam
2 CWI - Centrum voor Wiskunde en Informatica
3 UR - Université de Rennes

The trend to use large amounts of simple sensors as opposed to a few complex sensors to monitor places and systems creates a need for temporal pattern mining algorithms to work on such data. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. We contrast several recent approaches to the problem, and extend the T-Pattern algorithm, which was previously applied for detection of sequential patterns in behavioural sciences. The temporal complexity of the T-pattern approach is prohibitive in the scenarios we consider. We remedy this with a statistical model to obtain a fast and robust algorithm to find patterns in temporal data. We test our algorithm on a recent database collected with passive infrared sensors with millions of events.

Mots clés: MERL motion data; Gaussian mixture model; Lempel-Ziv; T-patterns; temporal pattern extraction; sensor networks

Domaines: Sciences de l'information et de la communication

Albert Ali Salah, Eric Pauwels, Romain Tavenard, Theo Gevers. T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data. Sensors, 2010, 10 (8), pp.7496-7513. ⟨10.3390/s100807496⟩. ⟨halshs-01138500⟩