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Sensors 2010, 10(8), 7496-7513; doi:10.3390/s100807496

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

Informatics Institute, University of Amsterdam, Science Park 107, 1098 XG, Amsterdam, The Netherlands
CWI, Science Park 123, 1098 XG, Amsterdam, The Netherlands
University of Rennes 1 - IRISA, 35042 Rennes Cedex, France
Author to whom correspondence should be addressed.
Received: 2 December 2009 / Revised: 23 February 2010 / Accepted: 23 July 2010 / Published: 10 August 2010
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in The Netherlands)
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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. View Full-Text
Keywords: sensor networks; temporal pattern extraction; T-patterns; Lempel-Ziv; Gaussian mixture model; MERL motion data sensor networks; temporal pattern extraction; T-patterns; Lempel-Ziv; Gaussian mixture model; MERL motion data

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Salah, A.A.; Pauwels, E.; Tavenard, R.; Gevers, T. T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data. Sensors 2010, 10, 7496-7513.

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