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

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

1
Informatics Institute, University of Amsterdam, Science Park 107, 1098 XG, Amsterdam, The Netherlands
2
CWI, Science Park 123, 1098 XG, Amsterdam, The Netherlands
3
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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Abstract

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.
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
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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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