%0 Journal Article
%T T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data
%A Albert Ali Salah
%A E. Pauwels
%A R. Tavenard
%A Theo Gevers
%J Sensors
%D 2010
%V 10
%N 8
%F Albert Ali Salah2010
%O ALTRES;ISE
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=1845), last updated on Tue, 12 Jul 2016 10:57:06 +0200
%X 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.
%K sensor networks
%K temporal pattern extraction
%K T-patterns
%K Lempel-Ziv
%K Gaussian mixture model
%K MERL motion data
%U http://dx.doi.org/10.3390/s100807496
%P 7496-7513