TY  - JOUR
AU  - Albert Ali Salah
AU  - E. Pauwels
AU  - R. Tavenard
AU  - Theo Gevers
PY  - 2010//
TI  - T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data
T2  - SENS
JO  - Sensors
SP  - 7496
EP  - 7513
VL  - 10
IS  - 8
KW  - sensor networks
KW  - temporal pattern extraction
KW  - T-patterns
KW  - Lempel-Ziv
KW  - Gaussian mixture model
KW  - MERL motion data
N2  - 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.
UR  - http://dx.doi.org/10.3390/s100807496
N1  - ALTRES;ISE
ID  - Albert Ali Salah2010
ER  -