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

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



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