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

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.

@Article{SalahSensors2010,
  author       = "Salah, A. A. and Pauwels, E. and Tavenard, R. and Gevers, T.",
  title        = "T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data",
  journal      = "Sensors",
  number       = "8",
  volume       = "10",
  pages        = "7496--7513",
  year         = "2010",
  url          = "http://www.science.uva.nl/research/publications/2010/SalahSensors2010"
}