title: 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.
authors.affiliation: Informatics Institute, University of Amsterdam, Science Park 107, 1098 XG, Amsterdam, The Netherlands. a.a.salah@uva.nl
authors.forename: Albert Ali
authors.initials: AA
authors.lastname: Salah
authors.forename: Eric
authors.initials: E
authors.lastname: Pauwels
authors.forename: Romain
authors.initials: R
authors.lastname: Tavenard
authors.forename: Theo
authors.initials: T
authors.lastname: Gevers
mesh_terms: D000465:Algorithms; D057225:Data Mining; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software