Fiche du document
2010
- handle: 10670/1.qowtot
- halshs: halshs-01138500
- doi: 10.3390/s100807496
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.3390/s100807496
info:eu-repo/semantics/OpenAccess
Mots-clés
MERL motion data Gaussian mixture model Lempel-Ziv T-patterns temporal pattern extraction sensor networksCiter ce document
Albert Ali Salah et al., « T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.3390/s100807496
Métriques
Partage / Export
Résumé
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.