T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data
TLDR
This work extends the T-Pattern algorithm, which was previously applied for detection of sequential patterns in behavioural sciences, to find patterns in temporal event data, with a statistical model to obtain a fast and robust algorithm.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.read more
Citations
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Journal ArticleDOI
Human activity recognition based on multiple order temporal information
TL;DR: This paper model the problem of human activity recognition as a classification problem through the definition of a representation scheme that uses multiple order temporal information and shows that the features with low and high support have limited discriminative power.
Proceedings ArticleDOI
Improving activity recognition by segmental pattern mining
Umut Avci,Andrea Passerini +1 more
TL;DR: A novel approach for introducing long-range interactions based on sequential pattern mining and enriching sensor-based representations with the mined patterns allows improving results over sequential and segmental labeling algorithms in most of the cases.
Proceedings ArticleDOI
Temporal association rule mining for the preventive diagnosis of onboard subsystems within floating train data framework
TL;DR: A temporal association rule mining approach named T-patterns is presented, applied on highly challenging floating train data, to discover temporal associations between pairs of timestamped alarms that can predict the occurrence of severe failures within a complex bursty environment.
Book ChapterDOI
Modeling and discovering occupancy patterns in sensor networks using latent dirichlet allocation
TL;DR: A novel way to perform probabilistic modeling of occupancy patterns from a sensor network based on the Latent Dirichlet Allocation (LDA) model, which successfully found latent topics over all rooms and therefore obtain the dominant occupancy patterns or routines on the sensor network.
Book ChapterDOI
Mining floating train data sequences for temporal association rules within a predictive maintenance framework
TL;DR: A methodology for discovering association rules in very bursty and challenging floating train data sequences with multiple constraints is proposed, based on using null models to discover significant co-occurrences between pairs of events.
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