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.
Highlights
Endowing environments with a capability to respond intelligently to different situations depends on observing the activity in the environment and deriving patterns of behaviour
In this paper we have reviewed existing methodologies for the discovery of temporal patterns in sensor data
We have explicitly contrasted compression-based methods, which collapse the sequence into a string and extract repetitive “words”, with the T-pattern approach, which takes advantage of the time dimension to find the typical delay between related events
Summary
Endowing environments with a capability to respond intelligently to different situations depends on observing the activity in the environment and deriving patterns of behaviour. Mining the data for temporal patterns aims to discover associations and structure, either in an offline manner to pave the way for new designs and applications, or in an online manner to ensure adaptation of the environment to the users. Two things make this task especially challenging. The original T-pattern algorithm has quadratic time complexity in the number of sensors, as well as in the number of discrete time steps considered for pattern search (i.e., event horizon) We show how this complexity can be reduced, and propose a modified algorithm that is quadratic only in the number of sensors.
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