Abstract
Anticipation is a key property of human-human communication, and it is highly desirable for ambient environments to have the means of anticipating events to create a feeling of responsiveness and intelligence in the user. In a home or work environment, a great number of low-cost sensors can be deployed to detect simple events: the passing of a person, the usage of an object, the opening of a door. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. Using a testbed that we have developed for this purpose, we first contrast current approaches to the problem. We then extend the best of these approaches, the T-Pattern algorithm, with Gaussian Mixture Models, to obtain a fast and robust algorithm to find patterns in temporal data. Our algorithm can be used to anticipate future events, as well as to detect unexpected events as they occur.
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© 2008 Springer-Verlag Berlin Heidelberg
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Tavenard, R., Salah, A.A., Pauwels, E.J. (2008). Searching for Temporal Patterns in AmI Sensor Data. In: Mühlhäuser, M., Ferscha, A., Aitenbichler, E. (eds) Constructing Ambient Intelligence. AmI 2007. Communications in Computer and Information Science, vol 11. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85379-4_7
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DOI: https://doi.org/10.1007/978-3-540-85379-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85378-7
Online ISBN: 978-3-540-85379-4
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