Searching for Temporal Patterns in AmI Sensor Data

  • Romain Tavenard
  • Albert A. Salah
  • Eric J. Pauwels
Part of the Communications in Computer and Information Science book series (CCIS, volume 11)


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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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Romain Tavenard
    • 1
  • Albert A. Salah
    • 2
  • Eric J. Pauwels
    • 2
  1. 1.IRISA/ENS de CachanRennes Cedex 
  2. 2.CWIAmsterdam

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