Understanding the effect of pervasive services on user context is critical to many context-aware applications. Detailed descriptions of context-altering services are necessary, and manually adapting them to the local environment is a tedious and error-prone process. We present a method for automatically providing service descriptions by observing and learning from the behavior of a service with respect to its environment. By applying machine learning techniques on the observed behavior, our algorithms produce high quality localized service descriptions. In a series of experiments we show that our approach, which can be easily plugged into existing architectures, facilitates context-awareness without the need for manually added service descriptions. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Rasch, K., Li, F., Sehic, S., Ayani, R., & Dustdar, S. (2012). Automatic description of context-altering services through observational learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7319 LNCS, pp. 461–477). https://doi.org/10.1007/978-3-642-31205-2_28
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