Abstract
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.
This work is supported by EU FP7 STREP Project SM4ALL (Smart hoMes for ALL), under Grant No. 224332.
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Rasch, K., Li, F., Sehic, S., Ayani, R., Dustdar, S. (2012). Automatic Description of Context-Altering Services through Observational Learning. In: Kay, J., Lukowicz, P., Tokuda, H., Olivier, P., Krüger, A. (eds) Pervasive Computing. Pervasive 2012. Lecture Notes in Computer Science, vol 7319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31205-2_28
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DOI: https://doi.org/10.1007/978-3-642-31205-2_28
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