Abstract
Much research on context quality in context-aware systems divides into two strands: (1) the qualitative identification of quality measures and (2) the use of uncertain reasoning techniques. In this paper, we combine these two strands, exploring the problem of how to identify and propagate quality through the different context layers in order to support the context reasoning process. We present a generalised, structured context quality model that supports aggregation of quality from sensor up to situation level. Our model supports reasoning processes that explicitly aggregate context quality, by enabling the identification and quantification of appropriate quality parameters. We demonstrate the efficacy of our model using an experimental sensor data set, gaining a significant improvement in situation recognition for our voting based reasoning algorithm.
This work is partially supported by Enterprise Ireland under grant number CFTD 2005 INF 217a, and by Science Foundation Ireland under grant numbers 07/CE/I1147 and 04/RPI/1544.
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McKeever, S., Ye, J., Coyle, L., Dobson, S. (2009). A Context Quality Model to Support Transparent Reasoning with Uncertain Context. In: Rothermel, K., Fritsch, D., Blochinger, W., Dürr, F. (eds) Quality of Context. QuaCon 2009. Lecture Notes in Computer Science, vol 5786. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04559-2_6
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DOI: https://doi.org/10.1007/978-3-642-04559-2_6
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