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Investigation of Context Prediction Accuracy for Different Context Abstraction Levels
June 2012 (vol. 11 no. 6)
pp. 1047-1059
Stephan Sigg, Technische Universitaet Braunschweig, Braunschweig
Dawud Gordon, Technische Universitaet Braunschweig, Braunschweig
Georg von Zengen, Technische Universitaet Braunschweig, Braunschweig
Michael Beigl, Technische Universitaet Braunschweig , Braunschweig
Sandra Haseloff, Kassel University, Kassel
Klaus David, Kassel University, Kassel
Context prediction is the task of inferring information about the progression of an observed context time series based on its previous behaviour. Prediction methods can be applied at several abstraction levels in the context processing chain. In a theoretical analysis as well as by means of experiments we show that the nature of the input data, the quality of the output, and finally the flow of processing operations used to make a prediction, are correlated. A comprehensive discussion of basic concepts in context prediction domains and a study on the effects of the context abstraction level on the context prediction accuracy in context prediction scenarios is provided. We develop a set of formulae that link scenario-dependent parameters to a probability for the context prediction accuracy. It is demonstrated that the results achieved in our theoretical analysis can also be confirmed in simulations as well as in experimental studies.

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Index Terms:
Pervasive computing, stochastic processes, location-dependent and sensitive, performance evaluation of algorithms and systems, time series analysis.
Citation:
Stephan Sigg, Dawud Gordon, Georg von Zengen, Michael Beigl, Sandra Haseloff, Klaus David, "Investigation of Context Prediction Accuracy for Different Context Abstraction Levels," IEEE Transactions on Mobile Computing, vol. 11, no. 6, pp. 1047-1059, June 2012, doi:10.1109/TMC.2011.170
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