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The Scientific World Journal
Volume 2015 (2015), Article ID 931931, 11 pages
http://dx.doi.org/10.1155/2015/931931
Research Article

Prediction of Domain Behavior through Dynamic Well-Being Domain Model Analysis

Faculty of EEMCS, University of Twente, P.O. Box 217, 7500 AE Enschede, Netherlands

Received 17 April 2015; Revised 12 July 2015; Accepted 14 July 2015

Academic Editor: Riccardo Martoglia

Copyright © 2015 Steven Bosems and Marten van Sinderen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As the concept of context-awareness is becoming more popular the demand for improved quality of context-aware systems increases too. Due to the inherent challenges posed by context-awareness, it is harder to predict what the behavior of the systems and their context will be once provided to the end-user than is the case for non-context-aware systems. A domain where such upfront knowledge is highly important is that of well-being. In this paper, we introduce a method to model the well-being domain and to predict the effects the system will have on its context when implemented. This analysis can be performed at design time. Using these predictions, the design can be fine-tuned to increase the chance that systems will have the desired effect. The method has been tested using three existing well-being applications. For these applications, domain models were created in the Dynamic Well-being Domain Model language. This language allows for causal reasoning over the application domain. The models created were used to perform the analysis and behavior prediction. The analysis results were compared to existing application end-user evaluation studies. Results showed that our analysis could accurately predict success and possible problems in the focus of the systems, although certain limitation regarding the predictions should be kept into consideration.