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

Bosems S, van Sinderen M - ScientificWorldJournal (2015)

Bottom Line: Using these predictions, the design can be fine-tuned to increase the chance that systems will have the desired effect.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.

View Article: PubMed Central - PubMed

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

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.

No MeSH data available.


DWDM with four variables and contradicting causal relations.
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Related In: Results  -  Collection


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fig5: DWDM with four variables and contradicting causal relations.

Mentions: For example, observe the following DWDM in Figure 5. If A were a means for achieving goal D, the tree of affected variables would be as in Figure 6.


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

Bosems S, van Sinderen M - ScientificWorldJournal (2015)

DWDM with four variables and contradicting causal relations.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4553332&req=5

fig5: DWDM with four variables and contradicting causal relations.
Mentions: For example, observe the following DWDM in Figure 5. If A were a means for achieving goal D, the tree of affected variables would be as in Figure 6.

Bottom Line: Using these predictions, the design can be fine-tuned to increase the chance that systems will have the desired effect.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.

View Article: PubMed Central - PubMed

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

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.

No MeSH data available.