Limits...
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.


Two causal loops.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4553332&req=5

fig4: Two causal loops.

Mentions: The other construct created using causal relations is the loop. A loop is a path that starts and ends in the same variable. Loops that have an even (or zero) number of negative causal relations are called reinforcing; a loop with an odd number of negative causal relations is called balancing. The former type models exponential change; the latter models a stable situation. A reinforcing loop may also be balanced by influences from causal relations from outside the loop itself. Figure 4 shows an example of a causal graph with two loops. The loop A-B-A is balancing; if A increases, this causes an increase in B, resulting in a decrease in A. On the other hand, the loop A-B-C-A is reinforcing; an increase in A causes B to increase, increasing C, which increases A again, resulting in additional, continuous increase.


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

Bosems S, van Sinderen M - ScientificWorldJournal (2015)

Two causal loops.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: Two causal loops.
Mentions: The other construct created using causal relations is the loop. A loop is a path that starts and ends in the same variable. Loops that have an even (or zero) number of negative causal relations are called reinforcing; a loop with an odd number of negative causal relations is called balancing. The former type models exponential change; the latter models a stable situation. A reinforcing loop may also be balanced by influences from causal relations from outside the loop itself. Figure 4 shows an example of a causal graph with two loops. The loop A-B-A is balancing; if A increases, this causes an increase in B, resulting in a decrease in A. On the other hand, the loop A-B-C-A is reinforcing; an increase in A causes B to increase, increasing C, which increases A again, resulting in additional, continuous increase.

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.