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

Abstract EN

As the concept of context-awareness is becoming more popularthe demand for improved quality of context-aware systems increases too.

Due tothe inherent challenges posed by context-awareness, it is harder to predict whatthe behavior of the systems and their context will be once provided to theend-user than is the case for non-context-aware systems.

A domain where suchupfront knowledge is highly important is that of well-being.

In this paper, weintroduce a method to model the well-being domain and to predict the effects thesystem will have on its context when implemented.

This analysis can be performedat design time.

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

The method has beentested using three existing well-being applications.

For these applications,domain models were created in the Dynamic Well-being Domain Model language.

Thislanguage allows for causal reasoning over the application domain.

The modelscreated were used to perform the analysis and behavior prediction.

The analysisresults were compared to existing application end-user evaluation studies.

Results showed that our analysis could accurately predict success and possibleproblems in the focus of the systems, although certain limitation regarding thepredictions should be kept into consideration.