Model transformations are increasingly being incorporated in software development processes. However, as systems being developed with transformations grow in size and complexity, the performance of the transformations tends to degrade. In this paper we investigate the factors that have an impact on the execution performance of model transformations. We analyze the performance of three model transformation language engines, namely ATL, QVT Operational Mappings and QVT Relations. We implemented solutions to two transformation problems in these languages and compared the performance of these transformations. We extracted metric values from the transformations to systematically analyze how their characteristics influence transformation execution performance. We also implemented a solution to a transformation problem in ATL in three functionally equivalent ways, but with different language constructs to evaluate the effect of language constructs on transformation performance. The results of this paper enable a transformation designer to estimate beforehand the performance of a transformation, and to choose among implementation alternatives to achieve the best performance. In addition, transformation engine developers may find some of our results useful in order to tune their tools for better performance. This work has been carried out as part of the FALCON project under the responsibility of the Embedded Systems Institute with Vanderlande Industries as the industrial partner. This project is partially supported by the Netherlands Ministry of Economic Affairs under the Embedded Systems Institute (BSIK03021) program.

Context-aware systems that make use of sensor information to reason about their context have been proposed in many domains. However, it is still hard to design effective context-aware applications, due to the absence of suitable domain theories that consider dynamic context and associated user requirements as a precursor of system development.In this paper, we discuss a theory for the well-being domain and propose a model-driven development process that exploits the proposed theory to build effective, i.e. user-centric, context-aware applications.

Recent advances in wearable sensor technology and smartphones enable simple and affordable collection of personal analytics. This paper reflects on the lessons learned in the SWELL project that addressed the design of user-centered ICT applications for self-management of vitality in the domain of knowledge workers. These workers often have a sedentary lifestyle and are susceptible to mental health effects due to a high workload. We present the sense-reason-act framework that is the basis of the SWELL approach and we provide an overview of the individual studies carried out in SWELL. In this paper, we revisit our work on reasoning: interpreting raw heterogeneous sensor data, and acting: providing personalized feedback to support behavioural change. We conclude that simple affordable sensors can be used to classify user behaviour and heath status in a physically non-intrusive way. The interpreted data can be used to inform personalized feedback strategies. Further longitudinal studies can now be initiated to assess the effectiveness of m-Health interventions using the SWELL methods.

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Abstract-The lifestyle of the Dutch workforce is degrading. Unhealthy habits cause both physical and psychological problems, putting a strain on the individual's well-being. In order to conquer both of these, a system will be created that will coach its user to improve their lifestyle through a better diet and promoting physical activity in order to improve their feeling of well-being. However, requirements engineering is troublesome in this domain. We propose ways to conquer these requirements engineering problems using model-driven engineering techniques.