Steven Bosems

Performance in model transformations: experiments with ATL and QVT

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.

Improving context-aware applications for the well-being domain: Model-driven design guided by medical knowledge

Computing applications for among others well-being and health become increasingly advanced as a result of their sensor-based awareness of the context in which they are used. Context-aware applications have the potential of providing enriched services to their users, i.e. services that are appropriate for the context at hand. A challenge for the design of context-aware applications is to identify and develop service enrichments which are effective and useful while not being overly complex and costly. It is hard to imagine, both for the designer and end-user, all possible relevant contexts and best possible corresponding enriched services. An enriched service which is not appropriate for the context at hand can irritate or even harm the user, and (eventually) leads to avoiding the use of the service. This paper discusses a model-driven approach that incorporates domain knowledge concerning the causal relationship between context factors and human conditions. We believe that such an approach facilitates the identification and development of appropriate sensor-based context-aware services. We focus on context-aware applications for the well-being domain.

Models in the Design of Context-Aware Well-Being Applications

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.

Model-driven development for user-centric well-being support from dynamic well-being domain models to context-aware applications

Applications that can use information obtained through device sensors to alter their behavior are called context-aware. Design and development of such applications is currently done by modeling the applications context or by using novel requirements engineering methods. If the application is to support the users well-being, these methods fall short due to their technical focus. We propose a model-driven approach that deals with the specifics of the well-being domain by using a DSL that captures the users personal well-being context. The development method is user-centric, rather than technology focused. Initial user experiments show promising results.

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

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.

Enterprise Interoperability - Proceedings of the 5th International IFIP Working Conference on Enterprise Interoperability, IWEI 2013

This book constitutes the proceedings of the 5th International IFIP Working Conference on Enterprise Interoperability (IWEI 2013), held in Enschede, The Netherlands, during March 2728, 2013. The theme for IWEI 2013 was Information Services and Processes for the Interoperable Economy and Society.The 15 full papers presented in this volume were carefully selected from 35 submissions. The selection was based on a thorough review process, in which each paper was reviewed by at least three experts in the field. The papers are representative of the current research activities in the area of enterprise interoperability and are grouped into six sections: enterprise service interoperability, enterprise interoperability in sectors, interoperability methodology, interoperability for specific application types, strategic and tactical aspects of enterprise interoperability, and ontology-based interoperability. Two keynotes and an invited paper complete this volume.

Towards Model-Driven Requirements Analysis for Context-Aware Well-Being Systems

Over the years, the interest in the field of pervasive computing has increased. A specific class of applications in this domain is that of context-aware applications. These programs utilize context information to adapt to their current environment. This quality can be used, among others, when dealing with health care and well-being situations. However, as the user requirements for these specific applications are almost never well-specified, there is a real risk that the resulting application does not offer the right set of features to the user. In order to mitigate this risk, we propose a model-driven method of requirements engineering for systems in the domain of context-aware well-being applications. This method will result in an explicit specification of requirements, and an improved alignment of user requirements and system features. Furthermore, due to the model-driven character of the method, the artifacts created during the requirements engineering phase of the development process can directly be incorporated in the subsequent development steps.