Abstract. To realise an Ambient Intelligence environment, it is paramount that applications can dispose of information about the context in which they operate, preferably in a very general manner. For this purpose various types of information should be assembled to form a representation of the context of the device on which aforementioned applications run. To allow interoperability in an Ambient Intelligence environment, it is necessary that the context terminology is commonly understood by all participating devices. In this paper we propose an adaptable and extensible context ontology for creating context-aware computing infrastructures, ranging from small embedded devices to high-end service platforms. The ontology has been designed to solve several key challenges in Ambient Intelligence, such as application adaptation, automatic code generation and code mobility, and generation of device specific user interfaces.

Abstract. As model transformations have become an integral part of the automated software engineering lifecycle, reuse, modularisation, and composition of model transformations becomes important. One way to compose model transformations is to compose modules of transformation rules, and execute the composition as one transformation (internal composition). This kind of composition can provide fine-grained semantics, as it is part of the transformation language. This paper aims to generalise two internal composition mechanisms for rule-based transformation languages, module import and rule inheritance, by providing executable semantics for the composition mechanisms within a virtual machine. The generality of the virtual machine is demonstrated for different rule-based transformation languages by compiling those languages to, and executing them on this virtual machine. We will discuss how ATL and graph transformations can be mapped to modules and rules inside the virtual machine.

As the application of model transformation becomes increasingly commonplace, the focus is shifting from model transformation languages to the model transformations themselves. The properties of model transformations, such as scalability, maintainability and reusability, have become important. Composition of model transformations allows for the creation of smaller, maintainable and reusable transformation definitions that together perform a larger transformation. This paper focuses on composition for two rule-based model transformation languages: the ATLAS Transformation Language (ATL) and the QVT Relations language. We propose a composition technique called module superimposition that allows for extending and overriding rules in transformation modules. We provide executable semantics as well as a concise and scalable implementation of module superimposition based on ATL.

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A single Model Transformation Chain (MTC) takes a high-level input model rooted in the problem domain and through one or more transformation steps produces a low-level output model rooted in the solution domain. To build a single "almighty" MTC that is in charge of every design, implementation and specific platform concern is a complex task. Instead, we can use several smaller MTCs that are easier to develop and maintain, because each MTC is independently developed focusing on a specific concern. However, the MTCs must interoperate to produce complete applications; this inherently creates dependencies between them, because each MTC generates a part of the final lowlevel model. In this paper, we propose an external and explicit mechanism to track dependencies between the MTCs (i.e., the MTCs are oblivious to the mechanism), which is used to automatically derive correspondence relationships between the final models generated by each MTC. The contribution of our mechanism is the reduction of complexity of building interoperable MTCs because the derived correspondences are resolved after the transformations execution, in the solution domain where the semantics of every concept is welldefined. The resolution process consists of (1) checking the consistency between the models, (2) producing communication bridges or (3) guiding the composition of the models. This paper presents three case studies to illustrate the derivation and resolution of correspondence relationships through the MTCs.