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To predict the performance of an application, it is crucial to consider the performance of the underlying infrastructure. Thus, to yield accurate prediction results, performance-relevant properties and behaviour of the infrastructure have to be integrated into performance models. However, capturing these properties is a cumbersome and error-prone task, as it requires carefully engineered measurements and experiments. Existing approaches for creating infrastructure performance models require manual coding of these experiments, or ignore the detailed properties in the models. The contribution of this paper is the Goal-oriented INfrastructure Performance EXperiments ( Ginpex) approach, which introduces goal-oriented and model-based specification and generation of executable performance experiments for automatically detecting and quantifying performance-relevant infrastructure properties. Ginpex provides a metamodel for experiment specification and comes with predefined experiment templates that provide automated experiment execution on the target platform and also automate the evaluation of the experiment results. We evaluate Ginpex using three case studies, where experiments are executed to quantify various infrastructure properties. [ABSTRACT FROM AUTHOR]

How should the World Wide Web look like if it were for location-based information? And how would mobile, spatially aware applications deal with such a platform? In this paper we present theneXusAugmented World Model, an object oriented data model which plays a major role in an open framework for both providers of location-based information and new kinds of applications: theneXusplatform. We illustrate the usability of the model with several sample applications and show the extensibility of this framework. At last we present a stepwise approach for building spatially aware applications in this environment. [ABSTRACT FROM AUTHOR]