Titlebook: ;

Flustered

https://doi.org/10.1007/978-1-4612-4748-7that may have to be ensured in an application scenario with regard to the first dimension of challenges for model transformations—the functional dimension—presented in Sect. 3.1. The first two sections of this chapter (Sects. 8.1 and 8.2) present powerful analysis techniques that are based on the in

低能兒

Dragan Ga?evi?,Dragan Djuri?,Vladan Deved?i?, CFH.09]). Triple graph grammars (TGGs) have been successfully applied in several case studies for bidirectional model transformation, model integration and synchronisation [KW07, SK08, GW09, GH09], and in the implementation of QVT [GK10]. This chapter provides a TGG framework for model synchronisa

Benign

Model Identification and Adaptive Control are typically instrumented with tools to autonomously perform adaptation to these changes while maintaining some desired properties. In this chapter, we model and analyse selfadaptive systems by means of typed, attributed graph grammars. The interplay of different grammars representing the applicat

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Anemia

Model Neural Networks and Behaviorformation system become crucial for the promotion of graph transformation in industry. In this chapter, we present four related modelling environments that have been developed at Technische Universit?t Berlin and that support the specification, simulation and analysis of behavioural models and model

LATHE

Canopy

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habile

Perigee