The modelling approach focuses on the definition of models and model transformations, from the application requirements to code generation, emphasizing dynamic reconfiguration of the application.
Hence a system that can use a small number of files to model transformations from one mode to another is important.
The advantage of this approach is that it allows defining several model transformations from the same abstraction level but targeted to different lower levels, offering opportunities to target different technology platforms.
The Instrumental Variable method employs as instrument an auxiliary model of the structure, and it is able to directly identify the continuous-time structure model using discrete-time data without resorting on model transformations from continuous-time to discrete-time, and vice-versa.
In this paper, we describe a model-to-model transformation from queueing Petri nets (QPN) into layered queueing networks (LQN).
These key benefits were achieved by removing a model-to-model transformation from the model extraction process and by using a single multi-dialect grammar instead of using a grammar for each dialect.
This may involve several model transformations, for example, from UML to SystemC.
The proposed direct interval robust optimization algorithm has the same advantage as our previous one [27] in avoiding the complex model transformation process from interval to deterministic.
