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Understanding Triple Graph Grammars: Concepts, Extensions, and Application Scenarios

This overview discusses Triple Graph Grammars (TGGs), a declarative approach for defining correspondences between different model types, essential for model transformation, synchronization, and integration. The document outlines fundamental assumptions and goals, detailing solutions for challenges such as deterministic transformation and matching models that may not align perfectly. Key scenarios include toy train construction and the establishment of correspondence nodes to facilitate project integration. The synthesis algorithm plays a crucial role in these processes, allowing for reapplication of rules and identifying optimal matches with minimal modifications.

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Understanding Triple Graph Grammars: Concepts, Extensions, and Application Scenarios

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  1. Triple Graph Gammares: Concepts, Extensions, Implementations, and Application Scenarios EkkartKindler and Robert Wagner 2007

  2. TGG Application Scenarios: Triple Graph Grammars (TGGs) “Are a technique for defining the correspondence between two different types of models in a declarative way.” Model Transformation TGG Model Synchronisation Model Integration

  3. A construction plan for a toy train

  4. Triple Graph Grammars (TGGs) Figure 17: Meta-model for the correspondence objects

  5. Model Transformation Assumption and Goal: • One of the two models at either side already exists • Generate a corresponding model at the other side Solution: • Matching the source domain of the rules of the TGG to the given project model. • Adding the missing correspondence nodes and nodes (belonging to the other model) of that rule. Challenges: Transformation from PN to a project model: No corresponding project for the PN and vice versa. The transformation is not deterministic. The matching process might need backtracking

  6. Model Integration Assumption and Goal: • Two models are given, the correspondences between them needs to be established. Solution: • Introduce the correspondence node between the root elements of the models. • Matching (the both model side of) the TGG rules on the existing models. • Introduce the correspondence nodes of the matching TGG rule. Challenge: • May not be able to fully match both models

  7. Model Synchronisation Assumption and Goal: • For each side there is a model. • The correspondences between two models already exist • The models are not in complete or correct correspondence. • Finding the best possible matching pair • With as few as possible changes on both sides Solution: • Reapplying the rules and adding the missing parts of both models. Challenge: • May not be able to fully match both models.

  8. Basic Rule Synthesis Algorithm

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