An algebraic framework for the transformation of attributed graphs
Term graph rewriting
Handbook of graph grammars and computing by graph transformation: volume I. foundations
Handbook of graph grammars and computing by graph transformation: volume I. foundations
Fundamentals of Algebraic Specification I
Fundamentals of Algebraic Specification I
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CONCUR '01 Proceedings of the 12th International Conference on Concurrency Theory
Introduction to the Algebraic Theory of Graph Grammars (A Survey)
Proceedings of the International Workshop on Graph-Grammars and Their Application to Computer Science and Biology
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ICGT '02 Proceedings of the First International Conference on Graph Transformation
Fundamentals of Algebraic Graph Transformation (Monographs in Theoretical Computer Science. An EATCS Series)
A unified categorical approach for attributed graph rewriting
CSR'08 Proceedings of the 3rd international conference on Computer science: theory and applications
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FASE'12 Proceedings of the 15th international conference on Fundamental Approaches to Software Engineering
M,N-adhesive transformation systems
ICGT'12 Proceedings of the 6th international conference on Graph Transformations
M,N-adhesive transformation systems
ICGT'12 Proceedings of the 6th international conference on Graph Transformations
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Attributes are an important concept for modeling data in practical applications. Up to now there is no adequate way to define attributes for different kinds of models used in $\mathcal{M}$-adhesive transformation systems, which are a special kind of graph transformation system based on $\mathcal{M}$-adhesive categories. Especially a proper representation and definition of attributes and their values as well as a suitable handling of the data does not fit well with other graph transformation formalisms. In this paper, we propose a new method to define attributes in a natural, but still formally precise and widely applicable way. We define a new kind of adhesive category, called $\mathcal W$-adhesive, that can be used for transformations of attributes, while the underlying models are still $\mathcal{M}$-adhesive ones. As a result, attributed models can be used as they are intended to be, but with a formal background and proven well-behavior.