2007
DOI: 10.1016/j.tcs.2007.02.001
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Attributed graph transformation with node type inheritance

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Cited by 96 publications
(63 citation statements)
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“…This substitution is not necessary in the elements of the LHS as they are not created, and it is not done in the NACs either in order to obtain the expected behaviour of disjunction. Using an optimization similar to [3], one could also work directly with abstract rules, but we would have to modify the notion of morphism and it is left for future work.…”
Section: Generation Of Operational Mechanismsmentioning
confidence: 99%
“…This substitution is not necessary in the elements of the LHS as they are not created, and it is not done in the NACs either in order to obtain the expected behaviour of disjunction. Using an optimization similar to [3], one could also work directly with abstract rules, but we would have to modify the notion of morphism and it is left for future work.…”
Section: Generation Of Operational Mechanismsmentioning
confidence: 99%
“…Note that this rule uses abstract nodes: piece "p" and machine "m" have abstract types, and are visually represented with asterisks. Abstract nodes in the rule can get instantiated to nodes of any concrete subtype [13]. In this way, rules become much more compact.…”
Section: Graph Transformation By Examplementioning
confidence: 99%
“…Our present work redefines some of these analysis properties, but taking into consideration a full-fledged meta-model, as well as OCL constraints in rules. Some preliminary efforts to integrate graph transformation with meta-modelling can be found in [13], where type graphs were extended with inheritance, and [20], were edge inheritance and edge cardinalities were incorporated into type graphs.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Rule "move" uses abstract nodes: piece "p" and machine "m" are abstract, and are visually represented with asterisks. Abstract nodes in a rule can get instantiated to nodes of any concrete subtype [35]. In this way, rules become much more compact.…”
Section: Introduction To Graph Transformationmentioning
confidence: 99%