نتایج جستجو برای: graph transformation
تعداد نتایج: 411772 فیلتر نتایج به سال:
Graph transformation is one of the key concepts in graph grammar. In order to accelerate the graph transformation, the concept of parallel graph transformation has been proposed by different tools such as AGG tool. The theory of parallel graph transformation used by AGG just allows clarifying the concepts of conflict and dependency between the transformation rules. This work proposes an approac...
Interconnected data or, in particular, graph structures are a valuable source of information. Gaining insights and knowledge from is applied throughout wide range application areas, for which efficient tools desired. In this work we present an open Java transformation framework. The framework provides simple fluent Application Programming Interface (API) to transform provided structure desired ...
The concept of graph transformation units in its original sense is a structuring principle for graph transformation systems which allows the interleaving of rule applications with calls of imported units in a controlled way. The semantics of a graph transformation unit is a binary relation on an underlying type of graphs. In order to get a flexible typing mechanism for transformation units and ...
Many important systems like concurrent heap-manipulating programs, communication networks, or distributed algorithms are hard to verify due to their inherent dynamics and unboundedness. Graphs are an intuitive representation of states of these systems, where transitions can be conveniently described by graph transformation rules. We present a framework for the abstraction of graphs supporting a...
The traditional double-pushout approach to graph transformation does not allow to change node labels in an arbitrary context. We propose a simple solution to this problem, namely to use rules with partially labelled interface graphs and to match rules injectively. In [8] we have shown that injective matching makes the double-pushout approach more expressive, and here we further generalise that ...
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