An algebraic framework for the transformation of attributed graphs
Term graph rewriting
Fundamentals of Algebraic Specification I
Fundamentals of Algebraic Specification I
Fundamentals of Algebraic Graph Transformation (Monographs in Theoretical Computer Science. An EATCS Series)
ICGT '08 Proceedings of the 4th international conference on Graph Transformations
Delaying constraint solving in symbolic graph transformation
ICGT'10 Proceedings of the 5th international conference on Graph transformations
Symbolic graphs for attributed graph constraints
Journal of Symbolic Computation
Adhesive High-Level Replacement Systems: A New Categorical Framework for Graph Transformation
Fundamenta Informaticae - SPECIAL ISSUE ON ICGT 2004
Fundamenta Informaticae - SPECIAL ISSUE ON ICGT 2004
Borrowed contexts for attributed graphs
ICGT'12 Proceedings of the 6th international conference on Graph Transformations
Checking bisimilarity for attributed graph transformation
FOSSACS'13 Proceedings of the 16th international conference on Foundations of Software Science and Computation Structures
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Applying an attributed graph transformation rule to a given object graph always implies some kind of constraint solving. In many cases, the given constraints are almost trivial to solve. For instance, this is the case when a rule describes a transformation G ⇒ H, where the attributes of H are obtained by some simple computation from the attributes of G. However there are many other cases where the constraints to solve may be not so trivial and, moreover, may have several answers. This is the case, for instance, when the transformation process includes some kind of searching. In the current approaches to attributed graph transformation these constraints must be completely solved when defining the matching of the given transformation rule. This kind of early binding is well-known from other areas of Computer Science to be inadequate. For instance, the solution chosen for the constraints associated to a given transformation step may be not fully adequate, meaning that later, in the search for a better solution, we may need to backtrack this transformation step. In this paper, based on our previous work on the use of symbolic graphs to deal with different aspects related with attributed graphs, including attributed graph transformation, we present a new approach that, based on the new notion of narrowing graph transformation rule, allows us to delay constraint solving when doing attributed graph transformation, in a way that resembles lazy computation. For this reason, we have called lazy this new kind of transformation. Moreover, we show that the approach is sound and complete with respect to standard attributed graph transformation. A running example, where a graph transformation system describes some basic operations of a travel agency, shows the practical interest of the approach.