We describe the programming system for the graph-transformation language GP, focusing on the implementation of its compiler and abstract machine. We also compare the system's performance with other graph-transformation systems. The GP language is based on conditional rule schemata and comes with a simple formal semantics which maps input graphs to sets of output graphs. The implementation faithfully matches the semantics by using backtracking and allowing to compute all possible results for a given input
This research is concerned with the automatic generation of syntax-directed editors for graphical pr...
Even sophisticated techniques start out from simple ideas. Later, in reply to application needs or t...
An abstract machine for graph rewriting is the central part of the middle layer of the implementatio...
Graph transformation languages are declarative, rule-based languages that abstract from low-level re...
AbstractWe introduce the York Abstract Machine (YAM) for implementing the graph programming language...
The graph programming language GP (Graph Programs) 2 and its implementation is the subject of this t...
This papers defines the syntax and semantics of GP 2, a revised version of the graph programming lan...
We show how to generate efficient C code for a high-level domain-specific language for graphs. The e...
GP 2 is a non-deterministic programming language for computing by graph transformation. One of the d...
Abstract: GP (for Graph Programs) is a rule-based, nondeterministic program-ming language for solvin...
The framework of graph transformation combines the potentials and advantages of both, graphs and rul...
Large-scale graph processing, with its massive data sets, requires distributed processing. However, ...
The graph programming language GP 2 allows to apply sets of ruleschemata (or “attributed” rules) non...
Abstract. The graph programming language GP allows to apply sets of rule schemata (or “attributed ” ...
The use of graphs to model dynamic structures is ubiquitous in computer science; prominent example a...
This research is concerned with the automatic generation of syntax-directed editors for graphical pr...
Even sophisticated techniques start out from simple ideas. Later, in reply to application needs or t...
An abstract machine for graph rewriting is the central part of the middle layer of the implementatio...
Graph transformation languages are declarative, rule-based languages that abstract from low-level re...
AbstractWe introduce the York Abstract Machine (YAM) for implementing the graph programming language...
The graph programming language GP (Graph Programs) 2 and its implementation is the subject of this t...
This papers defines the syntax and semantics of GP 2, a revised version of the graph programming lan...
We show how to generate efficient C code for a high-level domain-specific language for graphs. The e...
GP 2 is a non-deterministic programming language for computing by graph transformation. One of the d...
Abstract: GP (for Graph Programs) is a rule-based, nondeterministic program-ming language for solvin...
The framework of graph transformation combines the potentials and advantages of both, graphs and rul...
Large-scale graph processing, with its massive data sets, requires distributed processing. However, ...
The graph programming language GP 2 allows to apply sets of ruleschemata (or “attributed” rules) non...
Abstract. The graph programming language GP allows to apply sets of rule schemata (or “attributed ” ...
The use of graphs to model dynamic structures is ubiquitous in computer science; prominent example a...
This research is concerned with the automatic generation of syntax-directed editors for graphical pr...
Even sophisticated techniques start out from simple ideas. Later, in reply to application needs or t...
An abstract machine for graph rewriting is the central part of the middle layer of the implementatio...