Describing Horizontal Model Transformations with Graph Rewriting Rules

In this paper we demonstrate and compare two complementary approaches to the automatic generation of production rules from a set of given graphs representing sample designs. The first approach generates a complete rule set from scratch by means of frequent subgraph discovery. Whereas the second approach is intended to learn additional rules that fit an existing, yet incomplete, rule set using genetic programming. Both approaches have been developed and tested in the context of an application for automated conceptual engineering design, more specifically functional decomposition. They can be considered feasible, complementary approaches to the automatic inference of graph rewriting rules for conceptual design applications.

Download Full-text

Graph rewriting rules for RDF database evolution: optimizing side-effect processing

International Journal of Web Information Systems ◽

10.1108/ijwis-03-2021-0033 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Jacques Chabin ◽

Cédric Eichler ◽

Mirian Halfeld Ferrari ◽

Nicolas Hiot

Keyword(s):

Side Effects ◽

Side Effect ◽

Choice Functions ◽

Graph Rewriting ◽

Content Type ◽

Closed World ◽

Rdf Database ◽

Description Framework ◽

Resource Description ◽

Rewriting Rules

Purpose Graph rewriting concerns the technique of transforming a graph; it is thus natural to conceive its application in the evolution of graph databases. This paper aims to propose a two-step framework where rewriting rules formalize instance or schema changes, ensuring graph’s consistency with respect to constraints, and updates are managed by ensuring rule applicability through the generation of side effects: new updates which guarantee that rule application conditions hold. Design/methodology/approach This paper proposes Schema Evolution Through UPdates, optimized version (SetUpOPT), a theoretical and applied framework for the management of resource description framework (RDF)/S database evolution on the basis of graph rewriting rules. The framework is an improvement of SetUp which avoids the computation of superfluous side effects and proposes, via SetUpoptND, a flexible and extensible package of solutions to deal with non-determinism. Findings This paper shows graph rewriting into a practical and useful application which ensures consistent evolution of RDF databases. It introduces an optimised approach for dealing with side effects and a flexible and customizable way of dealing with non-determinism. Experimental evaluation of SetUpoptND demonstrates the importance of the proposed optimisations as they significantly reduce side-effect generation and limit data degradation. Originality/value SetUp originality lies in the use of graph rewriting techniques under the closed world assumption to set an updating system which preserves database consistency. Efficiency is ensured by avoiding the generation of superfluous side effects. Flexibility is guaranteed by offering different solutions for non-determinism and allowing the integration of customized choice functions.

Download Full-text

A Graph-Rewriting Paradigm for Discrete Relaxation

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001498000439 ◽

1998 ◽

Vol 12 (06) ◽

pp. 763-799 ◽

Cited By ~ 15

Author(s):

H. Fahmy ◽

D. Blostein

Keyword(s):

Complex Analysis ◽

Contextual Information ◽

A Priori ◽

Image Interpretation ◽

Semantic Interpretation ◽

Music Notation ◽

Image Domain ◽

Graph Rewriting ◽

Time Signature ◽

Rewriting Rules

In image analysis, recognition of the primitives plays an important role. Subsequent analysis is used to interpret the arrangement of primitives. This subsequent analysis must make allowance for errors or ambiguities in the recognition of primitives. In this paper, we assume that the primitive recognizer produces a set of possible interpretations for each primitive. To reduce this primitive-recognition ambiguity, we use contextual information in the image, and apply constraints from the image domain. This process is variously termed constraint satisfaction, labeling or discrete relaxation. Existing methods for discrete relaxation are limited in that they assume a priori knowledge of the neighborhood model: before relaxation begins, the system is told (or can determine) which sets of primitives are related by constraints. These methods do not apply to image domains in which complex analysis is necessary to determine which primitives are related by constraints. For example, in music notation, we must recognize which notes belong to one measure, before it is possible to apply the constraint that the number of beats in the measure should match the time signature. Such constraints can be handled by our graph-rewriting paradigm for discrete relaxation: here neighborhood-construction is interleaved with constraint-application. In applying this approach to the recognition of simple music notation, we use approximately 180 graph-rewriting rules to express notational constraints and semantic-interpretation rules for music notation. The graph rewriting rules express both binary and higher-order notational constraints. As image-interpretation proceeds, increasingly abstract levels of interpretation are assigned to (groups of) primitives. This allows application of higher-level constraints, which can be formulated only after partial interpretation of the image.

Download Full-text

Rule-based transformation of graph rewriting rules: Towards higher-order graph grammars

Theoretical Computer Science ◽

10.1016/j.tcs.2015.01.034 ◽

2015 ◽

Vol 594 ◽

pp. 1-23 ◽

Cited By ~ 9

Author(s):

Rodrigo Machado ◽

Leila Ribeiro ◽

Reiko Heckel

Keyword(s):

Higher Order ◽

Graph Grammars ◽

Rule Based ◽

Graph Rewriting ◽

Rewriting Rules

Download Full-text

Survey of Graph Rewriting applied to Model Transformations

Proceedings of the 2nd International Conference on Model-Driven Engineering and Software Development ◽

10.5220/0004731504310441 ◽

2014 ◽

Keyword(s):

Model Transformations ◽

Graph Rewriting

Download Full-text

Product family modeling and design support: An approach based on graph rewriting systems

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060402020097 ◽

2002 ◽

Vol 16 (2) ◽

pp. 103-120 ◽

Cited By ~ 15

Author(s):

XUEHONG DU ◽

JIANXIN JIAO ◽

MITCHELL M. TSENG

Keyword(s):

Empirical Studies ◽

Product Family ◽

Generic Model ◽

Design Support ◽

Product Families ◽

Graph Rewriting ◽

Rewriting Systems ◽

Engineering Design Problems ◽

Graph Rewriting Systems ◽

Rewriting Rules

Earlier research on product family design (PFD) often highlights isolated and successful empirical studies with a limited attempt to explore the modeling and design support issues surrounding this economically important class of engineering design problems. This paper proposes a graph rewriting system to organize product family data according to the underpinning logic and to model product derivation mechanisms for PFD. It represents the structural and behavioral aspects of product families as family graphs and related graph operations, respectively. The derivation of product variants becomes a graph rewriting process, in which family graphs are transformed to variant graphs by applying appropriate graph rewriting rules. The system is developed in the language of programmed graph rewriting systems or PROGRES, which supports the specification of hierarchical graph schema and parametric rewriting rules. A meta model is defined for family graphs to factor out those entities common to all product families. A generic model is defined to describe all specific entities relevant to particular families. An instance model describes all product variants for individual customer orders. A prototype of a graph-based PFD system for office chairs is also developed. The system can provide an interactive environment for customers to make choices among product offerings. It also facilitates design automation of product families and enhances interactions and negotiations among sales, design, and manufacturing.

Download Full-text