Abstract

Due to its extensive potential applications, model management has attracted many research interests and gained great progress. To provide easy-to-use interfaces, we have proposed a graph transformation-based model management approach that provides intuitive interfaces for manipulation of graphical data models. The approach consists of two levels of graphical operators: low-level customizable operators and high-level generic operators, both of which consist of a set of graph transformation rules. Users need to program or tune the low-level operators for desirable results. To further improve the ease-of-use of the graphical model management, automatic generation of low level of operators is highly desirable. The paper formalizes specifications of low- and high-level operators and proposes a generator to automatically transform high-level operators into low-level operators upon specific input data models. Based on graph transformation theoretical foundation, we design an algorithm for the generator to automatically produce low-level operators from input data models and mappings according to a high-level operator. The generator, called AutoGen, therefore eliminates many tedious specifications and thus eases the use of the graphical model management system.

Keywords: Model management; Graph transformation; Graph grammar; Visual programming; Schema interoperation

Article Outline

1. Introduction

2. The reserved graph grammar formalism

3. Model management by graph grammars

3.1. Graphical data models

3.2. Graphical mappings

3.3. Graphical operators

4. A generator for specific rules

4.1. Automatic generation overview

4.2. Preliminaries

4.3. Generator definitions

4.4. A generation algorithm

5. Illustrative examples

5.1. A merge example

5.2. A ModelGen example

6. Related work

7. Conclusion

Acknowledgements

References