AutoGen: Easing model management through two levels of abstraction

  • Authors:

  • Guanglei Song;Jun Kong;Kang Zhang

  • Affiliations:

  • Department of Computer Science, University of Texas at Dallas, Richardson, TX 75083-0688, USA;North Dakota State University, USA;Department of Computer Science, University of Texas at Dallas, Richardson, TX 75083-0688, USA

  • Venue:
  • Journal of Visual Languages and Computing
  • Year:
  • 2006

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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.