一种通用GML 3.0解析引擎的设计与实现

GML提供了一套核心模式和一个基于对象／属性的简单语义模型,使用GML模型及其模式组件,用户可以灵活定义自己的应用模式.然而由于GML建模的这种灵活性,导致不同领域、不同用户定义的应用模式之可能存在较大的差异,从而大大增加了GML数据处理软件的设计难度。如何解析不同用户、不同领域的GML应用模式并理解GML应用模式中元素及类型的语义,就成了基于GML空间数据语义集成共享必须解决的难题。本文首先简单介绍了GML3.0中最常用的3个核心模式、GML应用模式与核心模式的关系及GML模式中元素类型间的约束关系:然后分析了GML 3.0要素的属性组成;提出了通用GML3.0解析引擎中元素语义识别方法;最后,利用JavaBeans技术,在JBuilder X环境下实现了通用GML 3.0解析引擎。

Implementation of Universal GML 3.0 Parsing Engine

GML provides a set of core schemas together with a simple semantic model between objects andproperties. Using the GML model and its schema components, users can define their own GML application schemas flexibly. However, this flexibility results in much difference among GML application schemas of different users and in different domains, and also presents a substantial difficulty for writing GML software applications because the software would be expected to understand any GML dataset. The software can perform type discovery on the schema to identify which XML elements from the GML dataset represent a feature, a feature's properties, a feature's geometric properties, and a feature's topological properties. How to parse arbitrary GML user application schemas and understand the semantic meaning of elements and types becomes a difficult problem that must be solved in GML based spatial data semantic integrating and sharing. Developing one universal GML parsing engine is very important to GML based spatial data semantic integrating and sharing. Three core schemas used most frequently and relationship between GML application schema and core schemas, and constraint rules among elements and types in GML schemas are introduced first in this paper. Feature's properties composition is analyzed in section 3. In section 4, method of semantic identifying elements in GML application schema is put forward. In section 5, the universal GML Parsing Engine is implemented using JavaBeans techniques in JBuilder X Environment. Section 6 is the conclusion of this paper.