一种Vague数据模型及属性的归纳依赖关系

A Vague Data Model and Induction Dependencies between Attributes

提出了一种比现有模糊关系数据模型更有效的 Vague值 Vague关系数据模型。该模型引入了最近由Gau等提出的 Vague集的思想 ,从真、假隶属度两个方面体现元素隶属于以及不隶属于某个概念的程度 ,从而使得数据能够更加贴切地反映客观事物的本来面目。在给出两 Vague元组间 ε-相等定义的基础上 ,较为详细地讨论了 Vague关系代数 ,为在 Vague环境下对数据库进行操作提供了依据。最后 ,文章分析了 Vague关系数据库中的数据依赖问题 ,给出了基于不分明关系和基于包含因子的 Vague数据依赖的两种评判策略 ,并就其有关性质给出了证明。

A data model called vague data vague relation data model is presented. By introducing the idea of vague sets proposed by Gau et al., the model has much stronger expressive ability than that of the existing fuzzy relation data models. It behaves more flexibility than the fuzzy models which is characterized by both a truth-membership function and a falsity-membership function. It shows that the vague model can easily describe the database objects in their original appearances. Based on the concept of ε -equal defined on two vague tuples, a vague relation algebra is also discussed in detail so as to operate the database in vague environment. Finally, the problem of data dependencies in vague relation data model is analyzed. Two strategies for data dependencies based on the indiscernibility relation and vague inclusion are given. Some properties are also provided and proved.