具有多时间粒度的强全序时态模式中多值依赖问题研究

在时态数据库中存在着与时态多值依赖相关的存储冗余、更新异常等问题.但由于时态多值依赖的复杂性，使得根据时态多值依赖所进行的范式分解并不能总保持无损联接，为此对强全序时态模式中时态多值依赖与无损分解的相关性进行了深入研究，给出了规则的时态多值依赖(RTMVD)和属性集的时间粒度等概念，并给出了RTMVD的一套有效的推理规则，针对强全序时态模式中时态多值依赖与无损分解的相关性给出了相关定理，解决了规则的时态多值依赖环境下时态模式在多粒度上的无损分解问题，为规则时态多值依赖环境下时态模式的进一步规范化奠定了基础.

Study of Multi-Valued Dependency in Strong Total Order Temporal Scheme with Multiple Time Granularities

In temporal databases, the amount of data increases rapidly because of the introduction of time dimension which in turn causes storage abnormity and data redundancy. Therefore, it’s very important to effectively design temporal database. Data dependency is a core concept in database design, analogously temporal data dependency is also a core concept in temporal database design. Besides the storage redundancy and update abnormity associated with temporal functional dependencies, there also exist the storage redundancy and update abnormity associated with temporal multi-valued dependencies in temporal databases. However, because of temporal multi-valued dependencies’ complexity, normal forms decomposition based on it can’t always be lossless. To resolve the problem of lossless decomposition of temporal schemes with multi-valued dependencies, the following work has been done. The relativity between temporal multi-valued dependencies and lossless decomposition in strong total temporal schemes is investigated. The concepts of regular temporal multi-valued dependency and time granularity of attribute set are given. A set of effective inferring rules of regular temporal multi-valued dependencies is presented and theories of relativity between temporal multi-valued dependencies and lossless decomposition in strong total temporal schemes are also presented. The problem of lossless decomposition of temporal schemes with multiple time granularities under conditions of regular temporal multi-valued dependencies is resolved, laying the foundation for further normalization of temporal schemes with regular temporal multi-valued dependencies.