联机分析处理中的非规则维建模

预聚集技术通过预先计算并保存原始数据上的查询结果以实现联机分析处理系统的快速查询响应能力.然而，在实际应用中，许多非规则维的结构难以使用传统多维模型进行建模，从而影响了预聚集技术的使用.为此，基于子级别到父级别的部分映射定义级别之间的部分序关系，进而提出了一个支持非覆盖、非映上等非规则维中维级别关系建模的维模型.同时，在维模型基础上，定义了支持非规则维的立方体模型以及典型的联机分析处理操作.多维模型与关系模式的转换定义和实例分析证明了该多维模型可以实现对各种非规则维的建模支持，保证了预聚集技术在联机分析处理中的使用.

Modeling Irregular Dimensions in OLAP

Pre-aggregation is one of the most effective techniques in OLAP to ensure quick responses to user queries in data warehouses. The technique usually requires dimension hierarchies to be onto, covering and self-onto. However, in real-world applications, many irregular dimensions do not meet the requirements. To solve the problem, a novel dimension model is proposed to support the modeling of irregular dimensions by defining a partial mapping from a child level to a parent level. Based on the four cases of the partial mappings among different levels, the model can express the special relationships among the levels in irregular dimensions, such as non-covering and non-onto relationships. Furthermore, a cube model is defined based on the dimension model, along with some typical OLAP operations. The mapping from the multidimensional model to relational tables shows that the model provides a feasible way to overcome the difficulties caused by irregular dimensions.