一种数据仓库的多维数据模型

数据模型是数据仓库研究的核心问题之一.很多研究表明,传统数据模型(如实体联系模型和关系模型)不能有效地表示数据仓库的数据结构和语义,也难以有效地支持联机分析处理(on-line analysis processing,简称OLAP).最近,人们提出了几种多维数据模型.但是,这些多维数据模型在表示数据仓库的复杂数据结构和语义以及OLAP操作方面仍显不足.该文以偏序和映射为基础,提出了一种新的多维数据模型.该数据模型能够充分表达数据仓库的复杂数据结构和语义,并提供一个以OLAP操作为核心的操作代数,支持层次结构间的复杂聚集操作序列,能够有效地支持OLAP应用.该数据模型支持聚集函数约束的概念,提供了表示层次结构间聚集函数约束的机制.

Multidimensional Data Modeling for Data Warehouses

Data model is a basic aspect in the research field of data warehouses. It has been argued that traditional data models, such as the ER model and the relational model, are in principle not powerful enough for modeling the data structure and semantics of data warehouse and supporting OLAP (on-line analysis processing). As a result, several multidimensional models based on multidimensional view of data have emerged. However, these multidimensional data models still fall short of ability to model complex data in some real-world application domains and to support complete OLAP operations. In this paper, the authors propose a new multidimensional data model based on the concepts of partial order and mapping. This model addresses supporting for complex data structure and semantics of data warehouses, especially complex hierarchies in dimensions. Along with the model, they also present an associated algebra that includes a complete set of OLAP operations and supports complex aggregation, roll-up and drill-down along hierarchies in dimensions. A new concept of aggregation function constraint is also presented in this paper, and the mechanism for expressing and checking the aggregation function constraint is included in the model.