简要介绍了OLAP的概念和特点;从OLAP最新概念和技术等方面,解释了微软SQL Server 2005中统一维度模型;研究了微软现代OLAP搭建方法与传统搭建方法的差异;通过基于企业销售数据多维数据集的OLAP实例,说明了微软最新OLAP的设计概念的可行...简要介绍了OLAP的概念和特点;从OLAP最新概念和技术等方面,解释了微软SQL Server 2005中统一维度模型;研究了微软现代OLAP搭建方法与传统搭建方法的差异;通过基于企业销售数据多维数据集的OLAP实例,说明了微软最新OLAP的设计概念的可行性,有助于企业方便快捷地建立自已的分析决策支持平台。展开更多
In this paper, we designed a customer-centered data warehouse system with five subjects： listing, bidding, transaction, accounts, and customer contact based on the business process of online auction companies. For ea...In this paper, we designed a customer-centered data warehouse system with five subjects： listing, bidding, transaction, accounts, and customer contact based on the business process of online auction companies. For each subject, we analyzed its fact indexes and dimensions. Then take transaction subject as example, analyzed the data warehouse model in detail, and got the multi-dimensional analysis structure of transaction subject. At last, using data mining to do customer segmentation, we divided customers into four types： impulse customer, prudent customer, potential customer, and ordinary customer. By the result of multi-dimensional customer data analysis, online auction companies can do more target marketing and increase customer loyalty.展开更多
基金Acknowledgements： This work is supported by the National Natural Science Foundation of China （No. 60473012）, the Natural Science Foundation of Jiangsu Province of China （No. BK2005047, BK2004052 and BK2005046）, the Tenth-Five High Technology Key Project of Jiangsu Province of China （No. BG2004034）, and the Natural Science Foundation of yangzhou University （No. KK0413161）.
文摘简要介绍了OLAP的概念和特点;从OLAP最新概念和技术等方面,解释了微软SQL Server 2005中统一维度模型;研究了微软现代OLAP搭建方法与传统搭建方法的差异;通过基于企业销售数据多维数据集的OLAP实例,说明了微软最新OLAP的设计概念的可行性,有助于企业方便快捷地建立自已的分析决策支持平台。
基金Supported by the National Natural Science Foundation of China (70471037)211 Project Foundation of Shanghai University (8011040506)
文摘In this paper, we designed a customer-centered data warehouse system with five subjects： listing, bidding, transaction, accounts, and customer contact based on the business process of online auction companies. For each subject, we analyzed its fact indexes and dimensions. Then take transaction subject as example, analyzed the data warehouse model in detail, and got the multi-dimensional analysis structure of transaction subject. At last, using data mining to do customer segmentation, we divided customers into four types： impulse customer, prudent customer, potential customer, and ordinary customer. By the result of multi-dimensional customer data analysis, online auction companies can do more target marketing and increase customer loyalty.
