In this thesis, we studied the theory of OLAM which combined data mining with On-Line Analytical Processing (OLAP). This thesis presents a kind of distributed data mining service architecture aiming to resolve the problems that appear widely in the current data mining tools, such as low level of resource sharing, functional coupling too closely, difficult to reuse software and so on. On the basis of service architecture and its Hierarchical thinking, the overall structure was designed and analyzed specifically; all levels of design were also illustrated in details. Finally, OLAP service on sales data was implemented based on Microsoft SQL Server 2005 and sales forecast project was built based on cluster analysis algorithm as well. It has made a foundation for customer data analysis and sales rules mining, and provided a scientific decision support method for the analyst in this area.
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机译：在本论文中，我们研究了奥拉姆的理论，将数据挖掘与在线分析处理（OLAP）组合。 本文介绍了一种分布式数据挖掘服务架构，旨在解决当前数据挖掘工具中广泛出现的问题，例如低水平的资源共享，功能耦合太密切，难以重复使用软件等。 在服务架构及其层次思维的基础上，专门设计和分析了整体结构; 还详细说明了各种各样的设计。 最后，基于Microsoft SQL Server 2005的Microsoft SQL Server 2005实现了OLAP服务，并基于集群分析算法构建了销售预测项目。 它为客户数据分析和销售规则采矿奠定了基础，并为该地区分析师提供了科学决策支持方法。
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