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：在本文中，我们研究了将数据挖掘与在线分析处理（OLAP）相结合的OLAM理论。本文提出了一种分布式数据挖掘服务体系结构，旨在解决当前数据挖掘工具中普遍存在的资源共享水平低，功能耦合过于紧密，软件难于重用等问题。根据服务体系结构及其分层思想，对总体结构进行了设计和分析。还详细说明了所有级别的设计。最后，基于Microsoft SQL Server 2005实现了销售数据的OLAP服务，并基于聚类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挖掘奠定了基础，并为该领域的分析师提供了科学的决策支持方法。
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