商务智能是近年来企业信息化的热点，有着广阔的应用前景。本书首先介绍了商务智能的基本概念、商务智能系统的架构以及数据仓库、OLAP 、数据挖掘等核心技术。在此基础上，讨论了商务智能在电子商务、移动商务、知识管理、Web 挖掘、企业绩效管理和流程管理等领域的应用。此外，还分析了商务智能在国内外的发展趋势。

本书内容比较新颖、全面，案例丰富，适合计算机应用、软件工程、信息管理、电子商务和管理科学等相关专业本科生和研究生的教材，也可作为从事商务智能的信息化人员的参考资料。

从20 世纪80 年代以来，企业进入了信息时代，市场全球化，顾客需求多样化、个性化，变化频率加快，竞争范围和激烈程度逐渐加大和加剧。在这种快鱼吃慢鱼的商业环境中，企业为了生存，就必须迅速反应，实施管理信息化和决策智能化。Internet 、各种管理应用系统的广泛使用，为企业打通了数字神经，减少了企业运营成本，提高了企业的效率。另一方面，企业在提高效率的同时，也要考虑其本身的效益，这就要求企业决策者及时掌握运营过程中的各种信息、知识，而不是拍脑袋解决问题。信息、知识已成为企业最基本、最重要的生产要素，而这些信息、知识是各种管理应用系统难以提供的。在信息化提高企业竞争力的同时，各种管理应用系统也积累大量的数据。这些数据是企业的重要资产，其中蕴涵了许多有价值的信息、知识。事实上，日益积累的数据利用率还相当低，如何从中充分挖掘有价值的信息和知识，提高管理人员的决策水平，满足不同层次、不同部门和行业应用的需求，已成为业界和学术界关注的问题。

新经济时代的赢家是那些把顾客、供应商等相关的运营数据整合、分析和共享，转化为信息，并进一步分析得到知识，提高企业智能从而保持赢利的企业。面对激烈的竞争，传统的决策支持系统（Decision Support System，DSS）已难以支撑，而作为ERP 应用之后的企业信息化亮点，商务智能，也称为商业智能为企业提供了这样的一种利器。2002 年IDC 的研究表明，一些商务智能项目在一年多的时间就会获得430 ％的回报。商务智能具有传统DSS、主管信息系统（Executive Information System，EIS）等不具备的强大数据管理、数据分析和知识发现的能力，已成为企业差异化的重要因素，它对改善商务决策水平，采取有效的商务行动，提升企业绩效是非常有效的，因此在竞争比较激烈、信息化基础比较扎实的一些行业，例如银行、电信、零售、保险和制造等受到了重视，已成为信息化领域继ERP 、CRM 和SCM 等应用软件之后的新热点。近几年来，随着竞争的进一步加剧，越来越多的企业青睐商务智能，希望获得先机以抢占市场有利位置。欧美的国家在商务智能方面的投资逐年上涨，商务智能已形成一个产业。据IDC 预测，商务智能软件在中国内地的年销售额平均增长至少为65.6％。商务智能需求的增长，也促进了商务智能厂商不断进行技术创新，以抢占尚不成熟、处于高速发展阶段的商务智能市场。最近Oracle、SAP 和IBM 等公

IV 

司的并购案例，充分说明了未来商务智能市场的光明前途。

商务智能的成功需要人们对商务智能有一定的理解，目前熟悉商务智能的人才还很缺

乏。从内容上来讲，商务智能包括数据仓库、在线分析处理、数据挖掘及其应用。目前国

内在商务智能的教学才刚起步，尤其是教材基本还集中在数据挖掘方面，而难度适中、综

合介绍商务智能的教材极少。针对这种情况，作者参阅了大量国内外最新的商务智能资料，

编写了一本较全面反映商务智能的教材，并在复旦大学软件工程专业的研究生、本科生中

多次使用。本书不是一本深入探讨商务智能技术的学术性书籍，而是对商务智能的基本问

题进行系统的介绍，为读者对商务智能的深入学习打下基础。为增强读者的感性认识，本

书的讨论配有很多实例，每章最后还附有主要的参考文献以供读者更深入地学习。

全书共13 章，分为5 个部分。第一部分是引言，讨论商务智能基础，主要介绍了商务

智能的发展、概念、价值以及目前在一些领域的应用情况，使读者对商务智能有一个概要

的认识。第二部分介绍商务智能的核心技术，由第2～5 章组成，涉及商务智能系统架构、

数据仓库、在线分析处理和数据挖掘等核心技术，这些内容是商务智能的理论基础，此部分

内容不刻意介绍复杂的、扩展的数据分析算法，而是强调基本内容的应用。对于有一定基础

的读者，可以在学习这些内容后选择专门介绍数据仓库和数据挖掘的书籍深入学习。第三部

分由第6～11 章组成，涉及商务智能最新的一些前沿问题，总结了商务智能在移动商务、商

务智能与知识管理、Web 挖掘、企业绩效管理、电子商务和流程挖掘等领域的最新应用。此

部分内容是提高性的，供有一定基础的读者阅读。第12 章针对目前商务智能的进展，对国

内外商务智能的前景进行了展望。最后，第13 章介绍了香港大学电子商务科技研究所（ETI）

开发的数据挖掘系统AlphaMiner ，可供学生实验时使用。

本书在编写过程中得到了香港大学黄哲学教授和Business Objects（SAP）公司鲁百年

博士的指导，研究生范力、王安华、林涵溪、周佶平、曹烨和吴海峰等同学在案例分析等

方面做了许多工作，特表示感谢。由于作者理论水平和实践经验有限，书中难免有不当和

疏漏之处，望广大读者指正。

赵卫东

      2008 年12 月于复旦大学

第一部分商务智能基础

第1章商务智能概论··············································································································3 

1.1 

商业决策需要商务智能······························································································3 

1.1.1 

数据、信息与知识·························································································4 

1.1.2 

管理就是决策·································································································5 

1.1.3 

决策需要信息和知识·····················································································5 

1.1.4 

智能型企业·····································································································5 

1.1.5 

商务智能支持商业决策··················································································6 

1.2 

商务智能简介·············································································································7 

1.2.1 

商务智能概念·································································································7 

1.2.2 

商务智能的发展···························································································10 

1.2.3 

商务智能的价值···························································································11 

1.3 

商务智能系统的功能································································································14 

1.4 

商务智能的应用·······································································································16 思考题································································································································22 本章参考文献····················································································································22 

第二部分商务智能核心技术

第2章商务智能系统架构·····································································································27 

2.1 

商务智能系统的组成································································································27 

2.2 

数据集成···················································································································29 思考题································································································································31 本章参考文献····················································································································32 

第3章数据仓库····················································································································34 

3.1 

从数据库到数据仓库································································································34 

3.2 

数据仓库的概念·······································································································36 

3.3 

数据集市···················································································································38 

3.4 

元数据·······················································································································39 

VI 

3.5  ETL····························································································································42 

3.6 

操作数据存储···········································································································43 

3.7 

数据仓库模型···········································································································44 

3.8 

数据挖掘查询语言····································································································48 

3.9 

医保数据仓库设计····································································································50 思考题································································································································58 本章参考文献····················································································································58 

第4章在线分析处理············································································································59 

4.1 

OLAP 简介················································································································59 

4.2 

OLAP 与OLTP ·········································································································62 

4.3 

OLAP 操作················································································································62 

4.4 

OLAP 分类················································································································66 

4.5 

OLAP 操作语言········································································································69 

4.6 

流行的OLAP 工具···································································································72 思考题································································································································76 本章参考文献····················································································································76 

第5章数据挖掘····················································································································78 

5.1 

数据挖掘基础···········································································································78 

5.1.1 

数据挖掘的概念···························································································78 

5.1.2 

数据挖掘的发展···························································································80 

5.1.3 

数据挖掘的过程···························································································81 

5.1.4 

数据挖掘原语与语言···················································································84 

5.1.5 

基于组件的数据挖掘···················································································86 

5.1.6 

可视化技术···································································································88 

5.1.7 

数据挖掘的隐私保护···················································································91 

5.2 

数据挖掘的典型应用领域························································································92 

5.3 

数据预处理···············································································································93 

5.4 

聚类分析···················································································································99 

5.4.1 

聚类的概念···································································································99 

5.4.2 

聚类分析的统计量·······················································································99 

5.4.3 

常用聚类算法·····························································································102 

5.4.4 

其他聚类方法·····························································································109 

5.4.5 

离群点检测·································································································110 

5.5 

分类分析·················································································································111 

5.5.1 

贝叶斯分类器·····························································································112 

5.5.2 

决策树·········································································································115 

5.5.3 

支持向量机·································································································123 

5.5.4 

BP 神经网络·······························································································128 

5.5.5 

其他分类方法·····························································································131 

5.6 

关联分析·················································································································133 

5.6.1 

关联规则·····································································································134 

5.6.2 

Apriori 算法································································································136 

5.6.3 

FP 增长算法································································································139 

5.6.4 

其他关联规则挖掘算法··············································································141 

5.7 

序列模式挖掘·········································································································142 

5.7.1 

基本概念·····································································································142 

5.7.2 

类Apriori 算法···························································································143 

5.8 

回归分析·················································································································145 

5.8.1 

一元回归分析·····························································································146 

5.8.2 

多元线性回归分析·····················································································149 

5.8.3 

其他回归分析·····························································································153 

5.9 

时间序列分析·········································································································153 

5.10 

数据挖掘技术与应用的发展方向········································································155 思考题······························································································································157 本章参考文献··················································································································160 

第三部分商务智能应用

第6章移动商务智能··········································································································165 

6.1 

移动商务·················································································································165 

6.2 

商务智能在移动商务中的应用··············································································166 思考题······························································································································173 本章参考文献··················································································································174 

第7章商务智能与知识管理·······························································································175 

7.1 

知识管理·················································································································175 

7.2 

知识管理与商务智能的关系··················································································176 

7.2.1 

商务智能与知识管理的区别······································································176 

7.2.2 

商务智能与知识管理的联系······································································177 

VIII 

思考题······························································································································179 本章参考文献··················································································································179 第8章Web挖掘·················································································································181 

8.1 

Web 挖掘基础·········································································································181 

8.2 

Web 内容挖掘·········································································································183 

8.3 

Web 结构挖掘·········································································································188 

8.4 

Web 日志挖掘·········································································································192 思考题······························································································································196 本章参考文献··················································································································197 

第9章商务智能在企业绩效管理中的应用·······································································199 

9.1 

企业绩效管理的层次······························································································200 

9.2 

商务智能贯穿企业绩效管理的闭环流程······························································201 

9.3 

商务智能在企业绩效管理中的应用······································································203 

9.4 

商务智能给企业绩效管理带来的价值··································································206 

9.5 

企业绩效管理的主要工具······················································································207 思考题······························································································································210 本章参考文献··················································································································210 

第10章数据挖掘在电子商务中的应用·············································································211 

10.1 

电子商务需要数据挖掘························································································211 

10.2 

顾客管理···············································································································212 

10.3 

网站结构优化·······································································································215 

10.4 

智能搜索引擎·······································································································217 

10.5 

异常事件确定·······································································································219 思考题······························································································································220 本章参考文献··················································································································221 

第11章工作流挖掘············································································································222 

11.1 

工作流挖掘的发展································································································222 

11.2 

工作流挖掘的概念与作用····················································································223 

11.3 

工作流挖掘的内容································································································225 

11.3.1  

工作流模型的重构··················································································225 

11.3.2  

工作流的监控与工作流挖掘的评价·······················································226 

11.3.3  

组织视图挖掘··························································································228 

11.4 

工作流挖掘的应用································································································229 思考题······························································································································231 本章参考文献··················································································································231 

第四部分商务智能发展

第12章商务智能进展········································································································235 

12.1 

商务智能应用趋势································································································235 

12.2 

商务智能在中国的发展························································································239 

12.3 

商务智能动态·······································································································242 思考题······························································································································247 本章参考文献··················································································································247 

第五部分数据挖掘实验

第13章ＡphaMiner数据挖掘系统···················································································251 

13.1 

AlphaMiner 简介···································································································251 

13.2 

AlphaMiner 的应用·······························································································254 思考题······························································································································260 本章参考文献··················································································································260