数据仓库技术在商业智能系统中的应用
摘要
当今世界已进入全面知识管理时代，信息量的急剧增加使得企业的决策过程日益复杂。新兴的商业智能技术为企业提供了新的选择，越来越多的管理者开始借助商业智能技术为企业寻求解决之道。虽然当前商业智能的研究与应用正处于从起步阶段向发展阶段过渡的转变时期，其理论研究与系统应用已成为目前国内外企业界和软件开发界广泛关注的热点问题。
商业智能是对商业信息的搜集、管理和分析过程，目的是使企业各级决策者获得知识或洞察力，促使他们做出对企业更有利的决策。商业智能一般由数据仓库、数据分析、数据挖掘、在线分析、数据备份和恢复等部分组成。
目前对于商业智能的开发还没有一套系统的研究，本文从这些出发对该系统作较为全面的研究，并在项目实践过程中对其中的经验进行总结，对其中的重要问题提供相应的对策。
本文在第一章主要介绍了商业智能的产生以及体系结构，开发及实施过程以及商业智能的应用。第二章和第三章分别介绍了商业智能系统实现的技术基础：数据仓库和商业智能。在第二章重点对数据仓库产生的背景、数据仓库的体系结构、ETL、OLAP等技术做了深入研究；对数据仓库、数据集市、数据库做了比较分析；对数据仓库的实现策略、开发流程、实施特点进行研究，同时比较分析了常用的数据仓库工具以及数据仓库的新技术。这些为后面的系统实现奠定了基础。第四章和第五章主要介绍了系统的设计、实现。重要的是对系统实现过程中的经验作了总结，为以后系统的开发提供了重要的参考。
The world today has entered the era of comprehensive knowledge management, the dramatic increase in the amount of information allows companies increasingly complex decision-making process. Emerging business intelligence (BI) technology provide new options, more and more managers start using business intelligence technology for enterprises seeking a solution road. While the current business intelligence research and application are at the transitional period from the initial stage to the development stage, its theoretical research and application systems have attracted a wide attention of the software development circle at home and abroad.
Since there is no systemic research for the development of the business intelligence at present, in this article the author made an all-around study for this system, furthermore, the author summed up the experience of the more important issues and provided the corresponding Countermeasures in the course of the practice.
Business intelligence is a process of the collection, management and analysis for the business information, whose purpose is to ensure the corporate decision makers at all levels to acquire knowledge or insight, in order to make them more favorable to business decision-making. Business intelligence is composed of data warehouse, data analysis, data mining, online analysis, data backup and recovery.
In this article, the author introduces the birth, intelligent architecture, development and application of the business intelligence in the first chapter. The author introduces the base technology for the implement of the BI system and data warehouse and mining respectively in the second and third chapter. The second chapter focuses not only on the research of the background of data warehouse, data warehouse architecture and the technology of ETL, OLAP; on the comparative analysis of the data warehouse, data market and database; but also the investigation for the implement strategy, developing line and characteristic of the data warehouse; and makes a comparative analysis between the data warehouse tool and new technology. Both these pave the way for the system achievement. The fourth and fifth chapter mostly introduces the designment and implement of the system.
The chapter mainly sums up the important experience issue and provides important reference for the developing of the system.
商业智能是对商业信息的搜集、管理和分析过程，目的是使企业各级决策者获得知识或洞察力，促使他们做出对企业更有利的决策。商业智能一般由数据仓库、数据分析、数据挖掘、在线分析、数据备份和恢复等部分组成。
目前对于商业智能的开发还没有一套系统的研究，本文从这些出发对该系统作较为全面的研究，并在项目实践过程中对其中的经验进行总结，对其中的重要问题提供相应的对策。
本文在第一章主要介绍了商业智能的产生以及体系结构，开发及实施过程以及商业智能的应用。第二章和第三章分别介绍了商业智能系统实现的技术基础：数据仓库和商业智能。在第二章重点对数据仓库产生的背景、数据仓库的体系结构、ETL、OLAP等技术做了深入研究；对数据仓库、数据集市、数据库做了比较分析；对数据仓库的实现策略、开发流程、实施特点进行研究，同时比较分析了常用的数据仓库工具以及数据仓库的新技术。这些为后面的系统实现奠定了基础。第四章和第五章主要介绍了系统的设计、实现。重要的是对系统实现过程中的经验作了总结，为以后系统的开发提供了重要的参考。
The world today has entered the era of comprehensive knowledge management, the dramatic increase in the amount of information allows companies increasingly complex decision-making process. Emerging business intelligence (BI) technology provide new options, more and more managers start using business intelligence technology for enterprises seeking a solution road. While the current business intelligence research and application are at the transitional period from the initial stage to the development stage, its theoretical research and application systems have attracted a wide attention of the software development circle at home and abroad.
Since there is no systemic research for the development of the business intelligence at present, in this article the author made an all-around study for this system, furthermore, the author summed up the experience of the more important issues and provided the corresponding Countermeasures in the course of the practice.
Business intelligence is a process of the collection, management and analysis for the business information, whose purpose is to ensure the corporate decision makers at all levels to acquire knowledge or insight, in order to make them more favorable to business decision-making. Business intelligence is composed of data warehouse, data analysis, data mining, online analysis, data backup and recovery.
In this article, the author introduces the birth, intelligent architecture, development and application of the business intelligence in the first chapter. The author introduces the base technology for the implement of the BI system and data warehouse and mining respectively in the second and third chapter. The second chapter focuses not only on the research of the background of data warehouse, data warehouse architecture and the technology of ETL, OLAP; on the comparative analysis of the data warehouse, data market and database; but also the investigation for the implement strategy, developing line and characteristic of the data warehouse; and makes a comparative analysis between the data warehouse tool and new technology. Both these pave the way for the system achievement. The fourth and fifth chapter mostly introduces the designment and implement of the system.
The chapter mainly sums up the important experience issue and provides important reference for the developing of the system.
引文
[1] 谢炜，等．商务智能：新一代决策支持领域．计算机科学，2001，28(4)：9～16．
[2] 潘和平，赫琪．零售业商业智能解决方案．武汉：武汉大学数字智能研究组商业技术报告．DIRCCTR，2000．
[3] Colin J W. Decision Processing :the Next Generation of BusinessIntelligence. Database Associates International Inc. www.dmreview.com.2001.
[4] Hammer J, et al. The Stanford Data Warehousing Project http://www.stanford.edu/warehousing/warehouse.html.2001.
[5] Inmon W H. What Is a Data Warehouse ? www.chian3i.net, 2001.
[6] Klauer P , Brobst S. Building a Data Warehouse for DecisionSupport. (2nd Edition). Prentice Hall PTR. Prentice。 Hall ,Inc. 1998.
[7] Colin J W. Data Warehousing :Next Generation Solutions www.cio.com/research/data. 2000.
[8] Brohman M K. The Business Intelligence Value Chain :Datadriven Decision Support in a Data Warehouse Environment: an Exploratory Study. Proceedings of the 33rd Hawaii International Conference on System Sciences. 2000.
[9] Colin J W. IBM'S Teraplex Integration Centers: the Key to theIntelligent Business. Database Associate Version 1 www.ibm.com/software/data/pubs/papers. 2000.
[10] 韩家炜．数据挖掘概念与技术．北京：电子工业出版社，
[11] Jason Weir. A web/ business intelligence solution. Information systems management. Winter, 2000
[12] Dan Sullivan. The Need for Text Mining in Business Intelligence. DM Review. Dec. 2000
[13] [美]Efrem G．Mallach／李昭勇译．决策支持与数据仓库技术．北京：电子工业出版社．
[14] 石丽，李坚．数据仓库与决策支持．国防工业出版社，2003，5．
[15] Building the Data Warehouse (third version) by W. H. Inmon ,Wiley, John & Sons, Incorporated , 2002
[16] The Rise of the I- Market : The Convergence of E- Bisiness and Business Intelligence By Brenda Moncla and Faber Consulting Published in DMREVIEWin August 2000
[17] Data Warehouse: The Second Generation By Marcel Bhend Published in DMREVIEWin August 1998
[18] Data Warehouse in the Telecommunications Industry Copy by 1999 NUMA Q.
[19] Top Ten Trend in Data Warehousing By Dorinne Hoss Publishedin DMREVIEWin October 2001
[20] Data Warehouse Justification and ROI By William Mc KnightPublished in DMREVlEWin November 2000
[21] W．H．Inmon ＆ Ken Rudin 著王天佑等译．数据仓库管理．北京电子工业出版社，2000
[22] 数据仓库——追求最高的投资回报，中华智能网．
[23] NCR Scalable Data Warehouse技术白皮书．NCR(中国)有限公司，2000
[24] Michael Corey 等著施平安等译．Oracle8i数据仓库．北京机械工业出版社，2002
[25] Microsoft Corporation 著，郭东青李佳刘彬彬译．数据库创建、数据仓库与优化．北京清华大学出版社
[26] 张维明，邓苏(ZHANGWeiming，DENG Su)．数据仓库原理与应用(Theory and app lication of data warehouse)．北京：电子工业出版社(Peking：Publish ing house of electronics industry)，2002．
[27] 李建中，高宏．一种数据仓库的多维数据模型(A multidimensional data model in datawarehouse)．软件学报，2000，11(7)：908—917．
[28] 陈京民．数据仓库与数据挖掘技术 北京：电子工业出版社，2002．
[29] Akhil Kumar. An efficient algorithm for solving the partial sum query problem, Information Sciences, 2001, 137:245—258
[30] 王珊等．数据仓库技术与联机分析处理．科学出版社，1998．
[31] 张宁，贾自艳，等．数据仓库中ETL技术的研究．计算机工程与应用．2002，38(24)：213-216．
[32] 周春光，邢辉，徐振龙，王哲(ZHOU Chunguang，XING Hui，XU Zhenlong，WANGZhe)．商业数据的预测模型及其算法研究。吉林大学学报(信息科学版) 2002，20(3)：53—60．
[33] 林萍，蒋波．关于数据仓库中联机分析处理的几点研究．计算机时代，2002，(1)．
[34] 彭木根．数据仓库技术与实现北京：电子工业出版社，2002．
[35] Data Warehouses and Data Marts: Aynamic View, Author: Joseph M. Firestone, Ph.D. , Executive Information System Inc. White Paper 3 , March 27 , 1997; (3)
[36] Building the Data Mart, Author:Marc Demarest, DBMSMagazine, 1999 ;7 (8) :44
[37] 朱焱．浅论数据抽取，净化和转换工具．计算机应用，2000，20(4)：1-3．
[38] 张宁，贾自艳等．数据仓库中ETL技术的研究．计算机工程与应用．2002，38(24)．213-216．
[39] Agosta L. The Data Warehousing ETL Tool Market Matures.http://www.metagroup.com/cgi-bin/inetcgi/jsp/displayArticle.do?oid=32637
[40] Agosta L. Extraction, Transformation and Loading in Transition. http://www.metagroup.com/cgi-bin/inetcgi/jsp/displayArticle.do?oid=32539
[41] Gilpin M. Emerging Internet Data Integration Solutions. http://www.metagroup.com/cgi-bin/inetcgi/jsp/displayArticle.do?oid=41234
[42] Oracle. ETL Processing Within Oracle 9i—— An Oracle WhitePaper. http://otn.oracle.com/oracleworld/ow_integration_owp.html
[43] 彭永清，方旭生．用多维数据库实现数据集市．电脑开发与应用，2001，14(12)：24-25．
[44] E. Rahm, Hong Hai Do. Data Cleaning : Problems and current approachs. IEEE Bulletin of the ACM ,1998 ,41(2) :79-82
[45] A Metadata Management How - To, Author : Terry Moriarty, Database Programming &Design ,Feburary 1997: 57 -60
[46] Sheth A. P. ,Changing Focus on Interoperability in Information Systems : From System , Syntax , Structure to Semantics . Interoperating Geographic Information Systems , Kluwer , Academic Publishers ,1998. 5-30.
[47] Henry F. Korth, Abraham Siberschatz. Database Research Faces the Information Explosion. Common ACM ,1997 ,40(2) :139-142.
[48] Haag S , Cummings M , Dawkins J. Management Information Systems for the Information Age .Columbus : McGraw - Hill Companies , Inc. 1998
[49] R Agrawal , A Gupta , S Sarawagi .Modeling MutidimensionalDatabase . Proc. ICDE. 19971
[50] E Baralis , S Paraboschi , E Teniente. Materialized view selection in a multidimensional database . Proc. VLDB ,1987:156-1651
[51] L Cabbibo and R Torlone. Querying Multimensional Databases .Proc. DBPL , 19981
[52] B Dinter , C Sapia, GBofing, MBlaschka. The OLAPMarket :State of the Art and Research Issues . Proc. First International Workshop on Data Warehousing and OLAP(DOLAP), Bethesda ,MD ,Nov. 1998
[53] H Gupta. Selection of Views to Materialize in a Data Warehouse .Proc. ICDT, January 19971
[54] W H Inmon. Building the Data Warehouse. John Wiley & Sons. Inc. ,19931
[55] Informix数据仓库解决方案及Red Brick Decision Server．中国计算机报，2000．49
[56] Hoog R. Use Your Intranet for Effective Knowledge Management E-Business Advisor, 1999(4): 26-31.
[57] informix数据仓库解决方案http://www.world3ilcom/solution/Informix.htm．
[58] 九大数据仓库产品评析http://www.yesky.com/20010713/188957.shtml.
[59] Brio公司．Brio技术白皮书．2002
[2] 潘和平，赫琪．零售业商业智能解决方案．武汉：武汉大学数字智能研究组商业技术报告．DIRCCTR，2000．
[3] Colin J W. Decision Processing :the Next Generation of BusinessIntelligence. Database Associates International Inc. www.dmreview.com.2001.
[4] Hammer J, et al. The Stanford Data Warehousing Project http://www.stanford.edu/warehousing/warehouse.html.2001.
[5] Inmon W H. What Is a Data Warehouse ? www.chian3i.net, 2001.
[6] Klauer P , Brobst S. Building a Data Warehouse for DecisionSupport. (2nd Edition). Prentice Hall PTR. Prentice。 Hall ,Inc. 1998.
[7] Colin J W. Data Warehousing :Next Generation Solutions www.cio.com/research/data. 2000.
[8] Brohman M K. The Business Intelligence Value Chain :Datadriven Decision Support in a Data Warehouse Environment: an Exploratory Study. Proceedings of the 33rd Hawaii International Conference on System Sciences. 2000.
[9] Colin J W. IBM'S Teraplex Integration Centers: the Key to theIntelligent Business. Database Associate Version 1 www.ibm.com/software/data/pubs/papers. 2000.
[10] 韩家炜．数据挖掘概念与技术．北京：电子工业出版社，
[11] Jason Weir. A web/ business intelligence solution. Information systems management. Winter, 2000
[12] Dan Sullivan. The Need for Text Mining in Business Intelligence. DM Review. Dec. 2000
[13] [美]Efrem G．Mallach／李昭勇译．决策支持与数据仓库技术．北京：电子工业出版社．
[14] 石丽，李坚．数据仓库与决策支持．国防工业出版社，2003，5．
[15] Building the Data Warehouse (third version) by W. H. Inmon ,Wiley, John & Sons, Incorporated , 2002
[16] The Rise of the I- Market : The Convergence of E- Bisiness and Business Intelligence By Brenda Moncla and Faber Consulting Published in DMREVIEWin August 2000
[17] Data Warehouse: The Second Generation By Marcel Bhend Published in DMREVIEWin August 1998
[18] Data Warehouse in the Telecommunications Industry Copy by 1999 NUMA Q.
[19] Top Ten Trend in Data Warehousing By Dorinne Hoss Publishedin DMREVIEWin October 2001
[20] Data Warehouse Justification and ROI By William Mc KnightPublished in DMREVlEWin November 2000
[21] W．H．Inmon ＆ Ken Rudin 著王天佑等译．数据仓库管理．北京电子工业出版社，2000
[22] 数据仓库——追求最高的投资回报，中华智能网．
[23] NCR Scalable Data Warehouse技术白皮书．NCR(中国)有限公司，2000
[24] Michael Corey 等著施平安等译．Oracle8i数据仓库．北京机械工业出版社，2002
[25] Microsoft Corporation 著，郭东青李佳刘彬彬译．数据库创建、数据仓库与优化．北京清华大学出版社
[26] 张维明，邓苏(ZHANGWeiming，DENG Su)．数据仓库原理与应用(Theory and app lication of data warehouse)．北京：电子工业出版社(Peking：Publish ing house of electronics industry)，2002．
[27] 李建中，高宏．一种数据仓库的多维数据模型(A multidimensional data model in datawarehouse)．软件学报，2000，11(7)：908—917．
[28] 陈京民．数据仓库与数据挖掘技术 北京：电子工业出版社，2002．
[29] Akhil Kumar. An efficient algorithm for solving the partial sum query problem, Information Sciences, 2001, 137:245—258
[30] 王珊等．数据仓库技术与联机分析处理．科学出版社，1998．
[31] 张宁，贾自艳，等．数据仓库中ETL技术的研究．计算机工程与应用．2002，38(24)：213-216．
[32] 周春光，邢辉，徐振龙，王哲(ZHOU Chunguang，XING Hui，XU Zhenlong，WANGZhe)．商业数据的预测模型及其算法研究。吉林大学学报(信息科学版) 2002，20(3)：53—60．
[33] 林萍，蒋波．关于数据仓库中联机分析处理的几点研究．计算机时代，2002，(1)．
[34] 彭木根．数据仓库技术与实现北京：电子工业出版社，2002．
[35] Data Warehouses and Data Marts: Aynamic View, Author: Joseph M. Firestone, Ph.D. , Executive Information System Inc. White Paper 3 , March 27 , 1997; (3)
[36] Building the Data Mart, Author:Marc Demarest, DBMSMagazine, 1999 ;7 (8) :44
[37] 朱焱．浅论数据抽取，净化和转换工具．计算机应用，2000，20(4)：1-3．
[38] 张宁，贾自艳等．数据仓库中ETL技术的研究．计算机工程与应用．2002，38(24)．213-216．
[39] Agosta L. The Data Warehousing ETL Tool Market Matures.http://www.metagroup.com/cgi-bin/inetcgi/jsp/displayArticle.do?oid=32637
[40] Agosta L. Extraction, Transformation and Loading in Transition. http://www.metagroup.com/cgi-bin/inetcgi/jsp/displayArticle.do?oid=32539
[41] Gilpin M. Emerging Internet Data Integration Solutions. http://www.metagroup.com/cgi-bin/inetcgi/jsp/displayArticle.do?oid=41234
[42] Oracle. ETL Processing Within Oracle 9i—— An Oracle WhitePaper. http://otn.oracle.com/oracleworld/ow_integration_owp.html
[43] 彭永清，方旭生．用多维数据库实现数据集市．电脑开发与应用，2001，14(12)：24-25．
[44] E. Rahm, Hong Hai Do. Data Cleaning : Problems and current approachs. IEEE Bulletin of the ACM ,1998 ,41(2) :79-82
[45] A Metadata Management How - To, Author : Terry Moriarty, Database Programming &Design ,Feburary 1997: 57 -60
[46] Sheth A. P. ,Changing Focus on Interoperability in Information Systems : From System , Syntax , Structure to Semantics . Interoperating Geographic Information Systems , Kluwer , Academic Publishers ,1998. 5-30.
[47] Henry F. Korth, Abraham Siberschatz. Database Research Faces the Information Explosion. Common ACM ,1997 ,40(2) :139-142.
[48] Haag S , Cummings M , Dawkins J. Management Information Systems for the Information Age .Columbus : McGraw - Hill Companies , Inc. 1998
[49] R Agrawal , A Gupta , S Sarawagi .Modeling MutidimensionalDatabase . Proc. ICDE. 19971
[50] E Baralis , S Paraboschi , E Teniente. Materialized view selection in a multidimensional database . Proc. VLDB ,1987:156-1651
[51] L Cabbibo and R Torlone. Querying Multimensional Databases .Proc. DBPL , 19981
[52] B Dinter , C Sapia, GBofing, MBlaschka. The OLAPMarket :State of the Art and Research Issues . Proc. First International Workshop on Data Warehousing and OLAP(DOLAP), Bethesda ,MD ,Nov. 1998
[53] H Gupta. Selection of Views to Materialize in a Data Warehouse .Proc. ICDT, January 19971
[54] W H Inmon. Building the Data Warehouse. John Wiley & Sons. Inc. ,19931
[55] Informix数据仓库解决方案及Red Brick Decision Server．中国计算机报，2000．49
[56] Hoog R. Use Your Intranet for Effective Knowledge Management E-Business Advisor, 1999(4): 26-31.
[57] informix数据仓库解决方案http://www.world3ilcom/solution/Informix.htm．
[58] 九大数据仓库产品评析http://www.yesky.com/20010713/188957.shtml.
[59] Brio公司．Brio技术白皮书．2002