基于产业链协作平台的商务智能架构及数据挖掘技术研究
|
摘要
汽车产业链是由整车制造商、供应商、经销商、服务商等相关企业组成的庞大网络。基于ASP(Application Service Provider, ASP)模式的产业链协作平台,是降低产业链的运营成本、实现产业链协作中复杂信息交互的有效途径,已成为提升汽车制造业竞争能力的主要手段之一。这种公共服务平台的成功实施,提高了汽车产业链上下游企业的协作效率,降低了企业信息交换、传递和获取的成本,促进了企业业务数据的积累。
随着企业业务数据的积累越来越多,以及市场竞争环境的不断加剧,如何利用这些数据创造价值成为企业下一步思考的问题。企业希望能够把大量的业务数据迅速转换成可靠的信息,发现数据背后隐藏的知识,找到潜在的规律,从而提高决策质量,把握和发现市场机遇,提升企业的竞争力。但是,传统的信息管理系统已经难以满足企业的这些新需求,由数据仓库、联机分析处理和数据挖掘等技术支撑的商务智能(Business Intelligence, BI)给企业带来了新希望。BI是一种基于大量数据基础上的提炼和重新整合知识的过程,这个过程与知识共享和知识创造紧密结合,完成了从信息到知识的转变,帮助企业决策者做出及时、正确、可行和有效的决策,最终增强企业的竞争优势。
本文以四川省制造业信息化研究院开发的汽车产业链协作ASP平台为基础,依托国家科技支撑计划课题“面向高竞争性行业的产业链协作技术集成推广应用(2006BAF01A37)"的支持,进行了以下几个方面的研究工作：
1)随着汽车产业链协作ASP平台的成功推广实施,企业数据的积累量与日俱增。面对加剧的市场竞争环境,企业管理者考虑在现有基础上实施商务智能系统,以满足在决策信息方面的需求。本文结合汽车产业链平台的特点以及用户的需求,提出了基于汽车产业链协作平台的商务智能系统的解决方案和体系架构；
2)车辆故障分析是挖掘故障配件与车辆行驶里程、使用时间、使用地区、配件品牌、相关故障件等信息之间的关联规则,其目的是为了提高整车设计的合理性。关联规则挖掘的核心算法是寻找频繁集,经典的Apriori关联规则挖掘算法采用的候选项集产生原理决定了该算法寻找频繁集时对数据库扫描过于频繁；无候选项集FP-Growth算法,通过FP-tree寻找频繁集,极大地减少了I／O数据交换,但是,FP-tree的规模和数据特征有关,当数据集大且过于稀疏时,构造基于内存的FP-tree是不现实的。而且由于先天性“剪枝”算法的不足,导致这两种算法在数据库发生变化时,原来发现的频繁集很难重用。针对以上问题,提出了基于矩阵的频繁集发现及更新算法；
3)客户是企业的重要战略资源,高效的客户关系管理以扎实的客户细分为基础,而实现客户细分的主要途径就是客户聚类。设计高效、准确的客户细分聚类算法,成为汽车协作产业链平台实施客户细分的关键。针对经典的FKP混合数据聚类算法存在收敛速度慢、初始聚类中心选择困难等问题,提出一种利用遗传算法快速搜索FKP算法需要的初始聚类中心、同时利用FKP算法对染色体进行优化、避免早熟收敛的GA-FKP聚类算法。
GA_FKP聚类挖掘算法是对混合数据聚类算法改进,具有一定的通用性；基于频繁矩阵的频集发现算法,从一个全新的角度,发现频繁集,弥补了现有算法的不足；基于汽车产业链协作平台的商务智能系统架构实现了和原有平台系统的无缝结合以及程序资源共享,最大限度减少了系统开发工作量,为其他中小企业的信息系统集成BI功能提供一个参考方法。
The automobile industrial chain is a huge network, which is composed of automobile manufacturers, suppliers, dealers and service providers etc. ASP-based industrial chain collaborated platform is an effective way to cut the operation cost of industrial chain, and realize the complicated information exchange in industrial chain collaboration, and the platform has become a major way to enhance the competitiveness of automobile industry. The successful implementation of the public technology service platform improves the collaborative efficiency of upstream and downstream business in automobile industrial chain, lowers the cost of business information exchange, transmission and acquisition, and accelerates the accumulation of business data.
With the accumulation of enterprise's business data and competition of market environment, how to make use of these data to create value becomes the next issue needed to be considered by the enterprises. The enterprises hope to transform these data into reliable information, find the potential rule, improve the quality of decision, grasp and discover the market opportunity, and enhance the enterprise competitiveness. But the traditional information system can not meet the new requirement of enterprise. However, the business intelligence based on data warehouse, online analytical processing and data mine brings new hope for enterprise. Business intelligence is a process based on the extraction of mass data and knowledge re-integration, this process combines closely with knowledge share and knowledge creation, accomplishes the transformation from information to knowledge, and helps the enterprise policy maker make a timely, correct, feasible and effective decision, and finally strengthens the enterprise's competitiveness.
Based on automobile industrial chain collaborative ASP platform developed by Sichuan Institute of Manufacturing Information, which is supported by National technology project " highly competitive business-oriented promotion and application of industrial chain collaborative technology integration (2006BAF01A37)", following aspects of research work are finished.
1) With the successful promotion and implementation of automobile industrial collaborative ASP Platform, The accumulation of enterprise data has increased day by day. Face to the increased competition in the market environment, the managers of enterprise consider to implement the BI system based on current situation, to meet the requirements of information decision. Combined with the feature of automobile industrial chain and customer requirements, the solution and architecture of BI system based on automobile industrial chain collaborative platform is put forward.
2) Faulty analysis of vehicles is to find the association rules between faulty accessories and vehicle mileage, time, region, brand of accessory, related faulty accessory, which is to improve the performance of the overall design. The core algorithm of association rules mining is to find frequent item set. The candidate item set generation principle, used by the classical association rules mining algorithm Apriori, determines that scanning the database of this algorithm is too frequently when finding the frequent item set. FP-Growth algorithm of none candidate item set reduces the I/O data exchange greatly through finding frequent set by FP-tree. But the scale of FP-tree is related to data characteristic. Constructing FP-tree based on the memory is not realistic when data set is big and too sparse. And the congenital deficiency of the pruning algorithm causes that the frequent set is difficult to reuse when the database changes. For the issue mentioned above, frequent set discovering and updating algorithm based on matrix are put forward.
3) The customers are the important strategic resources of enterprises; efficient customer relation management is on the basis of solid customer segmentation. And the main way to realize the customer segmentation is customer cluster. Design an efficient, accurate customer segmentation cluster algorithm, which is the key to realize customer segmentation of automobile collaborative industrial chain platform. Based on the problems of slow convergence rate and difficulty on initial cluster center selection of mixed data cluster algorithm-FKP, GA-FKP algorithm is raised in this thesis. This algorithm use GA to search the initial clustering center needed by FKP algorithm, optimize the chromosome with FKP algorithm and avoid premature convergence.
GA_FKP cluster mining algorithm is an improved algorithm of mixed data cluster, and it has generality. FIMABoFM frequent itemset find algorithm find the frequent itmeset from a new perspective, and make up for the shortcomings of existing algorithms. Automobile industrial chain collaborative platform-based BI system architecture realizes the seamless integration and program resource sharing to the original platform, minimizes the workload of system development, and provide a reference method to information system BI integration of other small-and-medium-sized enterprises
随着企业业务数据的积累越来越多,以及市场竞争环境的不断加剧,如何利用这些数据创造价值成为企业下一步思考的问题。企业希望能够把大量的业务数据迅速转换成可靠的信息,发现数据背后隐藏的知识,找到潜在的规律,从而提高决策质量,把握和发现市场机遇,提升企业的竞争力。但是,传统的信息管理系统已经难以满足企业的这些新需求,由数据仓库、联机分析处理和数据挖掘等技术支撑的商务智能(Business Intelligence, BI)给企业带来了新希望。BI是一种基于大量数据基础上的提炼和重新整合知识的过程,这个过程与知识共享和知识创造紧密结合,完成了从信息到知识的转变,帮助企业决策者做出及时、正确、可行和有效的决策,最终增强企业的竞争优势。
本文以四川省制造业信息化研究院开发的汽车产业链协作ASP平台为基础,依托国家科技支撑计划课题“面向高竞争性行业的产业链协作技术集成推广应用(2006BAF01A37)"的支持,进行了以下几个方面的研究工作：
1)随着汽车产业链协作ASP平台的成功推广实施,企业数据的积累量与日俱增。面对加剧的市场竞争环境,企业管理者考虑在现有基础上实施商务智能系统,以满足在决策信息方面的需求。本文结合汽车产业链平台的特点以及用户的需求,提出了基于汽车产业链协作平台的商务智能系统的解决方案和体系架构；
2)车辆故障分析是挖掘故障配件与车辆行驶里程、使用时间、使用地区、配件品牌、相关故障件等信息之间的关联规则,其目的是为了提高整车设计的合理性。关联规则挖掘的核心算法是寻找频繁集,经典的Apriori关联规则挖掘算法采用的候选项集产生原理决定了该算法寻找频繁集时对数据库扫描过于频繁；无候选项集FP-Growth算法,通过FP-tree寻找频繁集,极大地减少了I／O数据交换,但是,FP-tree的规模和数据特征有关,当数据集大且过于稀疏时,构造基于内存的FP-tree是不现实的。而且由于先天性“剪枝”算法的不足,导致这两种算法在数据库发生变化时,原来发现的频繁集很难重用。针对以上问题,提出了基于矩阵的频繁集发现及更新算法；
3)客户是企业的重要战略资源,高效的客户关系管理以扎实的客户细分为基础,而实现客户细分的主要途径就是客户聚类。设计高效、准确的客户细分聚类算法,成为汽车协作产业链平台实施客户细分的关键。针对经典的FKP混合数据聚类算法存在收敛速度慢、初始聚类中心选择困难等问题,提出一种利用遗传算法快速搜索FKP算法需要的初始聚类中心、同时利用FKP算法对染色体进行优化、避免早熟收敛的GA-FKP聚类算法。
GA_FKP聚类挖掘算法是对混合数据聚类算法改进,具有一定的通用性；基于频繁矩阵的频集发现算法,从一个全新的角度,发现频繁集,弥补了现有算法的不足；基于汽车产业链协作平台的商务智能系统架构实现了和原有平台系统的无缝结合以及程序资源共享,最大限度减少了系统开发工作量,为其他中小企业的信息系统集成BI功能提供一个参考方法。
The automobile industrial chain is a huge network, which is composed of automobile manufacturers, suppliers, dealers and service providers etc. ASP-based industrial chain collaborated platform is an effective way to cut the operation cost of industrial chain, and realize the complicated information exchange in industrial chain collaboration, and the platform has become a major way to enhance the competitiveness of automobile industry. The successful implementation of the public technology service platform improves the collaborative efficiency of upstream and downstream business in automobile industrial chain, lowers the cost of business information exchange, transmission and acquisition, and accelerates the accumulation of business data.
With the accumulation of enterprise's business data and competition of market environment, how to make use of these data to create value becomes the next issue needed to be considered by the enterprises. The enterprises hope to transform these data into reliable information, find the potential rule, improve the quality of decision, grasp and discover the market opportunity, and enhance the enterprise competitiveness. But the traditional information system can not meet the new requirement of enterprise. However, the business intelligence based on data warehouse, online analytical processing and data mine brings new hope for enterprise. Business intelligence is a process based on the extraction of mass data and knowledge re-integration, this process combines closely with knowledge share and knowledge creation, accomplishes the transformation from information to knowledge, and helps the enterprise policy maker make a timely, correct, feasible and effective decision, and finally strengthens the enterprise's competitiveness.
Based on automobile industrial chain collaborative ASP platform developed by Sichuan Institute of Manufacturing Information, which is supported by National technology project " highly competitive business-oriented promotion and application of industrial chain collaborative technology integration (2006BAF01A37)", following aspects of research work are finished.
1) With the successful promotion and implementation of automobile industrial collaborative ASP Platform, The accumulation of enterprise data has increased day by day. Face to the increased competition in the market environment, the managers of enterprise consider to implement the BI system based on current situation, to meet the requirements of information decision. Combined with the feature of automobile industrial chain and customer requirements, the solution and architecture of BI system based on automobile industrial chain collaborative platform is put forward.
2) Faulty analysis of vehicles is to find the association rules between faulty accessories and vehicle mileage, time, region, brand of accessory, related faulty accessory, which is to improve the performance of the overall design. The core algorithm of association rules mining is to find frequent item set. The candidate item set generation principle, used by the classical association rules mining algorithm Apriori, determines that scanning the database of this algorithm is too frequently when finding the frequent item set. FP-Growth algorithm of none candidate item set reduces the I/O data exchange greatly through finding frequent set by FP-tree. But the scale of FP-tree is related to data characteristic. Constructing FP-tree based on the memory is not realistic when data set is big and too sparse. And the congenital deficiency of the pruning algorithm causes that the frequent set is difficult to reuse when the database changes. For the issue mentioned above, frequent set discovering and updating algorithm based on matrix are put forward.
3) The customers are the important strategic resources of enterprises; efficient customer relation management is on the basis of solid customer segmentation. And the main way to realize the customer segmentation is customer cluster. Design an efficient, accurate customer segmentation cluster algorithm, which is the key to realize customer segmentation of automobile collaborative industrial chain platform. Based on the problems of slow convergence rate and difficulty on initial cluster center selection of mixed data cluster algorithm-FKP, GA-FKP algorithm is raised in this thesis. This algorithm use GA to search the initial clustering center needed by FKP algorithm, optimize the chromosome with FKP algorithm and avoid premature convergence.
GA_FKP cluster mining algorithm is an improved algorithm of mixed data cluster, and it has generality. FIMABoFM frequent itemset find algorithm find the frequent itmeset from a new perspective, and make up for the shortcomings of existing algorithms. Automobile industrial chain collaborative platform-based BI system architecture realizes the seamless integration and program resource sharing to the original platform, minimizes the workload of system development, and provide a reference method to information system BI integration of other small-and-medium-sized enterprises
引文
[1]Karl V D B. BI Web Services Enabling Next Generation BI Extranets for total Visibilit Over the Value Chain. http://www.businessobjects.com/global/pdf/whitepapers/biws_wp.pdf.2008.1
[2]余长慧,潘和平.商业智能及其核心技术.计算机应用研究.2002,19(9)：14-15
[3]路耀华,张劲松.建立整合性商务智能系统的思考.经济纵横.2002,(12)：17-18
[4]朱晓武.商务智能的理论和应用研究综述.计算机系统应用.2007,(1)：115-117
[5]孙林夫.国家高新技术研究发展计划先进制造业领域重点项目立项申请书-制造业商务智能技术及产品研发.西南交通大学,2007,1-10.
[6]吴俊.商业智能打造企业竞争优势的利器.科学决策.2004,(9)：28-31
[7]Bernard Liautaud.e-Business Intelligence:Turning Information into Knowledge into Profit.McGraw-Hill Trade,2002,3-39
[8]言君.商务智能大师为BI喝彩.电子商务.2003,(12)：14
[9]赵华,张立厚,宋静.用商务智能完善客户关系管理的信息流程.情报杂志.2005,(10)：61-64
[10]何应龙.一种中小企业创新产品评价与投资决策的新方法.科技进步与对策.2007,24(11)：88-90
[11]高翠芬.商业智能及其应用分析.现代商贸工业.2008,20(2)：138-139
[12]罗明,周叶.基于MAS的供应链绩效智能管理系统构架研究.中国市场.2007,(2)：74-76
[13]2010年亚太地区BI和BPM市场将达6.39亿美元.http://news.e-works.net.cn/category6/news20140:htm.2008.3.20
[14]]IDC：商务智能2010年规模达19亿英镑.http://cio.ccw.com.cn/data/ccw/sb/htm2007/20070207_240762.asp.2008.2.27
[15]商业智能(BI)技术已成为企业差异化因素.http://www.ciotimes.com.2008.3.20
[16]王刚.混合智能系统及其在商务智能中的应用研究.复旦大学博士论文.2008：143-144
[17]夏维力,许昌元.商务智能技术在企业战略决策中的支持作用及方法研究.软科学.2004,18(3)：16-17
[18]胡培元,赫岩.商务智能大师：伯纳德.利奥托德.电子商务.2003,(12)：20-25
[19]徐嵩泉.商务智能(BI)在制造业“开花结果”http://www.amteam.org/print.aspx?id=484106.2008.10.20
[20]W.F.Cody, J.T.Kreulen,V Krishna,W.S.Spangler.The integration of business intelligence and knowledge management.IBM Systems Journal.2002
[21]J. Foley, J. D. Russell.Mining Your Own Business.Information Week. 1998,3(19):12-14
[22]Kamel Rouibah, Samia Ould-ali.Puzzle; aconcept and prototype for linking business intelligence to business strategy. Journal of Strategic Information System 2002; 11:133-152
[23]Karl Vanden Bergh, Timo Elliott.Why Standardizing Business Intelligence Is Critical.Business Obbeds Whitepaper,2003:1-3
[24]王茁,顾洁.三位一体的商务智能(BI)—管理、技术与应用.电子工业出版社.2004：2-5
[25]徐绚,王德生.应用商业智能系统推进企业管理信息化.情报杂志.2004,(8)：79-81
[26]焦慧敏,汪林林.商务智能在现代企业中的应用与研究.计算机工程与设计.2006,27(13)：2503-2505
[27]胡慧.全球制造业价值链的重构与我国的现实选择.科技进步与对策.2004,(4)： 43-46
[28]孔令丞,郁义鸿.经济全球化与“中国制造”：一个基于价值链增值的视角的研究.科技导报.2005,(1)：58-60
[29]梅丽,张向阳.全球价值链视角下的制造业升级.商业经济.2005,(10)：17-19
[30]李培根院士揭晓2007制造业信息化十大热点.http://news.e-works.net.cn/category6/news26524.htm.2008.3.14
[31]赵慧娟.面向装备和汽车产业链的企业网络化协作平台开发及应用.四川省制造业信息化研究院.2005：32-33
[32]ITPUB调查：中国BI技术与应用现状.www.it168.com.2008.12.31
[33]Edward R.Tufte.Envisioning Information.Graphics Press.1990
[34]Codd E. F, Codd S. B, Salley C. T.Beyond decision support.Computer World.1993,27(30):87-89
[35]Ken Orr. Data Qualitv and systems theory. Communications of the ACM. 1998,41(2):66-71
[36]Burges C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery.1998,2(2):127-167
[37]Nello Cristianini, John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press.2000
[38]Cortes C,Vapnik V.Support-vector networks.Machine Learning. 1995,20 (3):273-297
[39]Kahaner Larry. Competitive Intelligence:How to Gather,Analyze & Use Information to Move Your Business to the Top. Simon & Schuster,Inc. 1996
[40]Berry M,Linoff G. Data Mining Techniques:For Marketing,Sales,and Customer Support. John Wiley & Sons,Inc.1997
[41]Fayyad U.M, Piatetsky-Shapiro Smyth G.Pand Uthurusam R. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press,1996
[42]G Houhen,K Lenie,K Vanhoof. A knowledge-based SWOT-analysis system as an instrument for strategic planning in small and medium sized enterprises. Decision Support Systems.1999,26:125-135
[43]Jack G, Olin.Knowledge management across multi-tier enterprises:The promise of intelligent software in the auto industry. European Management Journal.1999,17(4):335-347
[44]Bill Inmon.Corporate Information Factory. John Wiley & Sons.2000
[45]Bill Inmon.Data Warehousing for E-Business. John Wiley & Sons.2001
[46]Albert A Angehrn,Michael Gihhert,Katerina Nicolopoulou. Management focus guest editors'introdction:Understanding learning networks. European Management Journal.2003,21(5):559-562
[47]Cody W. F., Kreulen J. T. The integration of business intelligence and knowledge management. IBM Systems Journal,2002,4(14):697-714
[48]Spil T. A. M. Business intelligence in healthcare organizations. Proceedings of the 35th Annual Hawaii International Conference on System Sciences. Waikoloa, Hawaii.2002:101-105
[49]Claire A. A stakeholder model of business intelligence. Proceedings of the 37th Annual Hawaii International Conferenc on System Sciences, Waikoloa, Hawaii.2004:223-23
[50]http://www.businessobjects.com/news/presscd/default.asp.2008.7.27
[51]张云涛,龚玲.商业智能设计、部署与实现.电子工业出版社,2004： 7-8
[52]周华云,邓燕.制造业ERP与BI的融合势在必行.http://www. amteam. org/print. aspx?id=613477.2008.10
[53]王汝林.商务智能驱动丰田扩张136亿的启示.http://www.gdpx.com.cn/news/2006112091751443502.2008.7
[54]度关注汽车制造业信息化.http://www.topoint.com.cn/html/e/2008/04/207620.html.2008.1.19
[55]SAP管理软件全面提升神龙汽车企业竞争力.http://www.enet.com.cn/article/2006/0815/A20060815165453.shtml.2008.3.20
[56]Business Objects公司商务智能系列案例之五--“驾驶舱”指引汽车飞奔.中国计算机用户.2007,17(3)：66-66
[57]夏国恩.基于商务智能的客户流失预测模型与算法研究.西南交通大学博士论文.2007：9-17
[58]郭伟刚,鞠时光.制造企业面向销售的通用智能商务系统研究.佛山科技学院学报(自然科学版),2005,23(4)：40-43
[59]李瑞臻,朱云龙.汽车行业营销OLAP系统设计与开发[J].信息与控制.2006,35(4)：522-531
[60]朱艺华.基于双层数据仓库的MIS设计研究.计算机工程与应用.2002；38(12)：198-203
[61]BI未来发展将出现三大趋势.http://soft.ccw.com.cn/comment/htm2007/20070608_269727.shtml.2007.12.7
[62]2007-2008中国商业智能市场发展报告.http://www.chinabi.net/Article/binews/200801/927.html.2008.5.11
[63]Alexander L. Factor.应用服务链供应商(ASP)解决方案.孙延明译.电子工业出版社,2003：240-248
[64]韩敏.基于ASP的汽车产业链协作模式、技术、平台研究.西南交通大学博士论文.2006：20-28
[65]赵慧娟,王淑营,孙林夫.面向中小企业信息化建设的ASP服务平台.计算机集成制造系统.2004,10(11)：1441-1443
[66]汽车行业的全球化,ANX意欲登陆中国.http://finance.163. com/economy2003/editor_2003/040609/040609_209189.html. 2008.1.13
[67]孙林夫.基于ASP的制造业产业链协作平台.四川省制造业信息化研究院.2006：11-13
[68]Christopher J. Matheus. System for Kowledge Discovery in Databases. IEEE Transactions on Knowledge and Data Engineering.1993,5 (6):903-913
[69]袁玉波,杨传胜,黄廷祝.数据挖掘与最优化技术及其应用.科学出版社,2007：5-6
[70]李雄飞,李军.数据挖掘与知识发现.高等教育出版社.2003,118-130
[71]中华机械网.汽车行业高利润率时代一去不返.http://news. machine365. com/arts/050917/1/60837_2. html.2008.10.25
[72]严隽琪,马登哲,范菲雅.面向中小企业信息化的ASP平台研究与开发.计算机集成制造系统.2005,11(2)：178-182
[73]赵岩.协同售后服务系统三包费用管理的实现及关联规则挖掘研究.西南交通大学硕士论文.2006：50-52
[74]史鹏翔.面向产业链协作的汽车销售管理系统的实现与研究.西南交通大学硕士论文.2006：15-19
[75]BO商务智能平台.http://www. china. businessobjects. com/products/platform/default. asp20 08.10.25
[76]Microsoft商务智能解决方案.Microsoft商务智能解决方案白皮书.2008：7-15
[77]IBM商务智能解决方案.ttp://www.00.ibm.com/cn/downloadfiles/products/servers/intelligence.pdf. 2008:3-4
[78]陈永杰.SAP商务智能完全解决方案.机械工业出版社,2008：8-15
[79]用友公司简介. http://it.21cn.com/itnews/newsgn/2003-05-25/1053458.htm
[80]何进伟.用友推出ERP-U8商务智能解决方案.ttp://industry.ccidnet.com/art/31/20031015/67078_1.html.2009.1
[81]毛井井.BO战略结盟用友.ERP与商业智能融合“破茧”.ttp://www.enet.com.cn/article/2006/0913/A20060913193957.shtml.2009.1
[82]2006年商务智能应用状况报告.http://www.chinabi.net,2007.5
[83]罗来鹏,土少青.试论商务智能在企业决策中的应用.企业管理信息化.2005,3(19)：18-19
[84]董仲元等.中小企业服务网络模式和使能技术的应用研究.“面向新世纪的应用工程发展研究”研究报告之五,西安理工大学,2001.4
[85]FAN Li-lin. Research on the Mode of over Enterprises Application Based on Service Oriented Architecture.2007 International Symposium on Biometrics and Security Technologies, Bahria University Bahria University, Pakistan,2007
[86]范黎林,李亚.基于SOA的区域协同电子商务平台设计与研究,河北师大学报(自然科学版),2006,30(2)：154-157
[87]ZOU Shu-rong.The Application of SOA Technology on Automobile Supply Chain Information Integrated.TheInternational Conference 2007 on Information Computing and Automation,2007
[88]北京综艺达软件技术有限公司.基于N层架构的优势分析解决方案.ttp://publish.it168.com,2004.10
[89]马学良.基于网格计算的商务智能研究.浙江工商大学硕士学位论文.2006：40-41
[90]李绍文.面向中小型汽车企业的服务管理系统设计与开发.西南交通大学硕士学位论文.2006：41-42
[91]Atre, Shaku. Business Intelligence Success Is Never An Accident. Computerworld. September 15,2003
[92]彭木根.数据仓库技术与实现.电子工业出版社,2002：5-31
[93]陈京民.数据仓库与数据挖掘技术.电子工业出版社,2002,8-9
[94]沈兆阳.数据仓库与Analysis Services.清华大学出版社,2001,9-10
[95]夏火松.数据仓库与数据挖掘技术.科学出版社,2004,3-4
[96]Gupta, Vivek. An Introduction To Data Warehousing. System Services Corporation White Paper. August 1997
[97]Jeffrey Richter. Applied Microsoft.NET Framework programming.李建忠.清华大学出版社,2003
[98]Bill Jones, Jr. SQL Server Reporting Services-Lessons Learned In Development and Deployment. http://www.eggheadcafe.com/articles/20040823.asp. 2007.5
[99]李显生、鹿应荣、周茹波.汽车现代诊断技术的发展趋势.公路交通科技.2001,18(1)：41-44
[100]李美华.神经网络专家系统在汽车故障诊断中的应用研究.东北林业大学硕士论文.2006：29-45
[101]张晓丹.汽车发动机故障诊断中不确定性问题的贝叶斯网络解法.东北大学博士论文.2005：54-76
[102]Avner S. Extraction of Comprehensive Symbolic Rules from a Multi-layer Perceptron. Engineering Applications of Artificial Intelligence. 1996,9(2):137-143
[103]李剑川.贝叶斯网络故障诊断与维修决策方法及应用研究.国防科学技术大学博士论文.2002：20-30
[104]吴今培.智能故障诊断技术的发展和展望.振动、测试与诊断.1999,19(2)：79-86
[105]孙颖楷,张邦礼.基于粗糙集理论的人工神经网络故障诊断系统.重庆大学学报.2000,20(6)：53-56
[106]高梅梅,吴智铭.模糊推理Petri网及其在故障诊断中的应用.自动化学报,2000,26(5)：677-680
[107]丁彩红,黄文虎.应用petri网改进基于故障树的诊断方法.振动工程学报.2000,13(2)：277-282
[108]吴今培,肖健华.智能故障诊断与专家系统.科学出版社,1997,7-98
[109]吴泉源,刘江宁.人工智能与专家系统.国防科技大学出版社,1995,22-102
[110]蔡自兴,徐光佑.人工智能及其应用.清华大学出版社,1996,36-132
[111]Sharkey A J C, Sharkey N E, Chandroth G O. Diverse Neural Netsolutions to Fault Diagnosis Problem. Neural Computing & Applications. 1996, (4):218-222
[112]Staszewski W J, Worden K. Classification of faults in gearboxes:preprocessing algorithms and neural networks. Neural Computing & Applications. 1997,(5):160-183
[113]Lu Yi, Chen Tie, Brennan T. Hamilton. A Fuzzy Diagnostic Model and Its Application in Automotive Engineering Diagnosis. Applied Intelligence. 1998, (9):231-243
[114]郝丽娜、徐心和.粗糙集理论在故障诊断规则获取中的应用.信息与控制.2001,30(7)：586-590
[115]R.Agrawal, T. Imielinski, and A. Swami.Database mining:A performance perspective.IEEE Trans Knowledge and Data Engineering.1993,5(6):34-38
[116]R. Agrawal, J. Shafer.Parallel mining of association rules.IEEE Trans on Konowledge and Data Engineering.1996,8(6):962-969
[117]R.Srikant, R.Agrawal.Mining Quantiative association rules in large relational tables.Proceedings of ACM SIGMOD International Conference on Management of Data, Montreal, Canadal.996,1-12
[118]R.Srikant, R.Agrawal.Mining generalized association rules.Future Generation Computer Systems.1997,13:161-180
[119]R.Agrawal and R. Srikant. Fast algorithms for mining association rules. Proceeding of the 20th International Conference on Very Large Database, Santiago,Chile.1994,487-499
[120]J.S.Park, M.S.Chen, and P.S.Yu. An effective hash-based algorithm for mining association rules.Proceeding of International Conference on Very Large Database.1995,175-186
[121]A.Savasere, E.Omiecinski, and S.Navathe. An efficient algorithm for mining association rules in large databases. Proceeding of International Conference on Very Large Database.1995,432-443
[122]H.Toivonen. Sampling large databases for association rules. Proceeding of International Conference on Very Large Database.1996,134-145
[123]S.Brin, R.Motwani, J.D.Unman, and S.Tsur. Dynamic itemset counting and implication rules for market basket analysis. Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, Tucson, AZ.1997: 255-264
[124]J.Han,J.Pei and Y.Yin. Mining frequent patterns without candidate generation of Data. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, TX.2000,:1-10
[125]Jiawei Han, Jianpei. Mining frequent patterns without candidate generation:A frequenh pattern tree approach. Data Mining and Knowledge Discovery,2004, 8(1):53-87
[126]廖仁全,工利华,邱江涛.一种基于FP-tree的频繁项集增量更新算法.计算机工程与应用.2007,43(4)：176-178
[127]李忠哗,任春龙,何丕廉.一种基于FP-树的最大频繁模式增量更新挖掘算法.计算机应用与软件.2007,24(5)：47-49
[128]王黎明,赵辉.基于FP树的全局最大频繁项集挖掘算法.计算机研究与发展.2007,44(3)：445-451
[129]毛国君.数据挖掘技术与关联规则挖掘算法研究.北京工业大学博士论文.2003；63-64
[130]陈立潮,张建华,刘玉树.提高频繁项集挖掘算法效率的方法研究.计算机工程与应用.2002,(10)：34-35
[131]Huang Liusheng, Chen Huaping. A fast algorithm for mining association rules. Journal of Computer Science and Technology.2000,15(6):619-624
[132]曾万聘,周绪波,戴勃.关联规则挖掘的矩阵算法.计算机工程.2006,32(2)：45-47
[133]牛小飞,石冰,卢军.挖掘关联规则的高效ABM算法,计算机工程.2004,30(11)：118-120
[134]胡慧蓉,王周敬.一种基于关系矩阵的关联规则快速挖掘算法.计算机应用.2005,25(7)：1577-1579
[135]Feng Yu Cai, Feng Jian Lin. Incremental updating algorithm for mining association rules. Journal of Software.1998,9(4):301-306
[136]David W Cheung, Jiawei Han. Maintenance of Discovered Association Rules in Large Database:an Incremental Updating Technique. Proceeding of 12th International Conference on Data Engineering.1996:106-114
[137]Chen Jing-song, SHi Xiao-ying. An Incremental Updating Algorithm for Mining Association Rules. Computer Engineering.2002,28(7):106-109
[138]Zhu Yu-quan, Sun Zhi-hui, Ji Xiao-jun. Incremental Updating Algorithm Based on Frequent Pattern Tree for Mining Association Rule. Chinese Journal of Computers.2003,26(1):91-96
[139]Frequent Itemset Mining Dataset Repository, http://fimi.cs.helsinki.fi.2008.4
[140]Darrll Rigby, Frederick F. Reichheld. Avoid the Four Perils of CRM. Harvard Business Review.2002,(1):101-109
[141]张国政.客户关系管理中基于数据挖掘的客户细分研究.商业研究.2006,(13)：153-155
[142]Anderson D M. Agile product development for mass customization. Beijing; China Mechanical Industry Press,1999,10-16
[143]M. R. Anderberg. Cluster Analysis for Applications. Academic Press, 1973:10-12
[144]J. A. Saunders.Cluster Analysis for Market Segmentation. European Journal of Marketing.1980,14(7):433
[145]D. Hand,H. Mannia,P. Smyth. Principles of Data Mining. MIT Press,2001: 23-28
[146]Yuan Y.Serveral Optimal modes for data mining. Journal of University of Electronic Science and Technology.2004,33:1-5
[147]Ralambondrainy H.A conceptual version of the k-means algorithm. Pattern Recognition Letter.1995,16:1147-1157
[148]Biawae G, Weinberg J, Li C ITERATE:A conceptual clustering method for knowledge discovery in database. Artificial Intelligence in the Petroleum Industry. Braunschweig B and Day Reds,1995,111-139
[149]Cheesman P, Stutz J. Bayesian classification (AutoClass):theory and results. Advances in knowledge discovery and data mining. AAAI Press,1995
[150]Huang Zhexue. A Fuzzy K-modes Algorithm for Clustering Categorical Data. IEEE Transaction on Fuzzy Systems.1999,.7(4):466-452
[151]Huang Zhexue. Clustering large data sets with mixed numeric and categoric values. Proceedings of the Fisrt Pacific-Asia Conference on Knowledge Discovery and Data Mining. Singapore:World Scientific,1997,21-34
[152]Zadeh L A. Fuzzy sets. Inforation Control.1965,8:338
[153]Chen Ning, Chen,An, Zhou Longxiang. Fuzzy K-prototypes Algorithm for Clustering Mixed Numeric and Categorical Valued Data. Journal of Software.2001,23(8):1107-1119
[154]张敏,于剑.基于划分的模糊聚类算法.软件学报.2004,15(6)：859-868
[155]丁建立,陈增强.遗传算法与蚂蚁算法的融合.计算机研究与发展.2003,9(40)：1351-1353
[156]于剑,程乾生.关于FCM算法中的加权指数m的一点标记.电子学报,2003,31(3)：478-480
[157]J. D. Bailey. The behavior of adaptive systems which employ genetic and correlation algorithms. Dissertation Abstracts International. 1967,28(12),5016B
[158]R.B.Hollstien.Artifical genetic adaptation in computer control systems, Doctoral Dissertation,University of Michigan, No.71-23773.1971.
[159]J.H.Holland.Adaptation in natural and artificial systems.University of Michigan Press, Ann Arbor,1975
[160]K.A.DeJong.An analysis of a class of genetic adaptive systems.University of Michigan.1975, No.76-9381
[161]L.D.Davis.Genetic algorithms and simulated annealing.Morgan Kaufmann,Lots Alots,1987
[162]D.E.Goldberg, Genetic algorithms in search,optimization and machine learning, Reading, MA:Addison Wesley,1989
[163]李茂军,樊韶胜.单亲遗传算法在模式聚类中的应用..模式识别与人工智能.1999,12(1)：32-37
[164]杨国亮,王志良,任金霞.一种基于遗传操作的聚类算法.计算机应用.2003,23(12)：199-200
[165]张伟,廖晓峰,吴中福.一种基于遗传算法的聚类新方法.计算机科学.002,29(6)：114-116
[166]邹远强,李国徽,赵梓屹.基于遗传和蚁群算法融合的聚类新方法.科学技术与工程.2006,6(23)：4701-4703
[167]王丽薇,拱勇,拱家荣.遗传算法的收敛性研究.计算机学报.1996,19(10),794-797
[168]周传华,钱锋.改进量子遗传算法及其应用.计算机应用.2008,28(2)：286-288
[169]Mitchell M. An Introduction to Genetic Algorithms. MIT Press,1996
[170]王小平、曹立明.遗传算法：理论、应用与软件实现.西安交通大学出版社.2002,1-20
[171]L.Davis. Handbook of genetic algorithms. New York:Van Nostrand Reinhold,1991
[172]董云影,张运杰,畅春玲.改进的遗传模糊聚类算法.模糊系统与数学.2005,19(2)：128-131
[173]吴庆洪,张纪会,徐心和.具有变异特征的蚁群算法.计算机研究与发展.1999,36(10)：1242-1243
[174]周明,孙树栋.遗传算法原理及应用.北京国防工业出版社.1999,19-20
[175]黎钧琪,石国祯.遗传算法交叉率与变异率关系的研究.武汉理工大学学报(交通科学与工程版).2003,27(1)：97-99
[176]Scrinivas M, PaLanaik L M.Adlaplive probabilities of crossover and mulalion. IEEE Transactions on System, Man and Cybernetics. 1994,24(4):656-666
[177]Fogarty T C. Varying the probability of mutation in genetic algorithms. Proceedings of the 3th International Conference on Genetic Algorithms, Boc.1989:104-109
[178]J. Cervantes, C. R. Stephens. "Optimal" Mutation Rates for Genetic Search.Proceedings of the 8th annual conference on Genetic and evolutionary computation, Seattle, Washington.2006:1313-1320
[179]Grefenstette J J. Optimization of control parameters for genetic algorithms. IEEE Trans on Systems,Man and Cybernetics. 1986,16(1):122-128.
[180]恽为民,席裕庚.简单遗传算法的运行机理研究.第三届全国自动化学术讨论会.珠海,1993：36-43
[181]熊军,高敦堂,都思丹.变异率和种群数目自适应的遗传算法.东南大学学报(自然科学版).2007,34(4)：553-556
[182]邝航宇,金晶,苏勇.自适应遗传算法交叉变异算子的改进.计算机工程与应用.2006,42(12)：93-95
[183]袁慧梅.具有自适应交换率和变异率的遗传算法.首都师范大学学报(自然科学版).2000,21(3)：14-18
[184]汪家才,朱艺华.模糊K-Prototypes算法中的加权指数研究.计算机应用.2005,25(2)：349-350
[185]MVC的处理过程及优点.ttp://www.rsky.com.cn/bbs/dispbbs.asp?BoardID=22&ID=19395.2009.2
[186]http://blog.csdn.net/Struts2BBS/archive/2008/04/09/2267537.aspx.2009.2
[187]熊小茜.从零开始ASP铸就信息化的“王牌”.http://www.ciotimes.com/industry/car/c/CAR200802200955.html.2008.5
[188]成都王牌股份有限公司.http://www.wangpai.cn.2008.5
[189]刘英姿,何伟.基于不同视角的客户细分方法研究综述.商场现代化.2007,(1)：271-272
[2]余长慧,潘和平.商业智能及其核心技术.计算机应用研究.2002,19(9)：14-15
[3]路耀华,张劲松.建立整合性商务智能系统的思考.经济纵横.2002,(12)：17-18
[4]朱晓武.商务智能的理论和应用研究综述.计算机系统应用.2007,(1)：115-117
[5]孙林夫.国家高新技术研究发展计划先进制造业领域重点项目立项申请书-制造业商务智能技术及产品研发.西南交通大学,2007,1-10.
[6]吴俊.商业智能打造企业竞争优势的利器.科学决策.2004,(9)：28-31
[7]Bernard Liautaud.e-Business Intelligence:Turning Information into Knowledge into Profit.McGraw-Hill Trade,2002,3-39
[8]言君.商务智能大师为BI喝彩.电子商务.2003,(12)：14
[9]赵华,张立厚,宋静.用商务智能完善客户关系管理的信息流程.情报杂志.2005,(10)：61-64
[10]何应龙.一种中小企业创新产品评价与投资决策的新方法.科技进步与对策.2007,24(11)：88-90
[11]高翠芬.商业智能及其应用分析.现代商贸工业.2008,20(2)：138-139
[12]罗明,周叶.基于MAS的供应链绩效智能管理系统构架研究.中国市场.2007,(2)：74-76
[13]2010年亚太地区BI和BPM市场将达6.39亿美元.http://news.e-works.net.cn/category6/news20140:htm.2008.3.20
[14]]IDC：商务智能2010年规模达19亿英镑.http://cio.ccw.com.cn/data/ccw/sb/htm2007/20070207_240762.asp.2008.2.27
[15]商业智能(BI)技术已成为企业差异化因素.http://www.ciotimes.com.2008.3.20
[16]王刚.混合智能系统及其在商务智能中的应用研究.复旦大学博士论文.2008：143-144
[17]夏维力,许昌元.商务智能技术在企业战略决策中的支持作用及方法研究.软科学.2004,18(3)：16-17
[18]胡培元,赫岩.商务智能大师：伯纳德.利奥托德.电子商务.2003,(12)：20-25
[19]徐嵩泉.商务智能(BI)在制造业“开花结果”http://www.amteam.org/print.aspx?id=484106.2008.10.20
[20]W.F.Cody, J.T.Kreulen,V Krishna,W.S.Spangler.The integration of business intelligence and knowledge management.IBM Systems Journal.2002
[21]J. Foley, J. D. Russell.Mining Your Own Business.Information Week. 1998,3(19):12-14
[22]Kamel Rouibah, Samia Ould-ali.Puzzle; aconcept and prototype for linking business intelligence to business strategy. Journal of Strategic Information System 2002; 11:133-152
[23]Karl Vanden Bergh, Timo Elliott.Why Standardizing Business Intelligence Is Critical.Business Obbeds Whitepaper,2003:1-3
[24]王茁,顾洁.三位一体的商务智能(BI)—管理、技术与应用.电子工业出版社.2004：2-5
[25]徐绚,王德生.应用商业智能系统推进企业管理信息化.情报杂志.2004,(8)：79-81
[26]焦慧敏,汪林林.商务智能在现代企业中的应用与研究.计算机工程与设计.2006,27(13)：2503-2505
[27]胡慧.全球制造业价值链的重构与我国的现实选择.科技进步与对策.2004,(4)： 43-46
[28]孔令丞,郁义鸿.经济全球化与“中国制造”：一个基于价值链增值的视角的研究.科技导报.2005,(1)：58-60
[29]梅丽,张向阳.全球价值链视角下的制造业升级.商业经济.2005,(10)：17-19
[30]李培根院士揭晓2007制造业信息化十大热点.http://news.e-works.net.cn/category6/news26524.htm.2008.3.14
[31]赵慧娟.面向装备和汽车产业链的企业网络化协作平台开发及应用.四川省制造业信息化研究院.2005：32-33
[32]ITPUB调查：中国BI技术与应用现状.www.it168.com.2008.12.31
[33]Edward R.Tufte.Envisioning Information.Graphics Press.1990
[34]Codd E. F, Codd S. B, Salley C. T.Beyond decision support.Computer World.1993,27(30):87-89
[35]Ken Orr. Data Qualitv and systems theory. Communications of the ACM. 1998,41(2):66-71
[36]Burges C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery.1998,2(2):127-167
[37]Nello Cristianini, John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press.2000
[38]Cortes C,Vapnik V.Support-vector networks.Machine Learning. 1995,20 (3):273-297
[39]Kahaner Larry. Competitive Intelligence:How to Gather,Analyze & Use Information to Move Your Business to the Top. Simon & Schuster,Inc. 1996
[40]Berry M,Linoff G. Data Mining Techniques:For Marketing,Sales,and Customer Support. John Wiley & Sons,Inc.1997
[41]Fayyad U.M, Piatetsky-Shapiro Smyth G.Pand Uthurusam R. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press,1996
[42]G Houhen,K Lenie,K Vanhoof. A knowledge-based SWOT-analysis system as an instrument for strategic planning in small and medium sized enterprises. Decision Support Systems.1999,26:125-135
[43]Jack G, Olin.Knowledge management across multi-tier enterprises:The promise of intelligent software in the auto industry. European Management Journal.1999,17(4):335-347
[44]Bill Inmon.Corporate Information Factory. John Wiley & Sons.2000
[45]Bill Inmon.Data Warehousing for E-Business. John Wiley & Sons.2001
[46]Albert A Angehrn,Michael Gihhert,Katerina Nicolopoulou. Management focus guest editors'introdction:Understanding learning networks. European Management Journal.2003,21(5):559-562
[47]Cody W. F., Kreulen J. T. The integration of business intelligence and knowledge management. IBM Systems Journal,2002,4(14):697-714
[48]Spil T. A. M. Business intelligence in healthcare organizations. Proceedings of the 35th Annual Hawaii International Conference on System Sciences. Waikoloa, Hawaii.2002:101-105
[49]Claire A. A stakeholder model of business intelligence. Proceedings of the 37th Annual Hawaii International Conferenc on System Sciences, Waikoloa, Hawaii.2004:223-23
[50]http://www.businessobjects.com/news/presscd/default.asp.2008.7.27
[51]张云涛,龚玲.商业智能设计、部署与实现.电子工业出版社,2004： 7-8
[52]周华云,邓燕.制造业ERP与BI的融合势在必行.http://www. amteam. org/print. aspx?id=613477.2008.10
[53]王汝林.商务智能驱动丰田扩张136亿的启示.http://www.gdpx.com.cn/news/2006112091751443502.2008.7
[54]度关注汽车制造业信息化.http://www.topoint.com.cn/html/e/2008/04/207620.html.2008.1.19
[55]SAP管理软件全面提升神龙汽车企业竞争力.http://www.enet.com.cn/article/2006/0815/A20060815165453.shtml.2008.3.20
[56]Business Objects公司商务智能系列案例之五--“驾驶舱”指引汽车飞奔.中国计算机用户.2007,17(3)：66-66
[57]夏国恩.基于商务智能的客户流失预测模型与算法研究.西南交通大学博士论文.2007：9-17
[58]郭伟刚,鞠时光.制造企业面向销售的通用智能商务系统研究.佛山科技学院学报(自然科学版),2005,23(4)：40-43
[59]李瑞臻,朱云龙.汽车行业营销OLAP系统设计与开发[J].信息与控制.2006,35(4)：522-531
[60]朱艺华.基于双层数据仓库的MIS设计研究.计算机工程与应用.2002；38(12)：198-203
[61]BI未来发展将出现三大趋势.http://soft.ccw.com.cn/comment/htm2007/20070608_269727.shtml.2007.12.7
[62]2007-2008中国商业智能市场发展报告.http://www.chinabi.net/Article/binews/200801/927.html.2008.5.11
[63]Alexander L. Factor.应用服务链供应商(ASP)解决方案.孙延明译.电子工业出版社,2003：240-248
[64]韩敏.基于ASP的汽车产业链协作模式、技术、平台研究.西南交通大学博士论文.2006：20-28
[65]赵慧娟,王淑营,孙林夫.面向中小企业信息化建设的ASP服务平台.计算机集成制造系统.2004,10(11)：1441-1443
[66]汽车行业的全球化,ANX意欲登陆中国.http://finance.163. com/economy2003/editor_2003/040609/040609_209189.html. 2008.1.13
[67]孙林夫.基于ASP的制造业产业链协作平台.四川省制造业信息化研究院.2006：11-13
[68]Christopher J. Matheus. System for Kowledge Discovery in Databases. IEEE Transactions on Knowledge and Data Engineering.1993,5 (6):903-913
[69]袁玉波,杨传胜,黄廷祝.数据挖掘与最优化技术及其应用.科学出版社,2007：5-6
[70]李雄飞,李军.数据挖掘与知识发现.高等教育出版社.2003,118-130
[71]中华机械网.汽车行业高利润率时代一去不返.http://news. machine365. com/arts/050917/1/60837_2. html.2008.10.25
[72]严隽琪,马登哲,范菲雅.面向中小企业信息化的ASP平台研究与开发.计算机集成制造系统.2005,11(2)：178-182
[73]赵岩.协同售后服务系统三包费用管理的实现及关联规则挖掘研究.西南交通大学硕士论文.2006：50-52
[74]史鹏翔.面向产业链协作的汽车销售管理系统的实现与研究.西南交通大学硕士论文.2006：15-19
[75]BO商务智能平台.http://www. china. businessobjects. com/products/platform/default. asp20 08.10.25
[76]Microsoft商务智能解决方案.Microsoft商务智能解决方案白皮书.2008：7-15
[77]IBM商务智能解决方案.ttp://www.00.ibm.com/cn/downloadfiles/products/servers/intelligence.pdf. 2008:3-4
[78]陈永杰.SAP商务智能完全解决方案.机械工业出版社,2008：8-15
[79]用友公司简介. http://it.21cn.com/itnews/newsgn/2003-05-25/1053458.htm
[80]何进伟.用友推出ERP-U8商务智能解决方案.ttp://industry.ccidnet.com/art/31/20031015/67078_1.html.2009.1
[81]毛井井.BO战略结盟用友.ERP与商业智能融合“破茧”.ttp://www.enet.com.cn/article/2006/0913/A20060913193957.shtml.2009.1
[82]2006年商务智能应用状况报告.http://www.chinabi.net,2007.5
[83]罗来鹏,土少青.试论商务智能在企业决策中的应用.企业管理信息化.2005,3(19)：18-19
[84]董仲元等.中小企业服务网络模式和使能技术的应用研究.“面向新世纪的应用工程发展研究”研究报告之五,西安理工大学,2001.4
[85]FAN Li-lin. Research on the Mode of over Enterprises Application Based on Service Oriented Architecture.2007 International Symposium on Biometrics and Security Technologies, Bahria University Bahria University, Pakistan,2007
[86]范黎林,李亚.基于SOA的区域协同电子商务平台设计与研究,河北师大学报(自然科学版),2006,30(2)：154-157
[87]ZOU Shu-rong.The Application of SOA Technology on Automobile Supply Chain Information Integrated.TheInternational Conference 2007 on Information Computing and Automation,2007
[88]北京综艺达软件技术有限公司.基于N层架构的优势分析解决方案.ttp://publish.it168.com,2004.10
[89]马学良.基于网格计算的商务智能研究.浙江工商大学硕士学位论文.2006：40-41
[90]李绍文.面向中小型汽车企业的服务管理系统设计与开发.西南交通大学硕士学位论文.2006：41-42
[91]Atre, Shaku. Business Intelligence Success Is Never An Accident. Computerworld. September 15,2003
[92]彭木根.数据仓库技术与实现.电子工业出版社,2002：5-31
[93]陈京民.数据仓库与数据挖掘技术.电子工业出版社,2002,8-9
[94]沈兆阳.数据仓库与Analysis Services.清华大学出版社,2001,9-10
[95]夏火松.数据仓库与数据挖掘技术.科学出版社,2004,3-4
[96]Gupta, Vivek. An Introduction To Data Warehousing. System Services Corporation White Paper. August 1997
[97]Jeffrey Richter. Applied Microsoft.NET Framework programming.李建忠.清华大学出版社,2003
[98]Bill Jones, Jr. SQL Server Reporting Services-Lessons Learned In Development and Deployment. http://www.eggheadcafe.com/articles/20040823.asp. 2007.5
[99]李显生、鹿应荣、周茹波.汽车现代诊断技术的发展趋势.公路交通科技.2001,18(1)：41-44
[100]李美华.神经网络专家系统在汽车故障诊断中的应用研究.东北林业大学硕士论文.2006：29-45
[101]张晓丹.汽车发动机故障诊断中不确定性问题的贝叶斯网络解法.东北大学博士论文.2005：54-76
[102]Avner S. Extraction of Comprehensive Symbolic Rules from a Multi-layer Perceptron. Engineering Applications of Artificial Intelligence. 1996,9(2):137-143
[103]李剑川.贝叶斯网络故障诊断与维修决策方法及应用研究.国防科学技术大学博士论文.2002：20-30
[104]吴今培.智能故障诊断技术的发展和展望.振动、测试与诊断.1999,19(2)：79-86
[105]孙颖楷,张邦礼.基于粗糙集理论的人工神经网络故障诊断系统.重庆大学学报.2000,20(6)：53-56
[106]高梅梅,吴智铭.模糊推理Petri网及其在故障诊断中的应用.自动化学报,2000,26(5)：677-680
[107]丁彩红,黄文虎.应用petri网改进基于故障树的诊断方法.振动工程学报.2000,13(2)：277-282
[108]吴今培,肖健华.智能故障诊断与专家系统.科学出版社,1997,7-98
[109]吴泉源,刘江宁.人工智能与专家系统.国防科技大学出版社,1995,22-102
[110]蔡自兴,徐光佑.人工智能及其应用.清华大学出版社,1996,36-132
[111]Sharkey A J C, Sharkey N E, Chandroth G O. Diverse Neural Netsolutions to Fault Diagnosis Problem. Neural Computing & Applications. 1996, (4):218-222
[112]Staszewski W J, Worden K. Classification of faults in gearboxes:preprocessing algorithms and neural networks. Neural Computing & Applications. 1997,(5):160-183
[113]Lu Yi, Chen Tie, Brennan T. Hamilton. A Fuzzy Diagnostic Model and Its Application in Automotive Engineering Diagnosis. Applied Intelligence. 1998, (9):231-243
[114]郝丽娜、徐心和.粗糙集理论在故障诊断规则获取中的应用.信息与控制.2001,30(7)：586-590
[115]R.Agrawal, T. Imielinski, and A. Swami.Database mining:A performance perspective.IEEE Trans Knowledge and Data Engineering.1993,5(6):34-38
[116]R. Agrawal, J. Shafer.Parallel mining of association rules.IEEE Trans on Konowledge and Data Engineering.1996,8(6):962-969
[117]R.Srikant, R.Agrawal.Mining Quantiative association rules in large relational tables.Proceedings of ACM SIGMOD International Conference on Management of Data, Montreal, Canadal.996,1-12
[118]R.Srikant, R.Agrawal.Mining generalized association rules.Future Generation Computer Systems.1997,13:161-180
[119]R.Agrawal and R. Srikant. Fast algorithms for mining association rules. Proceeding of the 20th International Conference on Very Large Database, Santiago,Chile.1994,487-499
[120]J.S.Park, M.S.Chen, and P.S.Yu. An effective hash-based algorithm for mining association rules.Proceeding of International Conference on Very Large Database.1995,175-186
[121]A.Savasere, E.Omiecinski, and S.Navathe. An efficient algorithm for mining association rules in large databases. Proceeding of International Conference on Very Large Database.1995,432-443
[122]H.Toivonen. Sampling large databases for association rules. Proceeding of International Conference on Very Large Database.1996,134-145
[123]S.Brin, R.Motwani, J.D.Unman, and S.Tsur. Dynamic itemset counting and implication rules for market basket analysis. Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, Tucson, AZ.1997: 255-264
[124]J.Han,J.Pei and Y.Yin. Mining frequent patterns without candidate generation of Data. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, TX.2000,:1-10
[125]Jiawei Han, Jianpei. Mining frequent patterns without candidate generation:A frequenh pattern tree approach. Data Mining and Knowledge Discovery,2004, 8(1):53-87
[126]廖仁全,工利华,邱江涛.一种基于FP-tree的频繁项集增量更新算法.计算机工程与应用.2007,43(4)：176-178
[127]李忠哗,任春龙,何丕廉.一种基于FP-树的最大频繁模式增量更新挖掘算法.计算机应用与软件.2007,24(5)：47-49
[128]王黎明,赵辉.基于FP树的全局最大频繁项集挖掘算法.计算机研究与发展.2007,44(3)：445-451
[129]毛国君.数据挖掘技术与关联规则挖掘算法研究.北京工业大学博士论文.2003；63-64
[130]陈立潮,张建华,刘玉树.提高频繁项集挖掘算法效率的方法研究.计算机工程与应用.2002,(10)：34-35
[131]Huang Liusheng, Chen Huaping. A fast algorithm for mining association rules. Journal of Computer Science and Technology.2000,15(6):619-624
[132]曾万聘,周绪波,戴勃.关联规则挖掘的矩阵算法.计算机工程.2006,32(2)：45-47
[133]牛小飞,石冰,卢军.挖掘关联规则的高效ABM算法,计算机工程.2004,30(11)：118-120
[134]胡慧蓉,王周敬.一种基于关系矩阵的关联规则快速挖掘算法.计算机应用.2005,25(7)：1577-1579
[135]Feng Yu Cai, Feng Jian Lin. Incremental updating algorithm for mining association rules. Journal of Software.1998,9(4):301-306
[136]David W Cheung, Jiawei Han. Maintenance of Discovered Association Rules in Large Database:an Incremental Updating Technique. Proceeding of 12th International Conference on Data Engineering.1996:106-114
[137]Chen Jing-song, SHi Xiao-ying. An Incremental Updating Algorithm for Mining Association Rules. Computer Engineering.2002,28(7):106-109
[138]Zhu Yu-quan, Sun Zhi-hui, Ji Xiao-jun. Incremental Updating Algorithm Based on Frequent Pattern Tree for Mining Association Rule. Chinese Journal of Computers.2003,26(1):91-96
[139]Frequent Itemset Mining Dataset Repository, http://fimi.cs.helsinki.fi.2008.4
[140]Darrll Rigby, Frederick F. Reichheld. Avoid the Four Perils of CRM. Harvard Business Review.2002,(1):101-109
[141]张国政.客户关系管理中基于数据挖掘的客户细分研究.商业研究.2006,(13)：153-155
[142]Anderson D M. Agile product development for mass customization. Beijing; China Mechanical Industry Press,1999,10-16
[143]M. R. Anderberg. Cluster Analysis for Applications. Academic Press, 1973:10-12
[144]J. A. Saunders.Cluster Analysis for Market Segmentation. European Journal of Marketing.1980,14(7):433
[145]D. Hand,H. Mannia,P. Smyth. Principles of Data Mining. MIT Press,2001: 23-28
[146]Yuan Y.Serveral Optimal modes for data mining. Journal of University of Electronic Science and Technology.2004,33:1-5
[147]Ralambondrainy H.A conceptual version of the k-means algorithm. Pattern Recognition Letter.1995,16:1147-1157
[148]Biawae G, Weinberg J, Li C ITERATE:A conceptual clustering method for knowledge discovery in database. Artificial Intelligence in the Petroleum Industry. Braunschweig B and Day Reds,1995,111-139
[149]Cheesman P, Stutz J. Bayesian classification (AutoClass):theory and results. Advances in knowledge discovery and data mining. AAAI Press,1995
[150]Huang Zhexue. A Fuzzy K-modes Algorithm for Clustering Categorical Data. IEEE Transaction on Fuzzy Systems.1999,.7(4):466-452
[151]Huang Zhexue. Clustering large data sets with mixed numeric and categoric values. Proceedings of the Fisrt Pacific-Asia Conference on Knowledge Discovery and Data Mining. Singapore:World Scientific,1997,21-34
[152]Zadeh L A. Fuzzy sets. Inforation Control.1965,8:338
[153]Chen Ning, Chen,An, Zhou Longxiang. Fuzzy K-prototypes Algorithm for Clustering Mixed Numeric and Categorical Valued Data. Journal of Software.2001,23(8):1107-1119
[154]张敏,于剑.基于划分的模糊聚类算法.软件学报.2004,15(6)：859-868
[155]丁建立,陈增强.遗传算法与蚂蚁算法的融合.计算机研究与发展.2003,9(40)：1351-1353
[156]于剑,程乾生.关于FCM算法中的加权指数m的一点标记.电子学报,2003,31(3)：478-480
[157]J. D. Bailey. The behavior of adaptive systems which employ genetic and correlation algorithms. Dissertation Abstracts International. 1967,28(12),5016B
[158]R.B.Hollstien.Artifical genetic adaptation in computer control systems, Doctoral Dissertation,University of Michigan, No.71-23773.1971.
[159]J.H.Holland.Adaptation in natural and artificial systems.University of Michigan Press, Ann Arbor,1975
[160]K.A.DeJong.An analysis of a class of genetic adaptive systems.University of Michigan.1975, No.76-9381
[161]L.D.Davis.Genetic algorithms and simulated annealing.Morgan Kaufmann,Lots Alots,1987
[162]D.E.Goldberg, Genetic algorithms in search,optimization and machine learning, Reading, MA:Addison Wesley,1989
[163]李茂军,樊韶胜.单亲遗传算法在模式聚类中的应用..模式识别与人工智能.1999,12(1)：32-37
[164]杨国亮,王志良,任金霞.一种基于遗传操作的聚类算法.计算机应用.2003,23(12)：199-200
[165]张伟,廖晓峰,吴中福.一种基于遗传算法的聚类新方法.计算机科学.002,29(6)：114-116
[166]邹远强,李国徽,赵梓屹.基于遗传和蚁群算法融合的聚类新方法.科学技术与工程.2006,6(23)：4701-4703
[167]王丽薇,拱勇,拱家荣.遗传算法的收敛性研究.计算机学报.1996,19(10),794-797
[168]周传华,钱锋.改进量子遗传算法及其应用.计算机应用.2008,28(2)：286-288
[169]Mitchell M. An Introduction to Genetic Algorithms. MIT Press,1996
[170]王小平、曹立明.遗传算法：理论、应用与软件实现.西安交通大学出版社.2002,1-20
[171]L.Davis. Handbook of genetic algorithms. New York:Van Nostrand Reinhold,1991
[172]董云影,张运杰,畅春玲.改进的遗传模糊聚类算法.模糊系统与数学.2005,19(2)：128-131
[173]吴庆洪,张纪会,徐心和.具有变异特征的蚁群算法.计算机研究与发展.1999,36(10)：1242-1243
[174]周明,孙树栋.遗传算法原理及应用.北京国防工业出版社.1999,19-20
[175]黎钧琪,石国祯.遗传算法交叉率与变异率关系的研究.武汉理工大学学报(交通科学与工程版).2003,27(1)：97-99
[176]Scrinivas M, PaLanaik L M.Adlaplive probabilities of crossover and mulalion. IEEE Transactions on System, Man and Cybernetics. 1994,24(4):656-666
[177]Fogarty T C. Varying the probability of mutation in genetic algorithms. Proceedings of the 3th International Conference on Genetic Algorithms, Boc.1989:104-109
[178]J. Cervantes, C. R. Stephens. "Optimal" Mutation Rates for Genetic Search.Proceedings of the 8th annual conference on Genetic and evolutionary computation, Seattle, Washington.2006:1313-1320
[179]Grefenstette J J. Optimization of control parameters for genetic algorithms. IEEE Trans on Systems,Man and Cybernetics. 1986,16(1):122-128.
[180]恽为民,席裕庚.简单遗传算法的运行机理研究.第三届全国自动化学术讨论会.珠海,1993：36-43
[181]熊军,高敦堂,都思丹.变异率和种群数目自适应的遗传算法.东南大学学报(自然科学版).2007,34(4)：553-556
[182]邝航宇,金晶,苏勇.自适应遗传算法交叉变异算子的改进.计算机工程与应用.2006,42(12)：93-95
[183]袁慧梅.具有自适应交换率和变异率的遗传算法.首都师范大学学报(自然科学版).2000,21(3)：14-18
[184]汪家才,朱艺华.模糊K-Prototypes算法中的加权指数研究.计算机应用.2005,25(2)：349-350
[185]MVC的处理过程及优点.ttp://www.rsky.com.cn/bbs/dispbbs.asp?BoardID=22&ID=19395.2009.2
[186]http://blog.csdn.net/Struts2BBS/archive/2008/04/09/2267537.aspx.2009.2
[187]熊小茜.从零开始ASP铸就信息化的“王牌”.http://www.ciotimes.com/industry/car/c/CAR200802200955.html.2008.5
[188]成都王牌股份有限公司.http://www.wangpai.cn.2008.5
[189]刘英姿,何伟.基于不同视角的客户细分方法研究综述.商场现代化.2007,(1)：271-272