银行网点排队分析系统研究
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摘要
随着现代社会高科技的发展,高速度、快节奏已经越来越成为人们必需的生活方式。人们的时间也就愈发显得宝贵,对排队的等待也就愈发无法忍受。于是,出行前先通过网络或手机查询一下哪个银行网点排队的人少,这是信息化社会人们解决这类问题的较好方案。
系统主要包括客户端、后台和排队模拟机三部分。系统首先通过排队模拟机,模拟银行中排队机实时发送的数据,并将数据送到后台服务器。在服务器端,系统通过联机分析和数据挖掘,对数据进行分析处理。客户可登陆我们的网站或手机查询,在地图上查询自己附近的网点,以及当前各网点中正在办理业务的客户等待情况。同时,客户还可以通过输入自己将要到达的时间点,系统将会根据历史数据以及当天已有数据做出一个合理的预测。同时,系统也能为银行内部组织决策者带来决策的宝贵参考。系统通过对排队人数的统计,将各网点在各时间段的客户人数的情况,以图表的方式显示给银行内的管理人员。并且,通过数据挖掘,对各网点客流量未来的发展趋势做出预测。决策者可参考预测结果,对网点进行更加合理的布局。
此系统就是在科技领域实现以人为本的具体体现。首先,系统能满足银行客户实时方便地了解银行业务情况以便安排去哪家银行办理业务。其次,银行内部也能及时了解各网点的各种历史业务情况和未来的预测,以确定更加合理的网点设置与人员配备。
With the high-tech development of modern society, high-speed, fast-paced lifestyles have increasingly become necessary. Time become more valuable, people will become more unbearable for the queue of waiting. Thus,Before leaving through the internet or mobile phone inquiries about which bank outlets queuing is less, this is a better solution to such problems of the information society.
System includes client, background and queuing simulator. First system simulates a queue machine in the bank outlet to send queuing data to the server background via a queue simulator. At server end, the system analyzes and process data via online analytical processing and data mining. A customer can log on our web server or use mobile phone, queue the outlets nearby on the electronic map to learn how many people are waiting in each outlet. In the meantime, a customer could input the time he would arrive. Then the system will give a reasonable forecast for future waiting people by history data and current data. At the same time, system can supply decision-making references for managers of bank. The system shows the number of customers by each outlet and each time segment in the form of chart and table to managers of bank according to the statistics of queuing people. The system can also forecast the trend of customer flux of each outlet by data mining. The decision-maker could refer the forecasting result to arrange bank outlets more reasonable.
In the field of science and technology system is a concrete expression to the people-centered. First, system can satisfy the bank customer to inquiry the bank’s real-time business in order to arrange to which bank is for business. Secondly, the bank can understand the history of the outlet’s business and future forecast, and confirm arranging bank outlets and Staffing more reasonable.
系统主要包括客户端、后台和排队模拟机三部分。系统首先通过排队模拟机,模拟银行中排队机实时发送的数据,并将数据送到后台服务器。在服务器端,系统通过联机分析和数据挖掘,对数据进行分析处理。客户可登陆我们的网站或手机查询,在地图上查询自己附近的网点,以及当前各网点中正在办理业务的客户等待情况。同时,客户还可以通过输入自己将要到达的时间点,系统将会根据历史数据以及当天已有数据做出一个合理的预测。同时,系统也能为银行内部组织决策者带来决策的宝贵参考。系统通过对排队人数的统计,将各网点在各时间段的客户人数的情况,以图表的方式显示给银行内的管理人员。并且,通过数据挖掘,对各网点客流量未来的发展趋势做出预测。决策者可参考预测结果,对网点进行更加合理的布局。
此系统就是在科技领域实现以人为本的具体体现。首先,系统能满足银行客户实时方便地了解银行业务情况以便安排去哪家银行办理业务。其次,银行内部也能及时了解各网点的各种历史业务情况和未来的预测,以确定更加合理的网点设置与人员配备。
With the high-tech development of modern society, high-speed, fast-paced lifestyles have increasingly become necessary. Time become more valuable, people will become more unbearable for the queue of waiting. Thus,Before leaving through the internet or mobile phone inquiries about which bank outlets queuing is less, this is a better solution to such problems of the information society.
System includes client, background and queuing simulator. First system simulates a queue machine in the bank outlet to send queuing data to the server background via a queue simulator. At server end, the system analyzes and process data via online analytical processing and data mining. A customer can log on our web server or use mobile phone, queue the outlets nearby on the electronic map to learn how many people are waiting in each outlet. In the meantime, a customer could input the time he would arrive. Then the system will give a reasonable forecast for future waiting people by history data and current data. At the same time, system can supply decision-making references for managers of bank. The system shows the number of customers by each outlet and each time segment in the form of chart and table to managers of bank according to the statistics of queuing people. The system can also forecast the trend of customer flux of each outlet by data mining. The decision-maker could refer the forecasting result to arrange bank outlets more reasonable.
In the field of science and technology system is a concrete expression to the people-centered. First, system can satisfy the bank customer to inquiry the bank’s real-time business in order to arrange to which bank is for business. Secondly, the bank can understand the history of the outlet’s business and future forecast, and confirm arranging bank outlets and Staffing more reasonable.
引文
[1]张圣亮,靳邦喜.商业银行缓解客户排队等待策略探讨.价值工程,2004,5: 95~96
[2]朱枫.扁平化管理与国有商业银行组织重组.中国金融, 2001,10: 42~43
[3]庄伟雄.讲求时效与追求实效—略谈因应网点扁平化管理的科技工作.华南金融电脑, 2005, 6: 18~19
[4]孟建松,李振宇.如何解决在银行排队等待问题.中外企业文化, 2003, 2: 33~34
[5]深圳市来邦电子有限公司.银行排队机在金融管理中的应用.中国安防产品信息, 2003, 4:34~36
[6]朱如龙.银行智能化排队管理系统的设计.常州信息职业技术学院学报, 2004, 6: 71~73
[7]赵军宽,熊晓放.医院门诊排队系统的“成本一效益”模型研究.中国卫生质量管理, 2005, 12: 14~17
[8]喻钢,周定康.联机分析处理(OLAP)技术的研究.计算机应用, 2001,11:80~84
[9]姚渝,向伟.联机分析处理(OLAP).中国城市金融. 2001, 5: 52~54
[10] William C.Amo. SQL Server OLAP开发指南.北京:电子工业出版社,2000:20~45
[11]林杰斌,刘明德,陈湘.数据挖掘与OLAP理论与实务,北京:清华大学出版社, 2003:11~23
[12]宋利.知识发现与数据挖掘.广西师范大学学报,1998, 14(5):147~152
[13]朱建平,范霄文,张志强.数据挖掘的技术与商业定义及其研究对象.统计教育,2004, 1: 7~10
[14] Mehmed Kantardzic. Data Mining Concepts,Models, Methods, and Algorithms. IEEE Press: 2002. 2~3
[15] J.Han and M.Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Press: 2000. 8~12
[16]孙炜,万筱宁.基于决策树的农产品供应商竞标能力的数据挖掘分析.中国农学通报, 2004, 2: 47~50
[17]陈富赞,寇继凇,王以直.数据挖掘方法的研究.系统工程与电子技术,2000,8: 78~81
[18] D.P.Ballou and G.K.Tayi.Enhancing data quality in data warehouse environments. Communication of ACM, 1999, 42:73～78
[19] H.LiuH.Motoda. Feature Selection for Konwledge Discovery and Data Mining, Kluwer Academic Publishers: 1998
[20] Usama m F. Data mingand knowledge discovery. Making sense Out of data IEEE Expert, 1996,10: 20~25
[21]陈京民.数据仓库与数据挖掘技术.北京:电子工业出版社. 2002. 104~110
[22]张云涛,龚玲.数据挖掘原理与技术.北京:电子工业出版社. 2004. 1~23
[23]颜雪松,李宏,王欣.用SQLServer2000构建数据挖掘解决方案. 2004:1~45
[24] W.H.Inmon. The Data Warehouse Environment: Quantifying Cost Justification and Return on Investment,2000
[25]汤宇松.数据挖掘系统设计.系统工程理论与实践,2000,9: 56~63
[26] M.Ankerst, C.Elsen, M.Ester. Visualassification: An interactive approach to decision tree construction. Knowledge Discovery and Data Mining, 1999: 392-396
[27] A.Arning, R.Agrawal, and P.Raghavan. A linear method for deviation detection in large databases. Data Mining and Knowledge Discovery, 1996: 164-199
[28] Sarawagi.S. Informative summarization of differences in multidimensional aggregates. Data Mining and Knowledge Discovery, 2001,5(4).255~276
[29] David Hand, Heikki Mannila,and Padhraic Smyth. Principles of Data Mining.北京:机械工业出版社, 2002: 179~192
[30]房祥飞,刘希玉.决策树在数据挖掘中的新进展和发展前景.信息技术与信息化, 2006, 3: 139
[31]薛薇.数据挖掘中的决策树技术及其应用.统计与信息论坛, 2002, 3:4~10
[32]朱绍文.决策树采掘技术及发展趋势.计算机工程, 2000, 10:1~3
[33]黄晓芳.数据挖掘中决策树算法及其应用.网络信息技术, 2005, 2:35
[34]刘钊,蒋良孝.基于神经网络的数据挖掘研究.计算机工程与应用,2004, 3:172~190
[35]巩慧,赵文吉. GIS的发展方向. WORLD GEOLOGY, 1999,3:60~63
[36]黄杏元,汤勤.地理信息系统概论.北京:高等教育出版社, 1990:1~20
[37] Maguire DJ. Anoverview and definitionof GIS. geographic information system. London: Longman Inc, 1991
[38]朱江、宋关福. SuperMap IS-互联网GIS开发平台.北京超图地理信息技术有限公司, 2003
[39] Len B, Paul C, and Rick K.软件构架实践.北京:清华大学出版社,2004. 80~123
[40]王国荣. Active Server Pages&数据库.北京:人民邮电出版社, 1999:1~10
[41] Jiohn Pa Pa. Data Source Controls in ASP.NET 2.0. MSDN Magazine, 2005,20:2~27
[42]赵文.排队论(随机服务系统理论)概述.天津商学院学报,1995,2: 21~29
[43]蔡忠林,刘援朝.基于C/S和B/S的电力调度信息系统.西北水力发电,2005,2:4~7
[44] Tian Naishuo, Zhang Daqing, Cao Jinhua. M/G/1 Queue with controllable vacations and optimization of vacation policy. Acta Mathematicae Applicatae Sinica, 1991, 7(4):363~373
[45]王庆瑞.数据结构教程.北京:希望电子出版社, 2002. 30~97
[46]萨师煊.数据库系统概论.北京:高等教育出版社, 2000.34~65
[2]朱枫.扁平化管理与国有商业银行组织重组.中国金融, 2001,10: 42~43
[3]庄伟雄.讲求时效与追求实效—略谈因应网点扁平化管理的科技工作.华南金融电脑, 2005, 6: 18~19
[4]孟建松,李振宇.如何解决在银行排队等待问题.中外企业文化, 2003, 2: 33~34
[5]深圳市来邦电子有限公司.银行排队机在金融管理中的应用.中国安防产品信息, 2003, 4:34~36
[6]朱如龙.银行智能化排队管理系统的设计.常州信息职业技术学院学报, 2004, 6: 71~73
[7]赵军宽,熊晓放.医院门诊排队系统的“成本一效益”模型研究.中国卫生质量管理, 2005, 12: 14~17
[8]喻钢,周定康.联机分析处理(OLAP)技术的研究.计算机应用, 2001,11:80~84
[9]姚渝,向伟.联机分析处理(OLAP).中国城市金融. 2001, 5: 52~54
[10] William C.Amo. SQL Server OLAP开发指南.北京:电子工业出版社,2000:20~45
[11]林杰斌,刘明德,陈湘.数据挖掘与OLAP理论与实务,北京:清华大学出版社, 2003:11~23
[12]宋利.知识发现与数据挖掘.广西师范大学学报,1998, 14(5):147~152
[13]朱建平,范霄文,张志强.数据挖掘的技术与商业定义及其研究对象.统计教育,2004, 1: 7~10
[14] Mehmed Kantardzic. Data Mining Concepts,Models, Methods, and Algorithms. IEEE Press: 2002. 2~3
[15] J.Han and M.Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Press: 2000. 8~12
[16]孙炜,万筱宁.基于决策树的农产品供应商竞标能力的数据挖掘分析.中国农学通报, 2004, 2: 47~50
[17]陈富赞,寇继凇,王以直.数据挖掘方法的研究.系统工程与电子技术,2000,8: 78~81
[18] D.P.Ballou and G.K.Tayi.Enhancing data quality in data warehouse environments. Communication of ACM, 1999, 42:73～78
[19] H.LiuH.Motoda. Feature Selection for Konwledge Discovery and Data Mining, Kluwer Academic Publishers: 1998
[20] Usama m F. Data mingand knowledge discovery. Making sense Out of data IEEE Expert, 1996,10: 20~25
[21]陈京民.数据仓库与数据挖掘技术.北京:电子工业出版社. 2002. 104~110
[22]张云涛,龚玲.数据挖掘原理与技术.北京:电子工业出版社. 2004. 1~23
[23]颜雪松,李宏,王欣.用SQLServer2000构建数据挖掘解决方案. 2004:1~45
[24] W.H.Inmon. The Data Warehouse Environment: Quantifying Cost Justification and Return on Investment,2000
[25]汤宇松.数据挖掘系统设计.系统工程理论与实践,2000,9: 56~63
[26] M.Ankerst, C.Elsen, M.Ester. Visualassification: An interactive approach to decision tree construction. Knowledge Discovery and Data Mining, 1999: 392-396
[27] A.Arning, R.Agrawal, and P.Raghavan. A linear method for deviation detection in large databases. Data Mining and Knowledge Discovery, 1996: 164-199
[28] Sarawagi.S. Informative summarization of differences in multidimensional aggregates. Data Mining and Knowledge Discovery, 2001,5(4).255~276
[29] David Hand, Heikki Mannila,and Padhraic Smyth. Principles of Data Mining.北京:机械工业出版社, 2002: 179~192
[30]房祥飞,刘希玉.决策树在数据挖掘中的新进展和发展前景.信息技术与信息化, 2006, 3: 139
[31]薛薇.数据挖掘中的决策树技术及其应用.统计与信息论坛, 2002, 3:4~10
[32]朱绍文.决策树采掘技术及发展趋势.计算机工程, 2000, 10:1~3
[33]黄晓芳.数据挖掘中决策树算法及其应用.网络信息技术, 2005, 2:35
[34]刘钊,蒋良孝.基于神经网络的数据挖掘研究.计算机工程与应用,2004, 3:172~190
[35]巩慧,赵文吉. GIS的发展方向. WORLD GEOLOGY, 1999,3:60~63
[36]黄杏元,汤勤.地理信息系统概论.北京:高等教育出版社, 1990:1~20
[37] Maguire DJ. Anoverview and definitionof GIS. geographic information system. London: Longman Inc, 1991
[38]朱江、宋关福. SuperMap IS-互联网GIS开发平台.北京超图地理信息技术有限公司, 2003
[39] Len B, Paul C, and Rick K.软件构架实践.北京:清华大学出版社,2004. 80~123
[40]王国荣. Active Server Pages&数据库.北京:人民邮电出版社, 1999:1~10
[41] Jiohn Pa Pa. Data Source Controls in ASP.NET 2.0. MSDN Magazine, 2005,20:2~27
[42]赵文.排队论(随机服务系统理论)概述.天津商学院学报,1995,2: 21~29
[43]蔡忠林,刘援朝.基于C/S和B/S的电力调度信息系统.西北水力发电,2005,2:4~7
[44] Tian Naishuo, Zhang Daqing, Cao Jinhua. M/G/1 Queue with controllable vacations and optimization of vacation policy. Acta Mathematicae Applicatae Sinica, 1991, 7(4):363~373
[45]王庆瑞.数据结构教程.北京:希望电子出版社, 2002. 30~97
[46]萨师煊.数据库系统概论.北京:高等教育出版社, 2000.34~65
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