云师大网络故障用户自助排查系统设计与实现
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摘要
高校校园网络随着计算机网络技术的高速发展,已逐步渗透到高校人群学习、工作和生活的方方面面,成为必不可少的交流和资源共享平台。与此同时,高校校园网络管理的重要性也不断加强。随着网络用户不断增多,网络规模日渐扩大,网络结构日趋复杂,网络应用逐步多元化,人们对网络管理的要求也越来越高。及时诊断、处理和解决校园网络故障,保证计算机网络的安全、可靠、稳定运行已成为一个极其重要的课题。
目前,高校校园网络管理部门普遍存在人员编制不足的问题,而在网络故障日常管理工作中,因为用户故障事件申报和问题种类繁多,数量巨大,完成问题解决和解答占用了大量的人力资源。如果运用信息检索、问答和文本挖掘技术对这些以文本形式呈现的用户申报和问题加以分析和利用,就可以提取蕴含其中的大量有用信息;同时将高校中成千上万的高素质网络用户有效地组织起来,协同网络管理员参与网络故障管理,就可以实现网络故障管理的自动化和智能化,从而有效提高人员配置效率,更好地为校园网络服务,为学校服务,为用户自己服务。
通过智能化高校校园网络故障管理系统的构建,可有效优化网络故障管理工作流程,让广大高素质高校用户同步参与校园网络故障管理,减轻网络管理员工作量,从而实现工作效率实质上的提升。
本文研究目标就是设计、开发出一个适于云南师范大学校园网络管理的基于Web的问答模式用户自助网络故障管理系统,主要研究成果工作如下:
结合高校校园网特点、数字化校园建设需求和网络故障管理工作特点,研究、分析和归纳了校园网络故障事件申报流程和解决流程。通过研究和应用信息检索技术、数据仓库技术、问答技术、文本挖掘技术、文本关联规则技术和词频统计等技术,设计、实现了云南师范大学校园网络故障管理用户自助排查系统。
With the rapid development of computer network technology, Campus Network has gradually penetrated into College crowd’s work and all aspects of life, become an essential communication and resource sharing platform. At the same time, the importance of managing campus network also been strengthened. With the growing number of Internet users, the network grew larger in scope, the growing complexity of network structure, network application gradually diversified people on the network management requirements are also increasing. Timely diagnosis, treatment and resolution of the campus network failure, ensure the security of computer networks, reliable, stable operation has become an extremely important issue.
Currently, the universality of the Campus Network Management exists to the problem of inadequate staffing. Because the user network fault event reporting and a wide range of issues, and a huge amount of work in the daily management of a network failure, the network administrator to complete problem solving and answers take a lot of human resources. If the use of information retrieval, text mining and question and answer form of text presented on these declarations and the problems of users for analysis and use, we can extract which contains lots of useful information; the same time, tens of thousands of high-quality university network users in Effectively organized, collaborative network administrator in network fault management, we can achieve network fault management automation and intelligence to improve staffing efficiency, better services for the campus network for school services, services for the user .
Campus Building intelligent network fault management system can effectively optimize network fault management workflow, so that the majority of users simultaneously participate in high-quality university campus network fault management, reduce the workload of network administrators in order to achieve substantial productivity improvement.
This research goal is to design, develop a network management for the campus of Yunnan Normal University Q & A mode of Web-based user self-service network fault management system, the main results are as follows:
I combine the characteristics of campus networks, digital campus building needs and characteristics of network fault management, research, analysis and summary of the campus network fault event reporting process and resolution processes. Through research and application of information retrieval technology, data warehouse, Q & A technology, text mining, text technology and word frequency association rules such as technology, design, implementation, Yunnan Normal University campus network fault management, user self-investigation system.
目前,高校校园网络管理部门普遍存在人员编制不足的问题,而在网络故障日常管理工作中,因为用户故障事件申报和问题种类繁多,数量巨大,完成问题解决和解答占用了大量的人力资源。如果运用信息检索、问答和文本挖掘技术对这些以文本形式呈现的用户申报和问题加以分析和利用,就可以提取蕴含其中的大量有用信息;同时将高校中成千上万的高素质网络用户有效地组织起来,协同网络管理员参与网络故障管理,就可以实现网络故障管理的自动化和智能化,从而有效提高人员配置效率,更好地为校园网络服务,为学校服务,为用户自己服务。
通过智能化高校校园网络故障管理系统的构建,可有效优化网络故障管理工作流程,让广大高素质高校用户同步参与校园网络故障管理,减轻网络管理员工作量,从而实现工作效率实质上的提升。
本文研究目标就是设计、开发出一个适于云南师范大学校园网络管理的基于Web的问答模式用户自助网络故障管理系统,主要研究成果工作如下:
结合高校校园网特点、数字化校园建设需求和网络故障管理工作特点,研究、分析和归纳了校园网络故障事件申报流程和解决流程。通过研究和应用信息检索技术、数据仓库技术、问答技术、文本挖掘技术、文本关联规则技术和词频统计等技术,设计、实现了云南师范大学校园网络故障管理用户自助排查系统。
With the rapid development of computer network technology, Campus Network has gradually penetrated into College crowd’s work and all aspects of life, become an essential communication and resource sharing platform. At the same time, the importance of managing campus network also been strengthened. With the growing number of Internet users, the network grew larger in scope, the growing complexity of network structure, network application gradually diversified people on the network management requirements are also increasing. Timely diagnosis, treatment and resolution of the campus network failure, ensure the security of computer networks, reliable, stable operation has become an extremely important issue.
Currently, the universality of the Campus Network Management exists to the problem of inadequate staffing. Because the user network fault event reporting and a wide range of issues, and a huge amount of work in the daily management of a network failure, the network administrator to complete problem solving and answers take a lot of human resources. If the use of information retrieval, text mining and question and answer form of text presented on these declarations and the problems of users for analysis and use, we can extract which contains lots of useful information; the same time, tens of thousands of high-quality university network users in Effectively organized, collaborative network administrator in network fault management, we can achieve network fault management automation and intelligence to improve staffing efficiency, better services for the campus network for school services, services for the user .
Campus Building intelligent network fault management system can effectively optimize network fault management workflow, so that the majority of users simultaneously participate in high-quality university campus network fault management, reduce the workload of network administrators in order to achieve substantial productivity improvement.
This research goal is to design, develop a network management for the campus of Yunnan Normal University Q & A mode of Web-based user self-service network fault management system, the main results are as follows:
I combine the characteristics of campus networks, digital campus building needs and characteristics of network fault management, research, analysis and summary of the campus network fault event reporting process and resolution processes. Through research and application of information retrieval technology, data warehouse, Q & A technology, text mining, text technology and word frequency association rules such as technology, design, implementation, Yunnan Normal University campus network fault management, user self-investigation system.
引文
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[3] [美]思科系统公司.Internetworking Troubleshooting Handbook Second Edition(姜汉龙,等,译者.王崧,审校).北京:电子工业出版社, 2002:1-4(图)
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[5] [美]Cisco Systems公司.Cisco Networking Academy Program.思科网络技术学院教程(第三、四学期)(第三版)(天津大学,电子科技大学,中山大学,思科网络技术学院,译者).北京:人民邮电出版社,2004:371-394,419-449,491,501-510
[6]韩冬.面向用户的校园网网络管理系统的研究与实现:硕士学位论文,广西大学,广西,2008.
[7]吴功宜.计算机网络高级教程.北京:清华大学出版社, 2007.
[8]郑先伟.整合校园网安全信息-分布式安全信息发布系统简介.中国教育网络, 2007,26:57-58 .
[9]刘雪平,艾治琼.基于JSP的校园网络故障申报系统.电脑知识与技术,2008,1(1),53-55.
[10]李汪丽.高校网络故障申报系统的结构功能设计.科技风,2009,01,63.
[11] BRAD GILMER. Network Troubleshooting.Broadcast Engineering. Overland Park: Jun 2006. Vol. 48, Iss. 6; pg. 34
[12] Thomas P Cavaiani. Using Selected Options of the Ipconfig Command to Teach Network Troubleshooting Techniques.Journal of Information Systems Education. West Lafayette: Fall 2005. Vol. 16, Iss. 3; pg. 251, 4 pgs
[13] John Garrett. Use pathping for enhanced network troubleshooting.Windows Professional. Rochester: Jun 2005. Vol. 10, Iss. 6; pg. 6, 2 pg.
[14]潘碧云.计算机网络管理系统及其发展趋势.职业圈,2007,04,168-170.
[15] [古文虎.基于策略的网络故障管理系统研究:硕士学位论文,湖南,2006.
[16] an Sommerville.圣安德鲁斯大学.软件工程(程成,陈霞,译者).北京:机械工业出版社,2008.
[17]王珊,萨师煊.数据库系统概论(第四版).北京:高等教育出版社,2007.
[18] Amir S. Ranjbar.CCNP Support认证考试指南(田学锋,魏占祯,等,译者).北京:电子工业出版社,2001.
[19]段立霞.计算机网络故障管理系统研究及实现:同济大学,上海,2006.
[20]马会彬.基于知识发现的网络故障管理系统研究与实现:硕士学位论文,西北工业大学,西安,2006.
[21]程建丽.计算机网络故障管理智能化研究.《成功(教育)》,2007,02,162.
[22] http://baike.baidu.com/view/7893.htm?fr=ala0_1
[23] Robert W. March Jr. Text mining patent literature: a case scenario of a methodology for analyzing unstructured data. Searcher 16.2 (Feb 2008): p.31(6).
[24] Jay Devore. The Text Mining Handbook.Journal of the American Statistical Association. Alexandria: Mar 2009. Vol. 104, Iss. 485; pg. 420
[25] A. Christy and P. CTSS: a tool for efficient information extraction with soft matching rules for text mining. Thambidurai. Journal of Computer Science 4.5 (May 2008): p.375(7).
[26] http://www.icst.pku.edu.cn/course/TextMining/07-08Spring/index.html
[27]刘宁.客户评价挖掘算法研究与实现:硕士学位论文,吉林大学,吉林,2009
[28]倪茂树.基于语义理解的观点评论挖掘研究[D]:硕士学位论文,大连理工大学,大连.2007.
[29] Yi J,Nasukawa T,Bunescu R C,et al.Sentiment analyzer:Extracting sentiments about a given topic using natural language processing techniques[C]The 3rd IEEE International Conference on Data Mining,2003.
[30] Popescu A M,Etzioni O.Extracting product features and opinions from reviews[C]Human Language Technology Conference and Conference on Empirical Methods inNatural Language Processing,Vancouver,Canada,2005.
[31] Gourant R,Hilbert.Methods of mathematical physics[J].1,1953.
[32] Salton G,Wong A,Yang C.S.A vector space model for automatic indexing[C].Communications of ACM,1975.
[33]成颖,史九林.自动分类研究现状与展望.情报学报[J],1999,10:20-26.
[34]肖明,沈英.自动分类研究进展.图书馆自动化[J],2000,5:25-28.
[35] Dumai S,Platt J,Heckerman Detal.Inductive learning algorithms and representations for text categorization[C].In Proceedings of the 1998 ACM 7th International Conference on Information and Knowledge Management,1998:148-155.
[36]张学工.关于统计学习理论与支持向量机.自动化学报[J],2000,1:32-42.
[37]卢增祥,李衍达.交互支持向量机学习算法及其应用[J].清华大学学报(自然科学报),1999,39(7):93-97.
[38]侯汉清.分类法的发展趋势简论[J].北京:书目文献出版社,1985.
[39]侯敏著.计算语言学与汉语自动分析[J].北京:北京广播学院出版社,1999.
[40]于玲,吴铁军.集成学习:Boosting算法综述[J].模式识别与人工智能,2004,17(1):52-59.
[41]秦进,陈笑蓉,汪维家等.文本分类中的特征抽取[J].计算机应用,2003,23(2):45-46.
[42]周茜,赵明生.中文文本分类中的特征选择研究[J].中文信息学报,2004,18(3):17-23.
[43] http://www.chinaai.org/pr/pattern-recognition/association-rules-algorithm.html
[44]赵俊杰.基于文本挖掘技术的论文抄袭判定研究:硕士学位论文,合肥工业大学,合肥,2009.
[45]周永伟.网络故障管理专家系统的研究与实现:硕士学位论文,北京工业大学,北京,2008
[46]亓雪龙.基于策略的网络管理系统设计与实现.硕士学位论文,山东大学,山东,2007
[47] http://www.icst.pku.edu.cn/course/TextMining/07-08Spring/index.html
[48]林亚平,刘云中,周顺先等.基于最大熵的隐马尔可夫模型文本信息抽取.电子学报. 2005.Vol.33.No.2:236-238.
[49]顾申华.基于互动问答系统的问题推荐.硕士学位论文,上海交通大学,上海,2008.
[50]刘祥.基于课程知识的问答系统研究与应用.硕士学位论文,大连海事大学,大连,2010.
[2] Mani Subramanian.Network Management Principles and Pranctice(王松,周靖,孟纯城,译者).北京:清华大学出版社,2003:31-39,83-86,393
[3] [美]思科系统公司.Internetworking Troubleshooting Handbook Second Edition(姜汉龙,等,译者.王崧,审校).北京:电子工业出版社, 2002:1-4(图)
[4] [美]Cisco Systems公司.Cisco Networking Academy Program.思科网络技术学院教程(第一、二学期)(第三版)(清华大学,北京大学,北京邮电大学,华南理工大学,思科网络技术学院,译者).北京:人民邮电出版社,2004:467-469
[5] [美]Cisco Systems公司.Cisco Networking Academy Program.思科网络技术学院教程(第三、四学期)(第三版)(天津大学,电子科技大学,中山大学,思科网络技术学院,译者).北京:人民邮电出版社,2004:371-394,419-449,491,501-510
[6]韩冬.面向用户的校园网网络管理系统的研究与实现:硕士学位论文,广西大学,广西,2008.
[7]吴功宜.计算机网络高级教程.北京:清华大学出版社, 2007.
[8]郑先伟.整合校园网安全信息-分布式安全信息发布系统简介.中国教育网络, 2007,26:57-58 .
[9]刘雪平,艾治琼.基于JSP的校园网络故障申报系统.电脑知识与技术,2008,1(1),53-55.
[10]李汪丽.高校网络故障申报系统的结构功能设计.科技风,2009,01,63.
[11] BRAD GILMER. Network Troubleshooting.Broadcast Engineering. Overland Park: Jun 2006. Vol. 48, Iss. 6; pg. 34
[12] Thomas P Cavaiani. Using Selected Options of the Ipconfig Command to Teach Network Troubleshooting Techniques.Journal of Information Systems Education. West Lafayette: Fall 2005. Vol. 16, Iss. 3; pg. 251, 4 pgs
[13] John Garrett. Use pathping for enhanced network troubleshooting.Windows Professional. Rochester: Jun 2005. Vol. 10, Iss. 6; pg. 6, 2 pg.
[14]潘碧云.计算机网络管理系统及其发展趋势.职业圈,2007,04,168-170.
[15] [古文虎.基于策略的网络故障管理系统研究:硕士学位论文,湖南,2006.
[16] an Sommerville.圣安德鲁斯大学.软件工程(程成,陈霞,译者).北京:机械工业出版社,2008.
[17]王珊,萨师煊.数据库系统概论(第四版).北京:高等教育出版社,2007.
[18] Amir S. Ranjbar.CCNP Support认证考试指南(田学锋,魏占祯,等,译者).北京:电子工业出版社,2001.
[19]段立霞.计算机网络故障管理系统研究及实现:同济大学,上海,2006.
[20]马会彬.基于知识发现的网络故障管理系统研究与实现:硕士学位论文,西北工业大学,西安,2006.
[21]程建丽.计算机网络故障管理智能化研究.《成功(教育)》,2007,02,162.
[22] http://baike.baidu.com/view/7893.htm?fr=ala0_1
[23] Robert W. March Jr. Text mining patent literature: a case scenario of a methodology for analyzing unstructured data. Searcher 16.2 (Feb 2008): p.31(6).
[24] Jay Devore. The Text Mining Handbook.Journal of the American Statistical Association. Alexandria: Mar 2009. Vol. 104, Iss. 485; pg. 420
[25] A. Christy and P. CTSS: a tool for efficient information extraction with soft matching rules for text mining. Thambidurai. Journal of Computer Science 4.5 (May 2008): p.375(7).
[26] http://www.icst.pku.edu.cn/course/TextMining/07-08Spring/index.html
[27]刘宁.客户评价挖掘算法研究与实现:硕士学位论文,吉林大学,吉林,2009
[28]倪茂树.基于语义理解的观点评论挖掘研究[D]:硕士学位论文,大连理工大学,大连.2007.
[29] Yi J,Nasukawa T,Bunescu R C,et al.Sentiment analyzer:Extracting sentiments about a given topic using natural language processing techniques[C]The 3rd IEEE International Conference on Data Mining,2003.
[30] Popescu A M,Etzioni O.Extracting product features and opinions from reviews[C]Human Language Technology Conference and Conference on Empirical Methods inNatural Language Processing,Vancouver,Canada,2005.
[31] Gourant R,Hilbert.Methods of mathematical physics[J].1,1953.
[32] Salton G,Wong A,Yang C.S.A vector space model for automatic indexing[C].Communications of ACM,1975.
[33]成颖,史九林.自动分类研究现状与展望.情报学报[J],1999,10:20-26.
[34]肖明,沈英.自动分类研究进展.图书馆自动化[J],2000,5:25-28.
[35] Dumai S,Platt J,Heckerman Detal.Inductive learning algorithms and representations for text categorization[C].In Proceedings of the 1998 ACM 7th International Conference on Information and Knowledge Management,1998:148-155.
[36]张学工.关于统计学习理论与支持向量机.自动化学报[J],2000,1:32-42.
[37]卢增祥,李衍达.交互支持向量机学习算法及其应用[J].清华大学学报(自然科学报),1999,39(7):93-97.
[38]侯汉清.分类法的发展趋势简论[J].北京:书目文献出版社,1985.
[39]侯敏著.计算语言学与汉语自动分析[J].北京:北京广播学院出版社,1999.
[40]于玲,吴铁军.集成学习:Boosting算法综述[J].模式识别与人工智能,2004,17(1):52-59.
[41]秦进,陈笑蓉,汪维家等.文本分类中的特征抽取[J].计算机应用,2003,23(2):45-46.
[42]周茜,赵明生.中文文本分类中的特征选择研究[J].中文信息学报,2004,18(3):17-23.
[43] http://www.chinaai.org/pr/pattern-recognition/association-rules-algorithm.html
[44]赵俊杰.基于文本挖掘技术的论文抄袭判定研究:硕士学位论文,合肥工业大学,合肥,2009.
[45]周永伟.网络故障管理专家系统的研究与实现:硕士学位论文,北京工业大学,北京,2008
[46]亓雪龙.基于策略的网络管理系统设计与实现.硕士学位论文,山东大学,山东,2007
[47] http://www.icst.pku.edu.cn/course/TextMining/07-08Spring/index.html
[48]林亚平,刘云中,周顺先等.基于最大熵的隐马尔可夫模型文本信息抽取.电子学报. 2005.Vol.33.No.2:236-238.
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