基于数据挖掘的网络优化
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摘要
网络优化是一项贯穿网络运营始终的工作。通过采集OMC统计数据、DT/CQT数据、用户投诉数据和告警信息,并对这些数据加以分析,可以发现网络故障并加以排除,从而实现保证网络性能、提高用户满意度这一目标。然而,随着移动通信事业的快速发展,人们对于业务种类及质量的要求越来越高。与此同时,各地区不断增多的用户群体数也加重了网络的负荷。在这样的背景下,传统的网络维护与优化工作遭遇了新的挑战。为了解决上述问题,我们期望在传统的网络优化工作中引入先进技术,从而更加可靠且高效地完成网优工作。
随着信息化社会的发展,各行各业都存储了海量数据。数据挖掘技术可以充分利用这些数据并从中寻找到有用信息。它具有如关联分析、预测、分类和聚类等多种功能。我们可以利用这些功能处理海量数据,找出数据间所包含的潜在信息。
论文首先详细介绍了传统网络优化的流程和工作中涉及的统计指标和无线参数,并说明如何利用这些数据来分析网络当前性能、定位网络问题。而后说明传统网络优化的缺点和不足,并对数据挖掘技术应用于网络优化工作进行了可行性分析。
笔者利用数据挖掘技术中的回归分析功能来预测话务量的发展趋势。文中说明了话务预测的具体流程,并介绍了真实数据的处理方法,对现有数据进行建模,最终通过模型预测短期内的话务变化。接下来,笔者讨论了如何发现某基站下性能较差的小区。文中首先说明传统方式的滞后性,并提出用数据挖掘技术中的聚类功能对小区性能进行划分,找出其中质量较差的小区。该方法高效、及时,具有较强的推广价值。而后,笔者研究如何利用数据挖掘中的分类的功能分析网络故障原因。文中结合真实案例进行说明,对处理结果进行了详尽的分析。论文最后分析了网络优化工作中可应用该技术的切入点,对数据挖掘技术在网络优化工作中的应用进行了展望。
Network optimization is an important category of task which impacts every aspects of network operation. By collecting and analyzing OMC statistic data, DT/CQT data as well as user complaints data and alarm information, network malfunctions can be identified and cleared in time such that network performance can be guaranteed and user's satisfaction can be improved.
However, with the rapid development of the mobile communication industry, user's expectation for business types and service quality become increasingly higher. At the same time, the expansion of user population in different areas can also overburden the network. Under such background, the traditional network maintenance and optimization encountered new challenges. In order to address these problems, we expect to introduce new advanced technologies to traditional network optimization so that we can tackle the task more reliably and efficiently.
As a leading-edge technology, data mining originates from the development of data warehouse, which makes full use of the massive data and extracts more useful information in these data. This technology is associated with multiple functions such as correlation analysis, forecasting, classification and clustering. By using these functions, we are able to discover the hiddern knowledge behind the huge amounts of data.
This dissertation briefly introduces the process of traditional network optimization and analyzes the statistic data and wireless parameter deeply. How to use the data to analyze performance of the network and pin point problems in the network will be explained in detail. This dissertation also discusses the weakness and shortage in traditional network optimization, and conducts a feasibility analysis.
The author uses the technology of regression analysis in data mining to predict traffic trends. This dissertation describes the flow of forecasting traffic and elaborates the processing for the existed data, building a modelbased on these data, which is used to predict the short-term traffic. This paper puts forward a new way to find inferior cell in base stations, which is more efficient and timely, and will be compared with the traditional one. At last, the writer analyzes the root-causes of network problems with data mining technology. Associated with a practical case, this paper gives a detailed analysis.
At last, the author presents some discussionon in which fields data mining can be used along with prospect of network optimization with data mining in the future.
随着信息化社会的发展,各行各业都存储了海量数据。数据挖掘技术可以充分利用这些数据并从中寻找到有用信息。它具有如关联分析、预测、分类和聚类等多种功能。我们可以利用这些功能处理海量数据,找出数据间所包含的潜在信息。
论文首先详细介绍了传统网络优化的流程和工作中涉及的统计指标和无线参数,并说明如何利用这些数据来分析网络当前性能、定位网络问题。而后说明传统网络优化的缺点和不足,并对数据挖掘技术应用于网络优化工作进行了可行性分析。
笔者利用数据挖掘技术中的回归分析功能来预测话务量的发展趋势。文中说明了话务预测的具体流程,并介绍了真实数据的处理方法,对现有数据进行建模,最终通过模型预测短期内的话务变化。接下来,笔者讨论了如何发现某基站下性能较差的小区。文中首先说明传统方式的滞后性,并提出用数据挖掘技术中的聚类功能对小区性能进行划分,找出其中质量较差的小区。该方法高效、及时,具有较强的推广价值。而后,笔者研究如何利用数据挖掘中的分类的功能分析网络故障原因。文中结合真实案例进行说明,对处理结果进行了详尽的分析。论文最后分析了网络优化工作中可应用该技术的切入点,对数据挖掘技术在网络优化工作中的应用进行了展望。
Network optimization is an important category of task which impacts every aspects of network operation. By collecting and analyzing OMC statistic data, DT/CQT data as well as user complaints data and alarm information, network malfunctions can be identified and cleared in time such that network performance can be guaranteed and user's satisfaction can be improved.
However, with the rapid development of the mobile communication industry, user's expectation for business types and service quality become increasingly higher. At the same time, the expansion of user population in different areas can also overburden the network. Under such background, the traditional network maintenance and optimization encountered new challenges. In order to address these problems, we expect to introduce new advanced technologies to traditional network optimization so that we can tackle the task more reliably and efficiently.
As a leading-edge technology, data mining originates from the development of data warehouse, which makes full use of the massive data and extracts more useful information in these data. This technology is associated with multiple functions such as correlation analysis, forecasting, classification and clustering. By using these functions, we are able to discover the hiddern knowledge behind the huge amounts of data.
This dissertation briefly introduces the process of traditional network optimization and analyzes the statistic data and wireless parameter deeply. How to use the data to analyze performance of the network and pin point problems in the network will be explained in detail. This dissertation also discusses the weakness and shortage in traditional network optimization, and conducts a feasibility analysis.
The author uses the technology of regression analysis in data mining to predict traffic trends. This dissertation describes the flow of forecasting traffic and elaborates the processing for the existed data, building a modelbased on these data, which is used to predict the short-term traffic. This paper puts forward a new way to find inferior cell in base stations, which is more efficient and timely, and will be compared with the traditional one. At last, the writer analyzes the root-causes of network problems with data mining technology. Associated with a practical case, this paper gives a detailed analysis.
At last, the author presents some discussionon in which fields data mining can be used along with prospect of network optimization with data mining in the future.
引文
[1]赵佳秋网络优化问题分析科技资讯30 201211-11.
[2]谷亚超数据挖掘在移动网络优化中的应用[学位论文],河北工程大学,2011
[3]贾松江关于数据挖掘在移动网络优化中的应用探讨中国新技术新产品24 2012 33-33
[4]刘秀彬浅谈数据挖掘在网络优化中的应用硅谷015 2010 101-101
[5]靳晓嘉,潘阳发,宋俊德移动通信网络优化技术的现状及其发展趋势电信技术122003 1-3
[6]吉多杰加移动通信网络优化现状及发展趋势价值工程042012 125
[7]韩斌杰GSM原理及其网络优化机械工业出版社2001
[8]刘海博基于数据仓库的移动通信网络优化研究[学位论文],河北大学2008
[9]王国民无线网络优化发展方向探讨通信管理与技术002 2010 11-13
[10]陈国军GSM网络优化浅析邮电设计技术4 2004 10-16
[11]李维鹏无线通信网络优化的流程及方法黑龙江科技信息002 2011 85-85
[12]钟云数据挖掘在网络优化中的应用[学位论文],电子科技大学2006
[13]毛国君,段立娟.数据挖掘原理与算法.清华大学出版社,2009.6
[14]施小钢数据挖掘在移动网络优化中的应用探讨信息与电脑(理论版)7 2011 095
[15]刘毅无线通信网络优化初探无线互联科技7 2011 14
[16]张传福,吴伟陵移动通信系统中的切换和切换算法中国数据通信4.7 2002 23-28
[17]陈安,陈宁,周龙骧数据挖掘技术及应用2006
[18]沈亮数据挖掘在移动通信网络优化中的应用[学位论文],上海交通大学2009
[19]程晓军,苗守野,景洪水等基于用户感知的移动网络优化体系及关键技术邮电设计技术112012 54-58.
[20]周鑫GPRS网络优化中小区流量的预测研究[学位论文],湖南大学2010.
[21]武黎强论移动通信网络优化及其流程无线互联科技1 2012 4-5.
[22]李伟斌数据挖掘在移动网络优化中的应用[学位论文],北京邮电大学2010
[23]张永敏数据挖掘在移动网络优化中的应用与实现[学位论文],电子科技大学2007
[24]邵峰晶,于忠清,王金龙,孙仁诚数据挖掘原理与算法科学出版社20098
[25]廖芹,郝志峰,陈志宏数据挖掘与数学建模国防工业出版社2010 2
[26]张红坡,张海峰等SPSS统计分析实用宝典清华大学出版社20126
[27]胡硕朴GSM网络优化方法探讨科技资讯 11 2012 33-33
[28]杨云,冯亚基于决策树的GSM网络优化专家系统微型电脑应用 011 2009 23-25
[29]徐晶,孙艳民,付旭轮等基于决策树算法的无线参数优化方法电信工程技术与标准化25.1 2012 7-10
[30]张云涛,龚玲数据挖掘原理与技术电子工业出版社2004
[31]李雄飞,李军数据挖掘与知识发现Vol 11高等教育出版社2003
[32]杨云,冯亚GSM网络优化中接通率的分析计算机工程与科学32.010 2010 20-22
[33]Lakshmi, B. N., and G. H. RaghunandhanA conceptual overview of data mininglnnovations in Emerging Technology (NCOIET),2011 National Conference on. IEEE,2011.
[34]Zhaogang Wang, Bin Zhang,Guohui LiA Topological Constraints Based Sequential Data Mining Approach on Telecom Networks Alarm Data Computational Sciences and Optimization,2009CSO 2009.
[35]高嫒,靳世杰,赵永峰智能优化技术在无线网络优化中的应用及算法研究电信快报网络与通信22011 17-20
[36]王雁平,乐春峡移动网络最差小区数据挖掘分析与实现计算机工程与设计 28.17 20074165-4168
[37]Antonie, Maria-Luiza, Osmar R. Zaiane, and AlexandruComan. Application of data mining techniques for medical image classification MDM/KDD 200194-101
[38]Aggarwal, Niyati, et al. Analysis the effect of data mining techniques on database. Advances in Engineering Software 47.1 2012 164-169
[39]Osei-Bryson, Kweku-Muata. Towards supporting expert evaluation of clustering results using a data mining process model Information Sciences 180.3 2010 414-431
[2]谷亚超数据挖掘在移动网络优化中的应用[学位论文],河北工程大学,2011
[3]贾松江关于数据挖掘在移动网络优化中的应用探讨中国新技术新产品24 2012 33-33
[4]刘秀彬浅谈数据挖掘在网络优化中的应用硅谷015 2010 101-101
[5]靳晓嘉,潘阳发,宋俊德移动通信网络优化技术的现状及其发展趋势电信技术122003 1-3
[6]吉多杰加移动通信网络优化现状及发展趋势价值工程042012 125
[7]韩斌杰GSM原理及其网络优化机械工业出版社2001
[8]刘海博基于数据仓库的移动通信网络优化研究[学位论文],河北大学2008
[9]王国民无线网络优化发展方向探讨通信管理与技术002 2010 11-13
[10]陈国军GSM网络优化浅析邮电设计技术4 2004 10-16
[11]李维鹏无线通信网络优化的流程及方法黑龙江科技信息002 2011 85-85
[12]钟云数据挖掘在网络优化中的应用[学位论文],电子科技大学2006
[13]毛国君,段立娟.数据挖掘原理与算法.清华大学出版社,2009.6
[14]施小钢数据挖掘在移动网络优化中的应用探讨信息与电脑(理论版)7 2011 095
[15]刘毅无线通信网络优化初探无线互联科技7 2011 14
[16]张传福,吴伟陵移动通信系统中的切换和切换算法中国数据通信4.7 2002 23-28
[17]陈安,陈宁,周龙骧数据挖掘技术及应用2006
[18]沈亮数据挖掘在移动通信网络优化中的应用[学位论文],上海交通大学2009
[19]程晓军,苗守野,景洪水等基于用户感知的移动网络优化体系及关键技术邮电设计技术112012 54-58.
[20]周鑫GPRS网络优化中小区流量的预测研究[学位论文],湖南大学2010.
[21]武黎强论移动通信网络优化及其流程无线互联科技1 2012 4-5.
[22]李伟斌数据挖掘在移动网络优化中的应用[学位论文],北京邮电大学2010
[23]张永敏数据挖掘在移动网络优化中的应用与实现[学位论文],电子科技大学2007
[24]邵峰晶,于忠清,王金龙,孙仁诚数据挖掘原理与算法科学出版社20098
[25]廖芹,郝志峰,陈志宏数据挖掘与数学建模国防工业出版社2010 2
[26]张红坡,张海峰等SPSS统计分析实用宝典清华大学出版社20126
[27]胡硕朴GSM网络优化方法探讨科技资讯 11 2012 33-33
[28]杨云,冯亚基于决策树的GSM网络优化专家系统微型电脑应用 011 2009 23-25
[29]徐晶,孙艳民,付旭轮等基于决策树算法的无线参数优化方法电信工程技术与标准化25.1 2012 7-10
[30]张云涛,龚玲数据挖掘原理与技术电子工业出版社2004
[31]李雄飞,李军数据挖掘与知识发现Vol 11高等教育出版社2003
[32]杨云,冯亚GSM网络优化中接通率的分析计算机工程与科学32.010 2010 20-22
[33]Lakshmi, B. N., and G. H. RaghunandhanA conceptual overview of data mininglnnovations in Emerging Technology (NCOIET),2011 National Conference on. IEEE,2011.
[34]Zhaogang Wang, Bin Zhang,Guohui LiA Topological Constraints Based Sequential Data Mining Approach on Telecom Networks Alarm Data Computational Sciences and Optimization,2009CSO 2009.
[35]高嫒,靳世杰,赵永峰智能优化技术在无线网络优化中的应用及算法研究电信快报网络与通信22011 17-20
[36]王雁平,乐春峡移动网络最差小区数据挖掘分析与实现计算机工程与设计 28.17 20074165-4168
[37]Antonie, Maria-Luiza, Osmar R. Zaiane, and AlexandruComan. Application of data mining techniques for medical image classification MDM/KDD 200194-101
[38]Aggarwal, Niyati, et al. Analysis the effect of data mining techniques on database. Advances in Engineering Software 47.1 2012 164-169
[39]Osei-Bryson, Kweku-Muata. Towards supporting expert evaluation of clustering results using a data mining process model Information Sciences 180.3 2010 414-431