运营商三方资料稽核系统研究及应用

复杂的业务使得电信企业在内部收入保障这个问题上遇到前所未有的挑战,各大运营商通过提高数据质量及改进业务流程来增加利润成为收入保障的必然之选。三方资料稽核系统是收入保障系统的子集,本文通过对收入稽核系统的研究来阐述业内的相关发展。     

改进支持向量机在电信客户流失预测的应用

电信流失客户数据精确预测是挽留客户的有效手段.电信业的管理中对收费、投诉、业务受理等问题,显然是一种典型的非平衡样本,传统用标准的支持向量机没有考虑样本分布不平衡问题,虽然在样本数据平衡前提下具有较好的预测精度,但对于不平衡电信客户数据,预测精度大大下降.为提高预测精度,针对支持向量机处理不平衡样本时的缺陷,提出了基于代价敏感学习的支持向量机模型.模型利用代价敏感学习对不平衡样本集分别采用不同惩罚系数,然后建立电信客户流失预测模型,最后对实际电信客户流失数据进行测试.通过与标准支持向量机、神经网络对比,结果表示模型提高了预测精度,有效地解决了数据集非平衡性问题,是一种有效的电信客户流失预测方法.     

基于七号信令的收入保障系统

目前我国电信行业的收入体系是对采集到的交换机原始话单进行计费来实现的,采集点分散、实时性差,交换系统所提供的原始话单接口的复杂多变使得计费收入得不到充分保障.介绍一种基于七号信令的收入保障系统,在信令链路上实时获取话务控制的信令信息,再对信令信息分析处理后形成信令话单,该话单与计费的原始话单的比对和校验来确保计费收入的正确和完备.系统具有实时性高、容量大、可靠性强等特点,有效解决了电信运营商的收入保障问题.     

基于RAP的收入保障过程控制的实现

收入流失的风险主要发生在BOSS系统的运行和维护过程中。如何有效的控制BOSS的收入流失。成为电信运营商关注的热点。本文介绍的收入保障产品RAP，已经部署在浙江省某移动公司。运行结果表明，该产品能有效地发现电信运营过程中存在的收入漏洞。     

面向电信欠费挖掘的数据质量评估策略研究

针对电信欠费挖掘主题,结合电信欠费数据非平衡的特点,重点研究了缺失与离群数据对分类结果的影响,从而提出了一个面向电信欠费挖掘的数据质量评估体系（TIM-DQAS）：对于缺失评估,提出了一种基于类分布差异的属性加权算法,以衡量输入属性的缺失代价;对于离群评估,分析了非平衡数据中的离群点对分类结果的影响,提出离群度的概念,以量化离群点的影响。基于某城市电信小灵通数据的对比实验,给出了评估结果的参照值,验证了评估策略的有效性。     

AR1MA时间序列模型在电信产品收入预算中的应用

本文通过AR1MA时间序列模型,利用SPSS统计软件对某电信产品收入进行了预测研究。结果表明,模型拟合较好,将模型预测数据与实际数据比较显示,模型预测精度较高。为电信制定经营决策、编制下年度的工作计划提供依据与参考。     

基于自相似异常判断模型的数据稽核方法

通过在数据稽核过程中根据数据自身的相似性来确定正常的数据趋势,并判断数据是否存在异常,满足横向 数据稽核的要求,解决通信业务办理数据稽核的问题。     

浅谈电信企业收入管理工作

简要介绍电信企业收入管理工作,对电信企业收入管理涉及的关键点和实施工作进行了分析。     

特征树阀值检测算法应对电信欺诈

电信网络日益复杂,这增加了电信营运的难度,并且大额欺诈和恶意欠费的状况使电信运营收入存在较大的风险。本文在数据挖掘技术、基于聚类的层次分析算法等理论基础上,采用了欺诈特征树阀值检测算法来应对电信欺诈,防范电信运营收入的流失。该算法将用户的数据特征项构建成欺诈特征树,采用关系数据模式来组织用户的欺诈特征项,并设定结点阀值作为检测判断的依据,依照用户最后的欺诈度值判断用户是否欺诈。算法简单高效,系统占用较少的内存并获得了较高的准确率。     

UML在取用水数据稽核中的应用

由于国家水资源监控平台可能存在取用水数据不一致和不可靠的问题,为提高数据质量,实现取用水数据的完整性和一致性,运用UML建立取用水数据稽核系统,挖掘取用水数据库表中隐含的阈值和字段关系,为取用水的异常数据提供数据稽核的方法;通过静态和动态建模支持稽核系统的数据分析,实现逐步提升取用水数据的数据质量;通过建立用例图、类图和活动图,确定用户和数据库之间的交互关系,展示取用水数据的内容属性,并实现系统中取用水数据的可视化和一致性检测。     

使用CC标准开发的高保证安全信息系统

通用标准（Common Criteria）提供了衡量系统安全性的流行准则。本文主要提出通过各类保证措施，如何构建符合CC标准的高保证安全信息系统。文中首先给出了CC的评估模型、评估过程和安全保证的具体要求。然后以开发安全审计系统为例，分析了系统安全功能和保证要求的产生、审计系统的实现框架以及为达到标准要求而在系统开发过程中使用的各种保证证据和保证措施。最后，又分析了审计系统对整个系统的性能影响因素，并提出了改进办法。本文通过深入剖析通用标准中各个保证要求的内涵，为开发具有高保证要求的信息系统提供了理论指导和实现方法。     

Intelligent churn prediction in telecom: employing mRMR feature selection and RotBoost based ensemble classification

Churn prediction in telecom has recently gained substantial interest of stakeholders because of associated revenue losses. Predicting telecom churners, is a challenging problem due to the enormous nature of the telecom datasets. In this regard, we propose an intelligent churn prediction system for telecom by employing efficient feature extraction technique and ensemble method. We have used Random Forest, Rotation Forest, RotBoost and DECORATE ensembles in combination with minimum redundancy and maximum relevance (mRMR), Fisher’s ratio and F-score methods to model the telecom churn prediction problem. We have observed that mRMR method returns most explanatory features compared to Fisher’s ratio and F-score, which significantly reduces the computations and help ensembles in attaining improved performance. In comparison to Random Forest, Rotation Forest and DECORATE, RotBoost in combination with mRMR features attains better prediction performance on the standard telecom datasets. The better performance of RotBoost ensemble is largely attributed to the rotation of feature space, which enables the base classifier to learn different aspects of the churners and non-churners. Moreover, the Adaboosting process in RotBoost also contributes in achieving higher prediction accuracy by handling hard instances. The performance evaluation is conducted on standard telecom datasets using AUC, sensitivity and specificity based measures. Simulation results reveal that the proposed approach based on RotBoost in combination with mRMR features (CP-MRB) is effective in handling high dimensionality of the telecom datasets. CP-MRB offers higher accuracy in predicting churners and thus is quite prospective in modeling the challenging problems of customer churn prediction in telecom.     

Coordination in competitive dual sales channels of the mobile phone industry

This paper examines the supply chain coordination and contracting issue for competitive dual sales channels of the mobile phone industry in which a telecom service operator (operator) cooperates and competes with a handset manufacturer (manufacturer) to deliver the complementary telecom service and handset to end consumers. After deriving the equilibrium strategy based on the game theoretical paradigm, we present the sensitivity and comparative analysis as well as the coordination analysis. Among others, our result shows that the widely used subsidy‐only contract fails to coordinate this dual sales system. To solve the coordination failure, we propose a two‐way revenue sharing contract that can be well utilized to realize the first best outcome, and even guarantee the win–win situation, in particular under the situation when the telecom service price is sufficiently high.     

Facilitating cross-selling in a mobile telecom market to develop customer classification model based on hybrid data mining techniques

As the competition between mobile telecom operators becomes severe, it becomes critical for operators to diversify their business areas. Especially, the mobile operators are turning from traditional voice communication to mobile value-added services (VAS), which are new services to generate more average revenue per user (ARPU). That is, cross-selling is critical for mobile telecom operators to expand their revenues and profits. In this study, we propose a customer classification model, which may be used for facilitating cross-selling in a mobile telecom market. Our model uses the cumulated data on the existing customers including their demographic data and the patterns for using old products or services to find new products and services with high sales potential. The various data mining techniques are applied to our proposed model in two steps. In the first step, several classification techniques such as logistic regression, artificial neural networks, and decision trees are applied independently to predict the purchase of new products, and each model produces the results of their prediction as a form of probabilities. In the second step, our model compromises all these probabilities by using genetic algorithm (GA), and makes the final decision for a target customer whether he or she would purchase a new product. To validate the usefulness of our model, we applied it to a real-world mobile telecom company’s case in Korea. As a result, we found that our model produced high-quality information for cross-selling, and that GA in the second step contributed to significantly improve the performance.     

Segmentation of telecom customers based on customer value by decision tree model

The more the telecom services marketing paradigm evolves, the more important it becomes to retain high value customers. Traditional customer segmentation methods based on experience or ARPU (Average Revenue per User) consider neither customers’ future revenue nor the cost of servicing customers of different types. Therefore, it is very difficult to effectively identify high-value customers. In this paper, we propose a novel customer segmentation method based on customer lifecycle, which includes five decision models, i.e. current value, historic value, prediction of long-term value, credit and loyalty. Due to the difficulty of quantitative computation of long-term value, credit and loyalty, a decision tree method is used to extract important parameters related to long-term value, credit and loyalty. Then a judgments matrix formulated on the basis of characteristics of data and the experience of business experts is presented. Finally a simple and practical customer value evaluation system is built. This model is applied to telecom operators in a province in China and good accuracy is achieved.     

Revolutionising mobile payments

The concept of using a mobile phone or device to purchase goods and services has always been one of the key ambitions for technology players within the telecom industry. Mobile proximity technology, or contactless technology as it is often referred to, is one such technology that is seen as a means to enable mobile transactions and open up ways for operators to reach consumers and generate new revenue streams. However, mobile proximity solutions remain a relatively new concept within the telecom industry itself and we are only now starting to see trials and deployments of this technology. So what is mobile proximity technology and will it be the promised nirvana it is perceived to be for operators?     

采用混合模型的电信领域用户流失预测

用户流失预测能够帮助公司减少客户的流失,对公司的营收和提高竞争力有重要意义。然而,由于电信领域数据的稀疏性和不平衡等问题,国内外对于电信领域的用户流失预测大多处于研究阶段,还没有真正应用到实际生产当中。提出了利用神经网络、机器学习与朴素随机过采样、投票相结合的混合模型来预测电信领域的流失用户。数据集使用的是KDD Cup 2009年比赛数据,该数据由法国电信运行商Orange公司提供。在十折交叉验证下,AdaBoost和Gradient Boosting一次投票分类后AUC值能够达到0.677 1,利用其他模型对混合模型预测出的流失用户清单进行二次投票分类,前200名高危流失用户的预测准确率能够达到31.8%。实验结果表明,朴素随机过采样和投票相结合有效提升了模型的准确性。     

基于模型的复杂系统任务保证研究综述

任务保证是国外航天领域为确保复杂系统在任务周期内的安全、可靠而提出的一种工作方式。随着基于模型的系统工程技术近年来的迅猛发展，将其与任务保证相结合而形成的基于模型的任务保证概念开始得到广泛的应用和认可。这为复杂系统在安全性、可靠性工作方法上的进一步发展提供了有利条件。介绍了基于模型的任务保证概念，并对其所涉及的安全/保证案例、基于模型的系统工程、目标结构表示等关键要素，以及其目前在复杂系统设计中的典型应用进行了阐述。对基于模型的任务保证在未来的发展方向进行了展望。为基于模型的任务保证在复杂系统中的应用提供了理论参考。     

数据挖掘方法在用户流失预测分析中的应用

移动通信在高速发展的同时,出现了大量用户离网的现象,基于客户信息、消费行为等历史数据,进行客户离网预测分析成为各个运营商普遍关注的问题。文章基于客户的历史数据和短期偶发数据,提出了链型数据挖掘方法,并结合决策树,形成了一个综合的链型树分类器(Chain Tree Classifier,CTC)和用户行为预测模型,实验结果显示,该分类器对移动通信运营商感兴趣的单个事件发生具有良好的预测能力,可被应用到客户离网预测中,从而帮助运营商提前发现具有离网倾向的用户,进而获得更高的利润。