Traditional signaling based telecom service control architecture, intelligent network service control architecture, and even Next Generation Network (NGN) and IP Multimedia Subsystem (IMS) that are pictured as the fun...Traditional signaling based telecom service control architecture, intelligent network service control architecture, and even Next Generation Network (NGN) and IP Multimedia Subsystem (IMS) that are pictured as the fundamental of future services have become an obstacle for telecom service development because they are closed in service creation and service control and implemented on a centralized computing platform. As newest technologies in Internet, distributed computing and enterprise information services, Service-Oriented Architecture (SOA), Web services and Web 2.0 have been widely recognized in information and service industry in past years. According to the SOA definition of OASIS, SOA will bring tremendous changes in capability, service and service interface, compared with traditional telecom service architecture. Moreover, it adds a capability-service transform mechanism and greatly simplifies application development.展开更多
The penetration of mobile phones is nearly saturated in both developing and developed regions. In such a circumstance, how to prevent subscriber churn has become an important issue for today＇s telecom operators, as t...The penetration of mobile phones is nearly saturated in both developing and developed regions. In such a circumstance, how to prevent subscriber churn has become an important issue for today＇s telecom operators, as the cost to acquire a new subscriber is much higher than that to retain an existing subscriber. In this paper, we propose to leverage the power of big data to mitigate the problem of subscriber churn and enhance the service quality of telecom operators. As the information hub, telecom operators have accumulated a huge volume of valuable data on subscriber behaviors, service usage, and network operations. To enable efficient big data processing, we first build a dedicated distributed cloud infrastructure that integrates both online and offline processing capabilities. Second, we develop a complete churn analysis model based on deep data mining techniques, and utilize inter-subscriber influence to improve prediction accuracy. Finally, we use real datasets obtained from a large telecom operator in China to verify the accuracy of our churn analysis models. The dataset contains the information of over 3.5 million subscribers, which generate over 600 million call detail records （CDRs） per month. The empirical results demonstrate that our proposed method can achieve around 90% accuracy for T ＋ 1 testing periods and identify subscribers with high negative influence successfully.展开更多
文摘Traditional signaling based telecom service control architecture, intelligent network service control architecture, and even Next Generation Network (NGN) and IP Multimedia Subsystem (IMS) that are pictured as the fundamental of future services have become an obstacle for telecom service development because they are closed in service creation and service control and implemented on a centralized computing platform. As newest technologies in Internet, distributed computing and enterprise information services, Service-Oriented Architecture (SOA), Web services and Web 2.0 have been widely recognized in information and service industry in past years. According to the SOA definition of OASIS, SOA will bring tremendous changes in capability, service and service interface, compared with traditional telecom service architecture. Moreover, it adds a capability-service transform mechanism and greatly simplifies application development.
基金This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61272397, 61472454, and 61572538, and the Guangdong Natural Science Funds for Distinguished Young Scholar under Grant No. S20120011187.
文摘The penetration of mobile phones is nearly saturated in both developing and developed regions. In such a circumstance, how to prevent subscriber churn has become an important issue for today＇s telecom operators, as the cost to acquire a new subscriber is much higher than that to retain an existing subscriber. In this paper, we propose to leverage the power of big data to mitigate the problem of subscriber churn and enhance the service quality of telecom operators. As the information hub, telecom operators have accumulated a huge volume of valuable data on subscriber behaviors, service usage, and network operations. To enable efficient big data processing, we first build a dedicated distributed cloud infrastructure that integrates both online and offline processing capabilities. Second, we develop a complete churn analysis model based on deep data mining techniques, and utilize inter-subscriber influence to improve prediction accuracy. Finally, we use real datasets obtained from a large telecom operator in China to verify the accuracy of our churn analysis models. The dataset contains the information of over 3.5 million subscribers, which generate over 600 million call detail records （CDRs） per month. The empirical results demonstrate that our proposed method can achieve around 90% accuracy for T ＋ 1 testing periods and identify subscribers with high negative influence successfully.
