针对传统的Web GIS功能简单,可扩展性差等缺点,引入SOA架构和Web服务理念,以及日益增长的地理信息的服务需求,提出了服务式GIS的三要素结构,着重阐述了交通网络分析数据模型及其功能,并探讨了如何以Super Map i Server为平台来设计并实...针对传统的Web GIS功能简单,可扩展性差等缺点,引入SOA架构和Web服务理念,以及日益增长的地理信息的服务需求,提出了服务式GIS的三要素结构,着重阐述了交通网络分析数据模型及其功能,并探讨了如何以Super Map i Server为平台来设计并实现基于服务式GIS理念的城市交通网络分析信息查询系统,实验测试结果表明该系统可以满足人们日常工作与旅游出行的交通网络分析与查询的需求。展开更多
Based on the analysis of application status in real network,the trace model of some typical mobile Internet applications data is given and their impact on 2G/3G network is discussed in this paper.Furthermore,in order ...Based on the analysis of application status in real network,the trace model of some typical mobile Internet applications data is given and their impact on 2G/3G network is discussed in this paper.Furthermore,in order to support the mobile Internet application efficiently in future,the issues including the impact on the Long Term Evolution (LTE-A) system and some potential solutions for performance optimization are studied.Based on the trace data model of IM traffic,the performacne evaluaiton of LTE-A system shows that some specific configuration machanisms can play an important role in improving network system efficiency in the case of IM traffic.展开更多
The ability of accurate and scalable mobile device recognition is critically important for mobile network operators and ISPs to understand their customers' behaviours and enhance their user experience.In this pape...The ability of accurate and scalable mobile device recognition is critically important for mobile network operators and ISPs to understand their customers' behaviours and enhance their user experience.In this paper,we propose a novel method for mobile device model recognition by using statistical information derived from large amounts of mobile network traffic data.Specifically,we create a Jaccardbased coefficient measure method to identify a proper keyword representing each mobile device model from massive unstructured textual HTTP access logs.To handle the large amount of traffic data generated from large mobile networks,this method is designed as a set of parallel algorithms,and is implemented through the MapReduce framework which is a distributed parallel programming model with proven low-cost and high-efficiency features.Evaluations using real data sets show that our method can accurately recognise mobile client models while meeting the scalability and producer-independency requirements of large mobile network operators.Results show that a 91.5% accuracy rate is achieved for recognising mobile client models from 2 billion records,which is dramatically higher than existing solutions.展开更多
文摘针对传统的Web GIS功能简单,可扩展性差等缺点,引入SOA架构和Web服务理念,以及日益增长的地理信息的服务需求,提出了服务式GIS的三要素结构,着重阐述了交通网络分析数据模型及其功能,并探讨了如何以Super Map i Server为平台来设计并实现基于服务式GIS理念的城市交通网络分析信息查询系统,实验测试结果表明该系统可以满足人们日常工作与旅游出行的交通网络分析与查询的需求。
基金supported by the project"the Cross Layer Optimization Technique for IMT-Advanced " under Grant No.2010ZX03003-001-01-03
文摘Based on the analysis of application status in real network,the trace model of some typical mobile Internet applications data is given and their impact on 2G/3G network is discussed in this paper.Furthermore,in order to support the mobile Internet application efficiently in future,the issues including the impact on the Long Term Evolution (LTE-A) system and some potential solutions for performance optimization are studied.Based on the trace data model of IM traffic,the performacne evaluaiton of LTE-A system shows that some specific configuration machanisms can play an important role in improving network system efficiency in the case of IM traffic.
基金supported in part by the National Natural Science Foundation of China under Grant No.61072061the National Science and Technology Major Projects under Grant No.2012ZX03002008the Fundamental Research Funds for the Central Universities under Grant No.2012RC0121
文摘The ability of accurate and scalable mobile device recognition is critically important for mobile network operators and ISPs to understand their customers' behaviours and enhance their user experience.In this paper,we propose a novel method for mobile device model recognition by using statistical information derived from large amounts of mobile network traffic data.Specifically,we create a Jaccardbased coefficient measure method to identify a proper keyword representing each mobile device model from massive unstructured textual HTTP access logs.To handle the large amount of traffic data generated from large mobile networks,this method is designed as a set of parallel algorithms,and is implemented through the MapReduce framework which is a distributed parallel programming model with proven low-cost and high-efficiency features.Evaluations using real data sets show that our method can accurately recognise mobile client models while meeting the scalability and producer-independency requirements of large mobile network operators.Results show that a 91.5% accuracy rate is achieved for recognising mobile client models from 2 billion records,which is dramatically higher than existing solutions.
