The internet activity records (IARs) of a mobile cellular network posses significant information which can be exploited to identify the network's efficacy and the mobile users' behavior. In this work, we extract useful information from the IAR data and identify a healthy predictability of spatio-temporal pattern within the network traffic. The information extracted is helpful for network operators to plan effective network configuration and perform management and optimization of network's resources. We report experimentation on spatiotemporal analysis of IAR data of the Telecom Italia. Based on this, we present mobile traffic partitioning scheme. Experimental results of the proposed model is helpful in modelling and partitioning of network traffic patterns.

本文从移动蜂窝网络的互联网活动记录（IAR）数据中提取了有用信息，并识别出网络流量中的健康可预测性的时空模式。这些提取出的信息有助于网络运营商规划有效的网络配置和执行网络资源的管理和优化。我们在 Telecom Italia 的 IAR 数据上进行了实验。基于此，我们提出了移动流量分区方案。所提出的模型的实验结果有助于建模和分割网络流量模式。

利用网络活动记录数据来理解和划分移动流量--一种时空方法