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An Experimental Study of Factor Analysis over Cellular Network Data

机译:细胞网络数据因子分析的实验研究

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Mobile Network Operators (MNOs) are evolving towards becoming data-driven, while delivering capacity to collect and analyze data. This can help in enhancing user experiences while empowering the operation workforce and building new business models. Mobile traffic demands of users can give insights to MNOs to plan, decide and act depending on network conditions. In this paper, we investigate the behaviour of Istanbul residents using the cellular network traffic activity over spatial and temporal dimensions via exploratory factor analysis (EFA) using a major MNO's cellular network traffic data in Turkey. Our results reveal various time and spatial patterns for Istanbul residents such as morning and evening commuting factors, business and residential factors as well as nightlife and weekend afternoon factors as the most prominent cultural behaviour. The analysis results also demonstrate interesting findings such as tunnels and transportation paths selected by Istanbul residents may differ during morning rush work hour compared to evening rush after-work hour.
机译:移动网络运营商(MNO)在向数据驱动的方向发展,同时提供了收集和分析数据的能力。这可以帮助增强用户体验,同时赋予操作人员权力并建立新的业务模型。用户的移动流量需求可以使MNO洞悉,以根据网络条件进行计划,决策和采取行动。在本文中,我们通过使用土耳其主要MNO蜂窝网络流量数据的探索性因子分析(EFA),调查了伊斯坦布尔居民在时空范围内使用蜂窝网络流量活动的行为。我们的研究结果揭示了伊斯坦布尔居民的各种时间和空间模式,例如早晚通勤因素,商业和居住因素以及夜生活和周末下午因素,这些都是最突出的文化行为。分析结果还表明,有趣的发现,例如伊斯坦布尔居民选择的隧道和运输路线,在早上高峰时间与晚上高峰时间可能会有所不同。

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