Impact of CDR data analysis using big data technologies for the public: An analysis

Nair, Suja Chandrasekharan; Elayidom, M. Sudheep; Gopalan, Sasi

doi:10.1109/icaccs.2017.8014651

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…For instance, our interactions with the Internet and social media platforms have allowed researchers to better understand several aspects of our collective behaviour [5–14]. However, an even more popular data source, which has quickly become fundamental in the study of human behaviour, is that derived from our interactions with the mobile phone network [15–18]. Indeed, such data have become a key part in the new field of computational social science and has recently been of great importance in studying the current COVID-19 pandemic [19–24].…”

Section: Introductionmentioning

confidence: 99%

Quantifying the differences incall detail records

Botta

2021

R. Soc. open sci.

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The increasing availability of mobile phone data has attracted the attention of several researchers interested in studying our collective behaviour. Our interactions with the phone network can take several forms, from SMS messages to phone calls and data usage. Typically, mobile phone data are released to researchers in the form of call detail records , which contain records of different types of interactions, and can be used to analyse various aspects of our behaviour. However, the inherently behavioural nature of these interactions may result in differences between how we make phone calls and receive text messages. Studies which rely on data derived from these interactions, therefore, need to carefully consider these differences. Here, we aim to investigate differences and limitations of different types of mobile phone interactions data by analysing a large mobile phone dataset. We study the relationship between different types of interactions and show how it changes over time. We anticipate our findings to be of interest to all researchers working in the area of computational social science.

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Section: Introductionmentioning

confidence: 99%

Quantifying the differences incall detail records

Botta

2021

R. Soc. open sci.

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“…A CDR is an information structure storing applicable information about a given telephonic activity including a customer of a network framework. A CDR, as a rule, contains spatial and temporal data [2]. Therefore, it is also very important for network analysis and optimization.…”

Section: Introductionmentioning

confidence: 99%

A new method for traffic forecasting in urban wireless communication network

Zhang

Chuai

Gao

et al. 2019

J Wireless Com Network

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With the development of wireless devices and the increase of mobile users, the operator's focus has shifted from the construction of the communication network to the operation and maintenance of the network. Operators are eager to know the behavior of mobile networks and the real-time experience of users, which requires the using of historical data to accurately predict future network conditions. Big data analysis and computing which is widely adopted can be used as a solution. However, there are still some challenges in data analysis and prediction for mobile network optimization, such as the timeliness and accuracy of the prediction. This paper proposes a traffic analysis and prediction system which is suitable for urban wireless communication networks by combining actual call detail record (CDR) data analysis and multivariate prediction algorithms. Firstly, a spatial-temporal modeling is used for historical traffic data extracting. After that, causality analysis is applied to communication data analysis for the first time. Based on causal analysis, multivariate long short-term memory models are used to predict future data for CDR data. Finally, the prediction algorithm is used to process real data of different scenes in the city to verify the performance of the entire system.

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