Big Data in Telecom Industry: Effective Predictive Techniques on CDRs

ElElimy, Sara and Moustafa, Samir (2020) Big Data in Telecom Industry: Effective Predictive Techniques on CDRs. EAI Endorsed Transactions on Smart Cities, 20 (11): e1. ISSN 2518-3893

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Abstract

Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since the mobile network operators have considered a source of big data traditional techniques are not effective with new era big data, internet of things (IoT) and 5G, as a result handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution(LTE) to 5G therefore, there is an urgent need for sufficient big data analytic to predict future demands, traffic, and network performance to fulfill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the auto-regressive integrated moving average(ARIMA) Bayesian-based curve fitting, and recurrent neural network(RNN) is employed for a data-driven application to mobile network operators. The main framework included in models is an identification parameter of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italian Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using specific well-known evaluation criteria that show that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset as the RNN (deep learning model).

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 09 Sep 2020 09:05
Last Modified: 09 Sep 2020 09:05
URI: https://eprints.eudl.eu/id/eprint/93

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