Big Data in Telecom Industry: Eﬀective Predictive Techniques on CDRs - ElElimy, Sara; Moustafa, Samir (EAI Endorsed Transactions on Smart Cities, 2020)
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).