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The growth in the number of mobile subscriptions has led to a substantial increase in the mobile network bandwidth demand. The mobile network operators need to provide enough resources to meet the huge network demand and provide a satisfactory level of Quality-of-Service (QoS) to their users. However, in order to reduce the cost, the network operators need an efficient network plan that helps them provide cost effective services with a high degree of QoS. To devise such a network plan, the network operators should have an in-depth insight into the characteristics of the network traffic. This paper applies the time-series analysis technique to decomposing the traffic of a commercial trial mobile network into components and identifying the significant factors that drive the traffic of the network. The analysis results are further used to enhance the accuracy of predicting the mobile traffic. In addition, this paper investigates the accuracy of machine learning techniques-Multi-Layer Perceptron (MLP), Multi-Layer Perceptron with Weight Decay (MLPWD), and Support Vector Machines (SVM)-to predict the components of the commercial trial mobile network traffic. The experimental results show that using different prediction models for different network traffic components increases the overall prediction accuracy up to 17%. The experimental results can help the network operators predict the future resource demands more accurately and facilitate provisioning and placement of the mobile network resources for effective resource management.

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Data analytics involves the process of data collection, data analysis, and report generation. Data mining workflow tools usually orchestrate this process. The data analysis step in this process further consists a series of machine learning algorithms. There exists a variety of data mining tools and machine learning algorithms. Each tool or algorithm has its own set of features that become factors to affect both functional and nonfunctional attributes of the system of data analytics. Given domain-specific requirements of data analytics, understanding the effects of these factors and their combinations provide a guideline of selecting workflow tools and machine learning algorithms. In this paper, we develop an empirical evaluation method based on the principle of Design of Experiment. We apply this method to evaluate data mining tools and machine learning algorithms towards building big data analytics for telecommunication monitoring data. Two case studies are conducted to provide insights of relations between the requirements of data analytics and the choice of a tool or algorithm in the context of data analysis workflows. The demonstration also shows that our evaluation method can facilitate the replication of this evaluation study, and can conveniently be expanded for evaluating other tools and algorithms.