With the collection of massive amounts of data every day, big data analytics has emerged as an important trend for many organizations. These collected data can contain important information that may be key to solving wide-ranging problems, such as cyber security, marketing, healthcare, and fraud. To analyze their large volumes of data for business analyses and decisions, large companies, such as Facebook and Google, adopt analytics. Such analyses and decisions impact existing and future technology. In this paper, we explore how big data analytics is utilized as a technique for solving problems of complex and unstructured data using such technologies as Hadoop, Spark, and MapReduce. We also discuss the data challenges introduced by big data according to the literature, including its six V's. Moreover, we investigate case studies of big data analytics on various techniques of such analytics, namely, text, voice, video, and network analytics. We conclude that big data analytics can bring positive changes in many fields, such as education, military, healthcare, politics, business, agriculture, banking, and marketing, in the future.INDEX TERMS Big data analytics, data analytics, deep learning, machine learning.

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Telecom operators are aiming to provide high-grade data, multimedia applications and low latency videos for smart devices. As today's mobile data is experiencing rapid growth and the usage of smart devices are fabricating unparalleled challenges for telecom operators to meet the global bandwidth requirement. From the first generation to fourth generation, the technology evolution is predominantly governed by the hardware side but now it is moving towards the concept of cognitive network management, resource orchestration and machine learning-based solutions. In this paper, we propose the adaptive capacity and frequency optimization (ACFO) method for adaptive optimization based on time series forecasting approach. The daily capacity utilization of microwave (MW) links is analyzed to use forecasted demand. Based on the projected demand, the capacity and frequency optimization will be executed. The two main forecasting models 1) SARIMA and 2) MLP are used and for performance evaluation, we used RMSE and MAPE criterion. The analytic outcomes show that MLP with two layers and six hidden nodes (6/6) are good enough to achieve the desired results. In some cases, we need to exceed the hidden nodes up to fifteen (15/15). By using the forecasting approach, the reactive optimization will successfully shift to the predicted/ proactive optimization, will balance the resource distribution and can condense the wastage of resources. The outcome of the study will be a contribution to the dynamic resource optimization in wireless backhaul network. INDEX TERMS Time series forecasting, capacity planning, dynamic resource optimization, wireless backhaul optimization.

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