# Big Data Perspective and Challenges in Next Generation Networks
## 3 Data Analytics Perspective on Next Generation Networks
### Significance of Big Data Analytics  
Data analytics is not a new phenomenon as data analytics algorithms have been in use for the last few decades. However, typical data analytics algorithms cannot be used for tackling big data. Thus, for big data handling, there should be novel big data analytics and advanced machine learning techniques such as deep learning, neural networks, representation lerning, transfer learning, active learning and online learning. Due to implementation of such type of techniques, we can efficiently process big data and make decisions accordingly. Raw data can be converted into right actionable data with the help of such analytics techniques. Recently, some frameworks have been proposed which shows the significance of big data and big data analytics in next generation networks. We discuss these models below. The flow and significance of big data in 5G networks can be best explained in Figure 5.  
Figure 5 represents flow structure of big data for next generation networks optimization. It is a closed-loop process and shows that, firstly, big data are collected from user's equipment as well as from a network. After collection of such huge data, storage and management of big data are also critical tasks. The data that are collected by the collection unit are in the raw form. These raw data include irregularities, misleading patterns, and noise. In this form, data cannot be sent to big data analytics unit for processing. Thus, big data storage/management unit performs data pre-processing and filtering to make the data ready for data analytics unit. On pre-processed and filtered data, \begin{table}
\begin{tabular}{l l l} \hline \hline
**Standard/Technology Enabler** & **References** & **Vision** \\ \hline
5 SNOW & [41] & Ultra-low latency, Concept of unified frame structure, Feasible/attainable waveform structure for 5G \\
5 GPP & [42] & Ubiquitous infrastructure with high coverage tendency \\ METIS & [43; 44] & Scalable radio access node (RAN)architecture \\ Samusung Electronics & [45] & Diverse range of billions of connected devices (IOT) \\ Nokia & [46] & Support up to 100 times more capacity, Self-aware network \\ Huawei & [47] & Ultra-high capacity/connectivity, Efficient network deployment \\ Ericsson & [48] & Legacy with the existing networks, Ubiquitous services and applications \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Standards and technology enablers for 5G. intelligent decision-making task is performed by the big data analytics unit. In this unit, advanced data analytics and machine learning algorithms are used for transformation of data into useful data. Finally, useful or actionable data are fetched to network optimization unit. As it is a closed loop process, the data evolved from the network further fetch to big data storage unit and so on. In this way, this closed loop phenomena works and network optimizes. Recently, [PERSON] et al. [51] presented a similar closed loop based phenomena for proactive caching and pushing technology. The authors in [51] described that the maximum utilization of spectrum and improvement in capacity gain is made possible by accurately predicting the user demands, with aid from big data analytics. Similar idea is also presented in [52]. For optimizing next-generation networks, [PERSON] et al. [5] proposed a big data-driven model. The proposed model consists of four layers: _data acquisition layer_, _data management and storage layer_, _data analyzing layer_ and _network optimization layer_. In the layered model, data are collected from user's equipment, core network, radio access nodes and Internet service providers. Once collected, the data are shifted to management and storage layer, where performance and capacity can be scaled. This layer performs storing and management operation. After storing the data, the next hindrance is to analyze the massive data and extract meaningful information from it. To transform raw data in to actionable data, machine learning and data analytics techniques are implemented.