# From Cyber-Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs
## 8 Big Data and Big Data Analytics--The Final Frontier
### Big Data-Inspired, Edge-Based DT for Real-Time Network Diagnostics  
In the big data and big data analytics context, a DT can be designed for the following purposes: (i) monitoring network operations, (ii) planning predictive maintenance, (iii) improving customer services, as well as (iv) resource management, i.e., optimizing RA [128]. Network operations in the current generation are already characterized by specifications of higher demands for data rates and higher quality of experience (QoE) on the user side. This, paralleled with the requirements for low complexity network architecture, and the continued reduction of costs on the RAN and network core, suggests that MNOs need to begin instantiating predictive analytics. Through the use of edge-based DT-based analytics, which entails the automation of network operations and maintenance through data analytics, network disruptions can be avoided and better QoE can be afforded to end users. In order to achieve this level of network automation, gNB functions and core network functions can be virtualized and arranged at the edge of the network to offer problem-specific solutions. A type of function virtualization for edge network diagnostics and prescriptive maintenance through intelligent virtual assistants is shown in Figure 7 below. Illustrated in Figure 7 above is a cloud-edge computing strategy to track QoE and service disruptions, whereby service providers can replace expensive site visits by running their networks with NFs using virtual machines (VMs) running on standard hardware at the network edge. MNOs might already be using the DT technology without them knowing, such as in customer service. For instance, a DT can be used in monitoring network conditions as a proxy measure of customer experience, where the origin of the issues affecting users can be predicted and addressed. Due to the need to deliver more personalized, anticipatory, and consistent services across all the physical assets in all network segments, the DT technology has a lot of potential in the customer service departments of MNOs [129]. Predictive and prescriptive analytics can be exploited in improving customer service. Service disruptions can be avoided by performing system diagnostics and making important maintenance decisions through customer service. Through the use of data mining techniques, DTs can enable better customer service by providing differentiated levels of service through the data obtained from customer reviews. To shed some light on this concept, an illustrative example is as follows:  
**Example 3**. : _Suppose the performance of the network has degraded to the point of inconveniencing network users. In this case, urgent and prompt intervention from the MNO is required. As shown in Figure 7 above, the service degradation from the subscribers that might be in the form of (i) poor network connectivity, (ii) high number of dropped calls, and (iii) high service termination can be intelligently detected and addressed before the subscribers begin logging their complaints. From a technical perspective, the kinds of complaints listed above may not differ greatly from one another, but based on proper analytics, they might require different technical solutions. On the other hand, from a customer service perspective, the similarities and/or differences in the complaints might not be clear, hence the requirement for proper data analytics to extract relevant features. For instance, if data mining techniques are used in mining the features from text and visualization techniques used, the best possible network diagnostics can be carried out and the accurate solution can be prescribed. Based on the complaints listed above, the corresponding problems might be (i) signal booster failure, (ii) operating update issues, and (iii) contract data cap limits [130].