# An Approach to Data Analysis in 5G Networks
## 2 Background
### Diagnosis Capabilities in 5G Networks  
A 5G network envisages an architecture able to cover three main domains [1]: (i) enhancement of radio capabilities to enable the spectrum optimization, the interference coordination and cost-effective dense deployments; (ii) provisioning of an effective network management environment to create and deploy a common core to support several use cases in a cost-effective manner; and (iii) simplification of the system operations by means of automated procedures, where the introduction of new capabilities or network functions should not imply increased complexity on operations and management tasks. In order to tackle these requirements, 5G networks take advantage of the separation between data and control plane (network programmability) offered by SDN architectures [23], the deployment of virtualized network functions, the scalability and flexibility in the service provisioning based on cloud environments, enabling high capacity and massive communications (cognitive radio, carrier aggregation, Machine to Machine Communication), spectrum and resource optimization (millimeter wave and massive Multiple Input Multiple Output (MIMO)) and intelligent capabilities provided by artificial intelligence or self-organization concepts [24; 25]. In particular, the introduction of analysis and intelligent capabilities [8; 19] could be applied to several domains such as autonomic network maintenance, automation in the provisioning of services, prediction and remediation of congestion or queue utilization, detection of security threats, improving network efficiency, multi cell coordination, provisioning of high QoS and QoE for services, etc. For this purpose, analysis and intelligent capabilities allow to response to network problems based on pattern recognition, the dynamic smart selection of the best location where the services can be deployed or migrated, sharing and releasing of resources based on forecasting methods, building of context awareness models based on real time information from the network, its devices and applications. In order to provide intelligence and facilitate the decision-making process, some tasks must be performed. On one hand, analysis stage is intended to perform the identification of network situations and events. These situations do not necessarily imply (a priori) a harmful nature. On the other hand, the decision-making task determines if a specific situation is a risk for the network health, or its components, and then it performs the respective countermeasures. In this context, traditional approaches apply different analysis and reasoning techniques, such as Bayesian Networks (BN) [26], in order to provide intelligent to common network management tasks. However, these models are not sufficient to guarantee the network performance according to SLAs and future 5G user needs [1]. There are some proposals to address the data analysis in 5G systems and its elements such as access and radio components [13; 27], network devices [28], cloud elements [29] and resource allocation [30]. In [31], a prototype to perform mobile network analysis based on Markov Logic Network (MLN) and semantic web technologies is presented. This approach allows the optimization and network status characterization but does not explain how it cover heterogeneous data sources. For its part, [PERSON] et al. [10] proposes a framework to provide a full view of network status based on machine learning and big data concepts. To this end, their proposal predicts the user behaviour and dynamically associate the network response to the network parameters. However it is doesn't specify how to deal with SDN or NFV components. Meanwhile, there are reports [1; 32] and projects [21; 33; 34; 35; 36; 37] that introduce analysis capacities to cover 5G requirements. In this way, METIS Project [28] takes into account SON concept in order to provide a new level of adaptability to 5G infrastructures.