# An Approach to Data Analysis in 5G Networks
## 3 SELFNET Analyzer Module Design
### Design Principles  
The following design principles and limitations lay the foundation of the Analyzer framework, as well as the implementation of its internal components:  
* _Big Data_. In order to deal with huge and homogeneous datasets, Big Data provides predictive algorithms, user behaviour analytics, and aggregation/correlation functionalities [39]. These capabilities are mainly taken into account in monitoring and aggregation tasks. The Analyzer Module deals with aggregated and correlated metrics, hence reducing the amount of information to be analysed. In our approach, the implementation of Big Data technologies to handle all this information is optional, leaving the decision of integrate these tools at the mercy of the SELFNET administrators, which driven by a better awareness of the use cases and the monitoring environment are more able to decide whether they are counterproductive or beneficial [40]. Because of this, our contribution is compatible with both Big Data and conventional techniques. * _Stationary Monitoring Environment_. According to [PERSON] et al. [41], in a stationary monitoring environment, the characteristics and distribution of the normal observations to be analysed match the reference sample population considered in the Analyzer learning processes. If the monitoring environment distribution is able to change representatively, it is considered non-stationary. Another problem that may reduce the quality of the analytics is the presence of gradual changes over time in the statistical characteristics of the class to which an observation belongs. In the literature this fluctuation is known as concept-drift. These problems are discussed at length in [42]. The assumption that the Analyzer Module operates on a stationary environment brings a simple and efficient solution, but prone to slight failures when the changes occur. On the other hand, to consider a non-stationary monitoring environment improves accuracy, but entails new challenges, among them: detection of changes, implementation of model/regression updating techniques, identifying when the calibration must be completed or selection of the samples that will be taken into account in new trainings. Given the complexity that this implies, the Analyzer Module assumes a stationary monitoring environment. The non-stationary approach will be part of future work. * _High Dimensional Data_. The analysis of high dimensional data implies to bear in mind data whose dimension is larger than dimensions considered in classical multivariate analysis. As indicated by [PERSON] et al. [43], when conventional methods deal with high dimensional data they are susceptible to suffer the well-known curse of dimensionality, where considering a large number of irrelevant, redundant and noisy attributes leads to important prediction errors. Hence operate with this data implies the need for more specific and complex algorithms. In terms of SELFNET this means that the vector of Health of Network Metrics (HoN) is large enough to consider the implementation of specific methods adapted to optimize the processing tasks of this kind of information. A priori there are no signs of SELFNET requiring processing an important amount of High Dimensional Data. Therefore, this paper does not take into account differences between conventional and High Dimensional data, assuming that the aggregation tasks will be able to optimize the amount of attributes to be analysed. * _Supervision_. SELFNET training mode. The analytical methods based on modeling/regression assume that new knowledge can be inferred from observations, by a prior learning stage. The learning process often requires reference data which allows identifying the most characteristic features of the monitoring environment, such as rules, boundaries, incidence matrices, direction vectors or basic statistics. Given the complexity involved in designing a SELFNET training mode, this approach describes how the information needed for the construction of new models is obtained. * _Centralized Design_.