# An Approach to Data Analysis in 5G Networks
## 6 Examples of Specification and Workflows
### UC 3: Payload Analysis
#### 6.3.4 Workflow  
At runtime, Aggregation layer notifies to the Analyzer Module facts related with the SG use case:  
\[ \begin{array}{l}Fa_{1}:\{O_{1}=FF217\mid 1\mid 1\mid Today\quad 12:22:17\mid ConexionA\}\\ Fa_{2}:\{O_{1}=FFFF\mid 1\mid 1\mid Today\quad 12:22:17\mid ConexionB\}\\ Fa_{3}:\{O_{1}=00 DE8\mid 1\mid 1\mid Today\quad 12:22:18\mid ConexionA\}\\ Fa_{4}:\{O_{1}=A4 FC9\mid 1\mid 1\mid Today\quad 12:22:18\mid ConexionA\}\\ Fa_{5}:\{O_{1}=FF218\mid 1\mid 1\mid Today\quad 12:22:19\mid ConexionC\}\\...\\ Fa_{36}:O_{1}=F0279\mid 1\mid 1\mid Today\quad 12:22:23\mid ConexionA\end{array} \]Each time a new payload is observed, the SELFNET Analyzer Module performs the pattern recognition actions \(PR_{1}\) and \(PR_{2}\). This returns new facts:  
\[\begin{array}{l}Fa_{1R1}:\{O_{2}=0.9\mid 1\mid 1\mid\mathit{Today}\quad 12:22:17\mid\mathit{ ConexionA}\}\\ Fa_{1R2}:\{O_{3}=0.2\mid 1\mid 1\mid\mathit{Today}\quad 12:22:17\mid\mathit{ ConexionA}\}\\ Fa_{2R1}:\{O_{2}=0.9\mid 1\mid 1\mid\mathit{Today}\quad 12:22:18\mid\mathit{ ConexionB}\}\\ Fa_{2R2}:\{O_{3}=0.18\mid 1\mid 1\mid\mathit{Today}\quad 12:22:18\mid\mathit{ ConexionB}\}\\ Fa_{3R1  
Note that SELFNET Analyzer Module is able to analyse information from heterogeneous sources such as SDN elements, virtual devices or metrics from specialized sensors. Another important characteristic is that the SELFNET Analyzer facilitates the incorporation of different analysis strategies, such as novel prediction or pattern recognition algorithms. This framework was developed to be able to operate indistinctly with very different data mining and machine learning paradigms, among them conventional information, big data or high dimensional data. The use of any of them does not imply design changes, being simply an implementation problem. As a result, this proposal is easily adaptable to future projects. The proposed data specification to accommodate the onboarding of new use cases is simple and adjustable. This is also corroborated by the fact that SELFNET has been able to incorporate every use case without modifications on the original definitions. This does not mean that in future use cases, more relevant changes will not be required. But without doubt, this robustness provides a solid base for design analytic schemes on similar contexts. Note that SELFNET implements a triad of services: self-protection, self-healing and self-optimization with completely different features and dependences (metrics, network devices to be monitored, prediction/pattern recognition algorithms, etc.). But despite these advantages, the proposal presents some weakness, most of them related with the limitations previously mentioned at the design principles (see Section 3). For example, the SELFNET Analyzer is not able to deal with complex stationary monitoring environments [42], where the quality of the analytics will decrease with time. Given the importance of this kind of scenarios on network environments, this is an aspect that must be studied. Another point to be keep in mind is that, according to the experience of the SELFNET consortium, the effectiveness of the Analyzer Module depends on the quality of their specification. It means that once deployed, the approach follows the guidelines provided by operators, which indicate what information should be processed, how it should be analysed and what results can be obtained from it. Despite of the simplicity of the proposed data specification, if the operator makes errors, there is a greatest chance of unexpected results. So in this sense, its robustness and scalability imply a high dependence on the quality of the specification inserted by operators where configuring the analyzer functionality. It is important to emphasize that loading new use cases is based only on configuration changes, without the need to modify the Analyzer implementation or to include additional software.