# On Predicting Ticket Reopening for Improving Customer Service in 5G Fiber Optic Networks
## 2 Data Collection, Data Preparation and Analytical Base Table  
With the aim of effectively predicting ticket reopening relative to the QoS perceived in 5G fiber optic networks--namely, according to the emerging FWA paradigm--the methodological approach inspiring this work can be structured as follows. * A first _data collection_ and _data preparation_ effort were made, by collecting, aggregating, cleaning and preparing the relevant data for the subsequent processing phase, resulting in a homogeneous ABT to be fed to the predictive analytics/classification engine that was subsequently designed and developed. This first activity was carried out in the framework of an extensive joint research initiative on Next Generation Networks between ELIS Innovation Hub and Vodafone over the years 2018-2021. * Then, the _KPI definition_ was necessary, that is, the identification of the relevant KPIs that allows for quantifying the benefits that are ultimately yielded by the adoption of the proposed predictive analytics engine. In this respect, we chose to evaluate the performance of the proposed predictive analytics engine in terms of accuracy, Gini coefficient, Youden index, and Area Under the ROC Curve (AUC). * The design and training of the _predictive analytics engine_ followed, together with the evaluation of the performance obtained on a suitable test dataset. In more detail, the relevant input data are collected from two heterogeneous data sources,  
1. the former related to the Virtual Unbundled Local Access (VULA) technology--VULA offers a means to any licensed network operator to effectively join the ultrabroadband network infrastructure of the backbone provider by virtually accessing the last mile only;
2. the latter related to the Sub-Loop Unbundling (SLU) technology--SLU is alternative to VULA, yet it joins the network infrastructure of the backbone provider only at the copper section, thus exhibiting lower performance than VULA. The _original_ dataset \(X\) is therefore represented by an \(m\times n\times t\) matrix aggregating the inputs from both VULA and SLU technologies. After being suitably cleaned, it reports \(n=307\) network QoS parameters collected over a period of \(t=30\) days of intensive usage for a group of \(m=600\),000 users. The \(i\)-th row of \(X\) (for \(i=1,\ldots,m\)),  
\[x_{i}=\left(x_{u,v}^{i}\right)_{ \begin{subarray}{c}u=1,\ldots,n\\ v=1,\ldots,\ t\end{subarray} }, \tag{1}\]  
associated with a user \(i\) that the customer service has already closed a ticket for (since the aim of the paper is to predict ticket _reopening_), accounts for the values of the \(n\) network QoS parameters perceived by user \(i\) on day \(v\). Among the \(m\) users, in the considered period, 25% reopened a ticket due to a technical issue that is still unsolved even though a ticket in that respect had already been closed. In particular, data collection was carried out with a specific data matching criterion: namely, in the case of a reopened ticket, only the last data point--i.e., the last values of the \(n\) features--that is, the temporally closest to ticket reopening for the considered user is collected and stored in the dataset \(X\), whereas, for all other tickets--that have already been closed and have not been reopened yet--all data in the time interval between ticket closing and the last possible sampling instant are collected and stored in the dataset \(X\). In order to obtain a smaller dataset, with reduced dimensionality (\(p<n=307\)) but with very similar information content, we first performed feature reduction on the original dataset \(X\) by removing all features with very low variance, i.e., proving to be redundant when it comes to estimating the probability of ticket reopening. Then, we carried out a linear correlation analysis among the features which, however, did not yield any relevant results; this is why we chose to resort to _hierarchical clustering_ in order to shed light on any existing nonlinear correlations.