## 5 Experiments and Results
### Application Scenario Test  
To test the J48 classification algorithm implemented in the PRINM DM mechanism from the attributes and their values of Figures 4 and 5, the application scenario described in Section 4 was used. The predictive attributes used in the application scenario were the same as those used to generate the artificial Target, Period, and Setting datasets. Based on this, the J48 algorithm was trained with the three artificial datasets, thus, creating three decision trees that classify for which user, period, and device configuration notifications must be notified throughout the application scenario. The Target set tree has a size of 36 nodes, 27 of these are leaf nodes. The tree of the Period set has a size of 235 nodes, 169 of these are leaf nodes. Finally, the tree of the Setting set had a size of 224 nodes, 159 of these are leaf nodes. Predictive data were collected from an artificial and a real source. For the artificial source, the data were collected from the users' actions in the application scenario for the values of the attributes user, profile, environment, and activity. For the real source, data were collected from the mobile _PRISER: Notification Collector_ application developed by [38] for the values of the attributes status, inPeriod, and inTarget. Data from both sources were merged, thus, generating three unclassified datasets (test sets). Artificial source data are presented in Tables 11-13 for each user. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Period** & **Profile** & **Environment** & **Activity** \\ \hline
10:10–10:25 \(|\) Morning & Guest & Show Room \(|\) Public & 1 \\
10:25–11:50 \(|\) Morning & Guest & Sector of Sales \(|\) Private & 2 \\
16:00–16:15 \(|\) Afternoon & Guest & Show Room \(|\) Public & 1 \\
16:15–16:30 \(|\) Afternoon & Guest & Sector of Sales \(|\) Private & 2 \\
16:30–18:00 \(|\) Afternoon & Basic & Office \(|\) Restrict & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Customer user data. Figure 9: Friedman test. Real source data was collected by the mobile application installed on three different mobile devices, representing the three users of the application scenario. The application collected notifications from the three mobile phones for 24 h, generating information in the JSON format of each notification. For each JSON, only the information corresponding to the attributes status, inPeriod, and inTarget were extracted, which coincide with the periods of each user within the dealership. Therefore, they were extracted for the client user from 10:10 a.m. until 11:50 a.m. and 04:00 p.m. until 06:00 p.m., for the employee user from 7:00 a.m. until 06:00 p.m., and the owner user from 7:30 a.m. until 10:00 p.m. Other notifications outside these times of each user was discarded. Therefore, 1377 notifications were received within the defined times for the client user (mobile phone 1), of these, 209 were extracted; 813 for the employee user (mobile phone 2), of these, 399 were extracted; and 413 for the owner user (mobile phone 3), of these, 333 were extracted. The application scenario test aims to analyze the behavior and classification performance of the J48 algorithm on the classification accuracy metric using predictive data from the test sets and classifying them. However, after merging the predictive data from the two sources for generating test sets, they were also merged into them, specifically, data classes following the same logic as the script that generated the artificial dataset Target, Period, and Setting. This was necessary because it would not be possible to demonstrate all the values of the class attributes that the J48 algorithm would classify from the predictive data of the test sets in the application scenario. Therefore, the test sets merged with the client, employee, and owner user class data were applied to the decision trees created by the J48 algorithm. The results presented show that the J48 algorithm was able to classify with 100% accuracy new unclassified data inserted in each decision tree.