# Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach
## 3 Methodological Framework for Applying Process Mining in Practice
### Generalization of Data  
During the data analysis, two other situations of repetition were found where events could not be removed. Instead, they needed to be generalized. The first case was the activities with different event IDs but the same activity names; this issue was resolved by keeping the one with a high occurrence. In these data, event IDs 1 and 2 have the same activity name 'purchase requisite generation'. Table 7 shows that 2 has a higher occurrence than 1, i.e., 25.1%. Thus, the activity with ID 2 was kept. The second issue was related to those cases with the same event ID but a different activity name, and its solution was to keep the one with a high occurrence. For example, ID 5 has two activity names, 'purchase  
\begin{table}
\begin{tabular}{c c c c} \hline
**SR\#** & **Activity** & **Frequency** & **Relative Frequency** \\ \hline
1 & Purchase requisite generation & & 25.41\% \\
2 & Requisite budget confirmation & 7026 & 24.76\% \\
3 & Requisite recommendation I & 7023 & 24.75\% \\
4 & Purchase requisite approved I & 7012 & 24.71\% \\
5 & Requisite budget reservation & 95 & 0.33\% \\
6 & Purchase requisite approved II & 11 & 0.04\% \\ \hline \end{tabular}
\end{table}
Table 6: Activity statistics after removing repetition of an activity.  
\begin{table}
\begin{tabular}{c c c} \hline
**SR\#** & **Event ID** & **Frequency** & **Relative Frequency** \\ \hline
1 & 4 & 7121 & 25.1\% \\
2 & 2 & 7023 & 24.75\% \\
3 & 3 & 7023 & 24.75\% \\
4 & 5 & 7023 & 24.75\% \\
5 & 1 & 186 & 0.66\% \\ \hline \end{tabular}
\end{table}
Table 7: Statistics after removing repetition of an activity.  
\begin{table}
\begin{tabular}{c c c} \hline
**SR\#** & **Attribute** & **Statistics** \\ \hline
1 & Total no. of events & 38,746 \\
2 & Total no. of cases & 8276 \\
3 & Total no. of event classes & 6 \\
4 & Maximum events per case & 35 \\
5 & Average events per case & 5 \\
6 & Minimum events per case & 4 \\
7 & No. of start events & 1 \\
8 & No. of end events & 2 \\
9 & Total no. of variants & 204 \\ \hline \end{tabular}
\end{table}
Table 5: Statistics of log data after removing incomplete traces.  
requisite approved I' and 'purchase requisite approved II'. Table 6 shows that 'purchase requisite approved I' covers 24.75% of the data, and 'purchase requisite approved II' covers 0.04% of the data. The first approval was kept because of its high occurrence. There was another similar case in the data where ID 4 has two different activities,'requisite budget confirmation' and'requisite budget reservation'.  
In comparison with Event ID 5, both the activities have different connotations, so we did not generalize; instead, we kept both these activities. A summary of highlighted issues in real-life log data is given in Table 8. This analysis aims to provide insight into how the identified quality problems exist in the actual data used for process mining. After removing noise and outliers from the event log, it was now in the form where process mining techniques and algorithms could be applied. Table 9 shows the global statistics of the noise-free log. As mentioned above, there were 4 activities in our final log data to gain insight into these activities.  
Table 10 presents these activities with their event IDs, frequency, and relative frequency. The following table shows two activities across event ID 4 because they have different meanings and cannot be removed or ignored.  
After the detailed analysis of log data and after removing noise from it, in the next section, different process mining algorithms are applied to visualize the organization's procurement process flow.