## 5 Result and Discussion
### Educational Scenario
It includes parallel flows that contain sub-processes. Having a reference process model, whose overall view is shown in Figure 10, and an academic planning event log per university, it was possible to derive a process model for each university. We applied the proposed exhaustive, genetic and greedy strategies to the three cases. The results are shown in Table 1, including fitness, precision and the conformance metric, calculated as 90% fitness and 10% precision. It is possible to observe in Table 1 that both Northern University and Central University have a high conformance and also a high fitness. We can assert that both universities have a high percentage of commonality with the configurable process model. However, that is not the case for Southern University, which has a lower conformance and a lower fitness. One of the reasons for this is the considerable amount of activities that are being executed at Southern University that are not found in the reference model. A decision maker would probably decide that the ERP system that is desired to acquire is not suitable for this university. Table 1 shows that all three strategies obtained process trees with the same fitness. However, the greedy strategy obtained a process tree with a lower precision. Taking the Northern University as an example, a qualitative analysis between the process tree obtained by the exhaustive strategy and the greedy strategy suggests that the exhaustive strategy finds a configuration of the model where the activity assign classroom automatically (ACA) is always performed (as observed in the event log), while the greedy strategy finds a configuration of the model where the activity ACA can either be performed or skipped (that was never observed in the event log), obtaining a lower precision. The performance of the three strategies is depicted in Figure 12. It is possible to observe that the time required by the exhaustive strategy is considerably higher than the time required by the genetic and greedy strategies. Moreover, the greedy approach was able to obtain a derived process model in seconds, whereas the other two algorithms required minutes and even hours. The time required \begin{table}
\begin{tabular}{l c c c c c c c c} \hline  & \multicolumn{3}{c}{**Northern**} & \multicolumn{3}{c}{**Central**} & \multicolumn{3}{c}{**Southern**} \\ \cline{2-9}  & **Fitness** & **Precision** & **Conformance** & **Fitness** & **Precision** & **Conformance** & **Fitness** & **Precision** & **Conformance** \\ \hline Exhaustive & 0.981 & 0.398 & **0.922** & 0.986 & 0.510 & **0.939** & 0.690 & 0.510 & **0.672** \\ Genetic & 0.981 & 0.398 & **0.922** & 0.986 & 0.510 & **0.939** & 0.690 & 0.510 & **0.672** \\ Greedy & 0.981 & 0.370 & **0.920** & 0.986 & 0.500 & **0.938** & 0.690 & 0.500 & **0.671** \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of the proposed strategies for the three universities. Figure 11: General idea of process model derivation for three different configurable sub-processes. in the case of the Southern University is higher due to the alignment technique used to obtain the conformance metric. As was mentioned before, at the Southern University, many activities are being executed that are not found in the reference model; in those cases, the alignment technique takes longer [34]. In conclusion, the greedy strategy is able to obtain in a short time a derived process model that has a quite good conformance, in comparison to the conformance obtained with the other two strategies.