## 5 Result and Discussion
### Algorithms' Performance Based on an Empirical Evaluation
#### 5.4.1 Performance According to Log Complexity
As previously mentioned, this is a feature of the conformance method applied to compute the conformance metric. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **\#Trace Variants** & **\#Trace Repetitions** & \begin{tabular}{c} **\#Traces in the Event Log** \\ **for a Target Model A** \\ \end{tabular} & \
\begin{tabular}{c} **\#Traces in the Event Log** \\ **for a Target Model B** \\ \end{tabular} &
\begin{tabular}{c} **\#Traces in the Event Log** \\ **for a Target Model C** \\ \end{tabular} \\ \hline \multirow{3}{*}{Different trace variants} & 50 & & 747 & 500 & 500 \\  & 100 & 10-20 & 1466 & 1543 & 1000 \\  & 500 & 7448 & 7456 & 7450 \\ \cline{2-6}  & & 10 & 1000 & 1000 & 1000 \\ \cline{2-6}  & 100 & 20 & 2000 & 2000 & 2000 \\ \cline{2-6}  & & 50 & 5000 & 5000 & 5000 \\ \cline{2-6}  & & 100 & 10,000 & 10,000 & 10,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Different log settings for three universities. Figure 16: Performance of the exhaustive strategy when varying log complexity. (**a**) Trace variants’ complexity; (**b**) trace repetitions’ complexity. Greedy strategy evaluation:  
The greedy strategy uses a local subtree configuration heuristic to derive the best process subtree for every configurable node. This strategy found a reasonable configuration and the corresponding derived process model, in all experiments. The solutions are similar to the optimal derived process models obtained by the exhaustive and genetic strategies. Figure 18a shows that the computing time varies linearly with the number of trace variants. It also illustrates that the greedy algorithm is very fast; it took about 7 min in the most complex scenario, corresponding to the university \(C\) with 500 trace variants. Figure 18b illustrates a slight ascending computing time when increasing the number of trace repetitions while keeping the number of trace variants constant. In summary, the greedy strategy finds a reasonable derived process model, which is very close to the optimal process model in a short computing time, whereas the exhaustive and genetic strategies find an optimal derived process model, but require more computing time. In all cases, the performances of the algorithms vary linearly with the number of trace variants, and this does not depend on the number of trace repetitions. In addition, Table 8 shows that the genetic strategy obtains the same conformance as the exhaustive strategy in all experiments for the three universities, except in one case (100t 10 rep), in which the results are quite similar. By contrast, the conformance of the greedy strategy is below the conformance obtained by the other two strategies. The strategies applied to the event log  
Figure 17: Performance of the genetic strategy when varying log complexity. (**a**) Trace variants complexity; (**b**) trace repetitions’ complexity. Figure 18: Performance of the greedy strategy when varying the log complexity. (**a**) Trace variants’ complexity; (**b**) trace repetitions’ complexity. of the university \(C\) do not reach 0.8 of conformance, meaning that providing a model for this location could be reconsidered.