## 5 Result and Discussion
### Limitations  
Our work is restricted to the new use case presented in Section 1 that seeks to obtain a configuration to derive a process model from a configurable process model that maximizes the conformance function for a given event log. In this context, our research presents the following limitations. First, a configurable process model that contains independent configurable nodes must be available, including a set of feasible configurations for each configurable node. Second, the event log must be representative of the executed process, i.e., it must contain traces representing all possible behaviors of the process; otherwise, the unobserved behavior could be left out of the derived process model. Notice that this is a common limitation of all process discovery techniques used in process  
Figure 19: Performance of the different strategies when varying the model complexity. (a) Exhaustive; (b) genetic; (c) greedy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{ \begin{tabular}{c} **Configurable** \\ **Model Topology** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Configurable** \\ **Node Dependencies** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Event** \\ **Log** \\ \end{tabular}  } & \multicolumn{4}{c}{**Exthaustive**} & \multicolumn{2}{c}{**Genetic**} & \multicolumn{2}{c}{**Greedy**} \\ \cline{5-10}  & & & **Time** & **Time** & **Time** & **Conformance** & **Time** & **Time** \\  & & & **(hhmmss)** & **Conformance** & **(hhmmss)** & **Conformance** \\ \hline \multirow{4}{*}{\(Q_{1}^{\text{G}}\)} & & & \(L_{1,1}\) & 00:08:15 & 0,98 & 00:01:46 & 0,98 & 00:00:04 & 0,98 \\  & & & \(L_{1,2}\) & 00:06:43 & 0,99 & 00:01:03 & 0,99 & 00:00:02 & 0,98 \\  & & & \(L_{1,3}\) & 01:24:07 & 0.84 & 00:02:42 & 0,84 & 00:00:05 & 0,84 \\  & & & \(L_{2,1}\) & 00:07:05 & 0,98 & 00:01:35 & 0,98 & 00:00:03 & 0,97 \\  & & & \(L_{2,2}\) & 00:05:25 & 0,98 & 00:01:19 & 0,98 & 00:00:02 & 0,97 \\  & & & \(L_{3,3}\) & 00:07:17 & 0,84 & 00:02:49 & 0,84 & 00:00:05 & 0,83 \\  & & & \(L_{3,1}\) & 00:07:51 & 0,98 & 00:02:15 & 0,98 & 00:00:05 & 0,98 \\  & & & \(L_{3,2}\) & 00:06:20 & 0,98 & 00:01:56 & 0,98 & 00:00:04 & 0,98 \\  & & & \(L_{3,3}\) & 00:08:18 & 0,89 & 00:03:37 & 0,89 & 00:00:06 & 0,87 \\ \hline \multirow{4}{*}{\(Q_{4}^{\text{G}}\)} & & & \(L_{4,1}\) & 00:47:51 & 0,93 & 01:01:16 & 0,93 & 00:00:25 & 0,93 \\  & & & \(L_{4,2}\) & 02:54:15 & 0,95 & 00:45:41 & 0,95 & 00:00:14 & 0,94 \\  & & & \(L_{4,3}\) & 04:49:52 & 0,87 & 01:25:55 & 0,87 & 00:00:47 & 0,87 \\  & & & \(L_{5,1}\) & 02:43:11 & 0,95 & 01:06:10 & 0,95 & 00:00:19 & 0,95 \\  & & & \(L_{5,2}\) & 01:57:22 & 0,95 & 00:48:31 & 0,95 & 00:00:09 & 0,95 \\  & & & \(L_{5,3}\) & 01:58:59 & 0,89 & 01:44:15 & 0,89 & 00:00:36 & 0,89 \\  & & & \(L_{6,1}\) & 01:59:99 & 0,94 & 00:49:10 & 0,94 & 00:00:24 & 0,94 \\  & & & \(L_{6,2}\) & 01:32:55 & 0,96 & 00:57:14 & 0,96 & 00:00:12 & 0,96 \\  & & & \(L_{6,3}\) & 02:49:35 & 0,87 & 01:24:52 & 0,87 & 00:00:43 & 0,89 \\ \hline \multirow{4}{*}{\(Q_{5}^{\text{G}}\)} & & & \(L_{7,3}\) & 04:32:13 & 0,93 & 01:02:32 & 0,93 & 00:00:31 & 0,92 \\  & & & \(L_{7,3}\) & 01:32:55 & 0,93 & 00:48:54 & 0,93 & 00:00:26 & 0,93 \\  & & & \(L_{7,3}\) & 05:52:15 & 0,82 & 02:26:41 mining. Third, each strategy has its own limitations, since the three proposed strategies seek to balance two opposing goals: to obtain optimal solutions versus to obtain solutions quickly. As observed in the analysis of Section 5.2, the exhaustive strategy allows one to obtain an optimal solution, but taking a considerable time. In contrast, the greedy strategy allows one to obtain a solution quickly, although not necessarily an optimal one. An intermediate approach is the genetic strategy, which allows one to obtain a very good, even potentially optimal, solution in a shorter time than the exhaustive strategy. Finally, the three strategies seek to find a single process model that maximizes the conformance function, which in turn weighs the four quality criteria.