## 5 Result and Discussion
### Real-Life Experiments  
In order to evaluate the performance of the techniques on real-life data, we apply all three strategies on a real-life dataset. The data used contain event data from the building permit process (WABO) of five Dutch municipalities [17; 35]. There are five event logs, each describing a different process variant, which were extracted from the IT systems of the corresponding municipality. The same data have been used to evaluate the performance of configurable process discovery of the Evolutionary Tree Miner (ETM) [20] (pp.250-251). In this experiment, we create some CPTs based on two of the models discovered by the ETM on this dataset. We then allow each of the three strategies to configure the CPTs to come to the best solution they can achieve. We then compare these results with the results obtained by the ETM on the same dataset. We use CPTs based on the models discovered by the ETM on this dataset, which are shown in Figure 13. Figure 14 shows the two CPTs created based on the model shown in Figure 13a. The CPT in Figure 14a has two configurable nodes, while the CPT in Figure 14b has eight configurable nodes, all randomly chosen. Notice that we could not create a CPT similar to Figure 13a because we are not considering the downgrade operator used in [20]. Figure 15 shows the three CPTs created based on the model shown in Figure 13b. The CPT in Figure 15a is equivalent to the one shown in Figure 13b. The CPT in Figure 15b has six configurable nodes, while the model in Figure 15c has twelve configurable nodes, all randomly chosen. All configurable nodes can be set to enable (or allow), hide or block. We allow each of the three strategies to configure all models to come to the best solution they can achieve. We then compare these results with the results obtained by the ETM on the same dataset. The results of the experiments with the CPTs created based on the model discovered by the ETM Approach 3 are shown in Tables 2 and 3. Tables 4-6 show the results of the experiments with the CPTs created based on the model discovered by the ETM Approach 4. All tables include fitness, precision and the conformance metric, calculated as 90% fitness and 10% precision. Figure 12: Benchmarking of the performance of the three strategies. Figure 14: CPTs based on the model discovered by the ETM Approach 3. (**a**) CPT 3-A contains two configurable nodes; (**b**) CPT 3-B contains eight configurable nodes. Figure 13: Process trees originally discovered by the evolutionary tree miner (ETM) on the building permits process event logs. (**a**) ETM Approach 3; (**b**) ETM Approach 4. Figure 15: CPTs based on the model discovered by the ETM Approach 4. (**a**) CPT 4-A contains one configurable node; (**b**) CPT 4-B contains six configurable nodes; (**c**) CPT 4-C contains 12 configurable nodes. It can be observed that in all cases, the different strategies obtain good results. Since the exhaustive strategy always obtains the best possible result, we can highlight that the genetic strategy always finds the best result and that the greedy strategy in general obtains very good results, in many cases matching those obtained with the exhaustive strategy. When comparing the results with those obtained by the ETM, we can point out that in the only case where the CPT is equivalent to the one used by the ETM (Table 4 corresponding to CPT 4-A, shown in Figure 15a), the results obtained with the ETM and the results obtained with the exhaustive