## 5 Evaluation
### Comparing with The Existing Method  
Currently, three state-of-the-art approaches are proposed for detecting redundant activity labels in event logs [11; 13; 14]. However, the approaches in [13; 14] are an interactive detection approach, which requires domain experts. Hence, for the baseline approach, we selected the SynonymousLabelRepair [11], which seems to be more advanced in handling redundant activity labels and requires less domain knowledge than the other methods. We used its default settings in evaluations, which are a 0.7 threshold and a uniform weight, as the optimal weight settings required extra domain knowledge.  
We followed the evaluation pipeline proposed in [11] for the first group of experiments, which aimed at comparing the performance of our proposed framework with the existing method to detect redundant activity labels in event logs, as shown in Figure 5. The \"Hospital Billing\" and \"Sepsis\" event logs were evaluated by SynonymousLabelRepair in [11]. In total, 70 event logs were generated and evaluated in the experiment. Hence, in this section, we compare our framework with the SynonymousLabelRepair by using the above event logs.  
Table 2 along with Figure 6 and 7 show the results for the evaluation. It is clear that our framework outperforms the baseline approach. The f-scores of our framework are all above 0.8 for the event logs under different settings, which indicates that almost all redundant activity labels can be successfully detected regardless of their occurrence frequencies in event logs. On the contrary, the average f-scores for the baseline were merely 0.33 and 0.43 in the experiments. Moreover, the baseline performs poorly when the redundant activity labels are less frequent. For instance, the baseline only achieves a 0.08 f-score when there are 1% redundant activity labels in the \"Hospital Billing\" event log. We also noticed that the baseline begins to catch up and even surpasses our framework in recall when redundant activity labels become more frequent in the \"Hospital Billing\" event log, which shows that both approaches can successfully detect most of the redundant activity labels. However, the low precision values indicate that the baseline constantly produces false-positive results (i.e., non-redundant activity labels are falsely detected as redundant), which further proves the necessity and effectiveness of our multi-view framework.  
The baseline approach only compares directly-follows relations while ignoring their frequency distributions and indirectly-follows relations. However, low-frequency activity labels rarely contain all directly-follows relations while only maintaining the main one. In this case, frequency distributions of the control-flow relations are becoming important in the approach. We also notice that the control-flow view is limited when handling activity labels that share XOR relations, such that they have identical incoming and outgoing relations. This further illustrates the necessity to also consider other views when detecting the redundant activity labels. Furthermore, the cost function between different activity  
Figure 5: Overview of the evaluation pipeline.  
labels in EMD can be better defined instead of simply adopting the unit cost if domain knowledge is available. Thus, more satisfying results can be potentially achieved by our framework. Additionally, the baseline relies on a probability density function to assess value distributions for activity attributes. However, distributions of attribute values are less structured when redundant activity labels are infrequent and mixed with categorical and numerical values.