## 5 Evaluation
### Further Analysis of Our Proposed Framework
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Event Log**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**Attribute Value Only**} & \multirow{2}{*}{**Our Framework**} \\  & **Redundant Activity Labels** & & & \\ \hline \multirow{8}{*}{Hospital Billing} & 5254 (1\%) & 0.72 & 0.71 & 0.93 \\  & 21,693 (5\%) & 0.74 & 0.69 & 0.92 \\  & 44,890 (10\%) & 0.70 & 0.66 & 0.84 \\  & 66,368 (15\%) & 0.78 & 0.67 & 0.87 \\  & 90,273 (20\%) & 0.71 & 0.66 & 0.86 \\  & 112,840 (25\%) & 0.69 & 0.65 & 0.86 \\  & 135,480 (30\%) & 0.67 & 0.63 & 0.87 \\ \hline \multirow{8}{*}{Sepsis} & 180 (1\%) & 0.66 & 0.60 & 0.82 \\  & 745 (5\%) & 0.63 & 0.55 & 0.81 \\  & 1569 (10\%) & 0.70 & 0.67 & 0.84 \\  & 2327 (15\%) & 0.65 & 0.58 & 0.81 \\  & 3086 (20\%) & 0.71 & 0.58 & 0.83 \\  & 3844 (25\%) & 0.69 & 0.66 & 0.82 \\  & 4605 (30\%) & 0.72 & 0.61 & 0.82 \\ \hline \multirow{8}{*}{Helpdesk} & 213 (1\%) & 0.73 & 0.66 & 0.92 \\  & 1067 (5\%) & 0.70 & 0.62 & 0.89 \\  & 2135 (10\%) & 0.77 & 0.58 & 0.90 \\ \cline{1-1}  & 3202 (15\%) & 0.71 & 0.60 & 0.87 \\ \cline{1-1}  & 4270 (20\%) & 0.71 & 0.62 & 0.88 \\ \cline{1-1}  & 5337 (25\%) & 0.65 & 0.70 & 0.85 \\ \cline{1-1}  & 6404 (30\%) & 0.68 & 0.65 & 0.84 \\ \hline \multirow{8}{*}{BPI Challenge 2012} & 2622 (1\%) & 0.58 & 0.65 & 0.86 \\ \cline{1-1}  & 13,110 (5\%) & 0.60 & 0.71 & 0.88 \\ \cline{1-1}  & 26,220 (10\%) & 0.65 & 0.63 & 0.83 \\ \cline{1-1}  & 39,330 (15\%) & 0.60 & 0.61 & 0.84 \\ \cline{1-1}  & 52,440 (20\%) & 0.70 & 0.68 & 0.88 \\ \cline{1-1}  & 65,550 (25\%) & 0.68 & 0.66 & 0.85 \\ \cline{1-1}  & 78,660 (30\%) & 0.63 & 0.63 & 0.82 \\ \hline \end{tabular}
\end{table} 
Table 3: Further analysis of our framework. Finally, we also conducted experiments to show how redundant activity labels can impact the discovered process models and how our framework can be used as a preprocessing tool to repair event logs. We selected two event logs with the highest (Hospital Billing) and lowest (Sepsis Log) detection f-score for the experiments. A simple and fundamental repair mechanism was adopted. Redundant activity labels were replaced with the most similar non-redundant activity labels in event logs. A mode of advanced repair technology is left as future work. We aimed to compare the f-scores for discovered process models on original event logs (no redundant activity labels exist), problematic event logs (with redundant activity labels), and repaired event logs by using the proposed detection framework with the basic repair mechanism. We used the Inductive Miner infrequent [21] to mine process models. We conducted conformance checking between all discovered process models against the original event logs. The more conformance there was between the process model discovered from the repaired event log and the original event log, the higher the f-score achieved. We used alignment-based fitness and conformance-based precision tools in the PM4 PY [48]. The f-scores for the original event logs are referenced from [15] directly. The results are presented in Table 4. We can see that the existence of redundant activity labels has a tremendous impact on the discovered process models. For instance, the f-scores drop significantly compared with the original f-scores. On the contrary, the f-scores on process models discovered from repaired event logs drop slower and remain closer to the original event log. The results indicate that our framework can be used as a preprocessing tool and help with improving the quality of process models when there are redundant activity labels in the event logs. Figure 8: F-score comparisons of our framework with the baseline using four event logs.