# Process Discovery for Event Logs with Multi-Occurrence Event Types
## 4 Test Experiments
### Tests on Real Data  
In this section, we will use the same benchmark datasets as the paper on GNN-based process discovery [66]. The source of the real datasets is the BLP challenge. BPI Challenge datasets ([[https://www.tf-pm.org/competitions-awards/bpi-challenge](https://www.tf-pm.org/competitions-awards/bpi-challenge)]([https://www.tf-pm.org/competitions-awards/bpi-challenge](https://www.tf-pm.org/competitions-awards/bpi-challenge)), accessed on 3 January 2025) have become important benchmarks in the community. For the presentation, we selected the following three datasets:  
- BPI_2012_O  
- BPI_2017_O  
- BPI_2020_Permit_log  
The size parameters of the data sets are summarized in Table 3.  
The key problem in the resulting schema of the inductive miner (Figure 21) is that it is very over-generalized; for example, it allows a loop f->f or a -a, which is not part of the incoming traces. As the generated schema shows GNN miner (Figure 22) performed a very high level of reduction, some frequent traces are not covered by the schema. For example, there are traces where the last step is O_CANCELED (code: f). Our proposed (Figure 23) method generates a more compact schema; it distinguished four subtypes of the activity d (d_3, d_6, d_7, and d_8) because these occurrences have very different neighborhood contexts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Number of Traces** & **Number of Event Types** & **Average Trace Length** \\ \hline BPL\_2012\_O & 31,244 & 8 & 7.6 \\ BPI\_2017\_O & 31,509 & 8 & 8.2 \\ BPI\_220\_Permit\_log & 7065 & 18 & 11.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset parameters.  
Figure 20: Result schema by the proposed method for T5.  
T7: BPI_2017_O dataset  
The result of the heuristic method (Figure 24) contains some incorrect structure sections and the schema generated by GNN method (Figure 25) is over simplified. For example, neither of these methods discover the repetition of the sequence CREATE_OFFSET (code: a) \(\rightarrow\) CREATED (code: b). On the other hand, the proposed method discovers that the event CANCELED (code: f) can be repeated only in specific contexts; thus, it generates more subtypes of this event. According to the test results, the proposed method (Figure 26) provided the best matching with the input trace database.  
Figure 21: Result schema by inductive miner for T6.  
Figure 22: Result schema by GNN miner for T6 ([[https://gitlab.com/dominiquesommers/thesis_data](https://gitlab.com/dominiquesommers/thesis_data)]([https://gitlab.com/dominiquesommers/thesis_data](https://gitlab.com/dominiquesommers/thesis_data)), accessed on 4 January 2025).  
T8: BPI_2021_Permit dataset  
The standard methods generated over-generalized schemas. The heuristic miner produced a complex and incorrect schema (Figure 27). As this experiment shows, the GNN-based process discovery method (Figure 28) is not suitable to manage the multiple occurrences of the same event type in the expected way. Only the proposed method (Figure 29) could distinguish the different occurrence contexts of the event types. Although the number of nodes is larger in the output schema than in the baseline schemas, the node simplicity level (number of related edges) is at a lower value.  
Figure 26: Result schema by the proposed method for T7.  
Figure 24: Result schema by the heuristic miner for T7.  
Figure 27: Result schema by the heuristic miner for T8.  
Figure 28: Result schema by GNN miner for T8 ([[https://gitlab.com/dominiquesommers/thesis_data](https://gitlab.com/dominiquesommers/thesis_data)]([https://gitlab.com/dominiquesommers/thesis_data](https://gitlab.com/dominiquesommers/thesis_data)), accessed on 5 January 2025).  
In Table 4, we summarize the test results presenting the key efficiency measures of the investigated methods.