## 3 Methodology
### Results
The generalisation rating is constant for majority of the time, which indicates all parts of the model are being used consistently. When reaching 100% activity rate, there is a sudden drop observed in S2 and S3, while S1 and S4 have a gradual decrease in their generalisation. S5, S6 and S7 are mostly constant throughout, with minor fluctuations nearing 100% activity rate. The variation in generalisation indicates the addition of an event into the model. In S1 and S4, the events being added after 50% activity rate are less frequently used; hence, all parts of the model are not equally used, thereby reducing the overall generalisation of the model. In S2 and S3, the huge dip in generalisation is due to the appearance of sparsely occurring events added at 100% activity rate. These events cause the process model to be less generic, resulting in low overall generalisation rating. In S5 and S6, the frequency of events have very similar thresholds. This causes most of the events to be shown within very small intervals of the activity rate. Hence, the number of activities remains constant throughout. This also means that all the activities are equally utilised in the event log itself, thus making the process model generic. The activity rate controls the number of activities shown in the process model; therefore, an increase in activities impacts the generalisation based on the frequency of occurrence of the events. When frequent events are shown, the generalisation is high, since the events are used consistently throughout. When infrequent events are added, the generalisation is low, since certain parts of the model are not always used. This can be observed in Figures 13 and 14, where the number of infrequent activities correlates to the reduction in generalisation. The red circles indicate the new events that were added at each iteration of activity rate in R1. In our adaptation of this metric, the path rate does not have an impact on the generalisation in R1, since we account only for the activities and not the edges. By adding edges for the calculation, the results might not be conclusive, as the concept of generalisation is designed to measure the events in the model that is most often used. The addition and removal of paths will not have a direct effect directly the threshold of activities is maximum. The behaviour of generalisation in R2 is straightforward, as observable in Figure 11.  
Figure 10: Generalisation ratings of datasets 1 to 7 in R1. The generalisation stays constant throughout the increase in path rate because the number of activities is fixed in R2. E.g., at activity rate = 5, only 5% of the most frequent activities are shown. Thereby, the model will have a fixed number of events throughout the variation in path rate in R2. In S1, at activity rates 5 to 80, the generalisation is almost maximum. This is due to the total number of activities being constant throughout the analysis. All the events are used in the model equally throughout R2. At AR = 100, the generalisation decreases, due to the addition of all paths present in the event log. These paths are not utilised as much as the major ones; hence, the model is less generic than before. Figures 11 and 12 showcases this behaviour. Similarly, in datasets 2-7, each stage of activity holds the total amount of events constant. Therefore, the generalisation in R2 has no impact on the change in path rate and only changes when the activity rate is varied by its stages. _Simplicity is a measure to understand how easily the model can be perceived by a human subject._ Figures 13 and 13 show the behaviour of simplicity in R1 and R2. The behaviour of simplicity is equated to the complexity or difficulty in understanding the model. The greater amount of events present in the model, the more difficult it is to understand for a human subject. In this adaptation of Simplicity, the number of interconnections was not involved in calculating the metric.