## 4 Discussion  
Our work provided a ton of insight into metrics and the evaluation of process models. In this section, we have listed important issues that were encountered during this work. Addressing these issues will further improve the understanding of process models. **Metrics, preferences and their application**. Every person is accustomed to their preferences. Complex process models are suitable for industry professionals, as they portray information in greater detail. However, beginners would find complex models very hard to understand. Using a constant model for both audiences results in differences in detail being experienced by either party. This causes a loss of information. To answer this issue, there are several ways we have considered. The first method would be the generation of personalised models depending on the intended user's preferences, background and subject knowledge. The possible backdrop of this method would be the aggressive collection of personal data. A more realistic approach involves creating a universal model that describes a process within a balance of complexity (i.e., a model that can be useful for both beginners and experts). Metrics and evaluation techniques can be used to determine the optimal range of information to be described via the model. The accuracy of this range can be further improved by incorporating secondary data from the process model (e.g., size of traces, avg. size, number of unique events, etc.). Figure 14 describes the behaviour of QUAD metrics when an event log is tested in R1. This graph explains the performance of the model and, by analysis, we find the optimal range where models have a balance between all the metrics. Figure 13: Simplicity ratings of datasets 1 to 7 in R2. At activity rate 50%, it can be observed that the fitness is greater than 75%, indicating that 3/4 of the traces from the event log can be replayed by the model. The generalisation is good, signalling that most parts of the model are used equally. The precision at the same point has started to reduce but is greater than 75%, signalling that the model might have a small amount of behaviour unseen in the event log. The simplicity is low, indicating that the model is easy to interpret. From these observations, it can be concluded that the region near the 50% mark can be termed as the optimal range, since the models generated within this area during simulation have a good balance between all metrics. This result proves that the model is not aligned with any party (expert or novice). Hence, by using metrics, balanced models can be estimated and used for universal purposes. When required, individual models with weightings on specific features such as complexity, fitness, etc. can be explicitly created for usage (e.g., if all traces should be shown in the model, then the point at which fitness is 100% should be chosen). **Looping in precision**. During our experiment, we encountered an issue while calculating precision. It is best explained with an example. Consider a process model with the following flow {AB, BC, CA, BA}. To calculate precision, we calculate the various possible paths between two points. To evaluate, we utilise the full trace of individual caseIDs to check for behaviour not seen in the event log. In the exemplar process model depicted in Figure 15, the model starts at A and ends at C. It has one intermediate event B. We can notice there exists a looping construct BA and CA. The presence of loops gives rise to uncertainty, i.e., _How many times should an occurrence of a loop be included for evaluating precision_? For example, a trace in this event log could consist of the sequence ABABABABC as one of its entries. If we set our looping threshold to three (i.e., any activity can repeat only three times), the precision of this example would be negative. This is because of the existence of duplicate events that are not recorded, giving an impression  
Figure 14: An optimal range for a process model in R1. Figure 15: Process model to demonstrate the looping threshold.