## 4 Discussion
of too much existing behaviour. This can also be seen in our experimentation of precision in R1. Some samples cross the 0% mark and start generating negative precision values. This is due to the same issue [97; 98; 99]. **Specific changes occurring due to variations in the event log during process map creation**. This is an example of the inclusion and exclusion of events causing changes in the metrics recorded. In our experiment, we faced issues with our datasets when using \"START\" and \"STOP\" events within our models (i.e., the trace begins with an event \"START\" and ends with \"STOP\"). Some of our datasets did not explicitly have this event within them, while a few did. We decided to remove the START and STOP events and then evaluate our metrics. Before taking this decision, we analysed the possible issues that may arise by including and excluding these events. **By excluding START and STOP**  
* The traces are uniform for universal analysis;
* Traces begin and end with activities from the log, resulting in directly analyzable pure information;
* By excluding START and STOP, instances of measurement are scaled exactly to their logs and not the collective size. **By including START and STOP**  
* In datasets without the keywords, traces have to be explicitly extracted and modified, thereby increasing pre-processing time;
* When there is more than one event that leads to a STOP, there is an imbalance in precision and generalisation, since they would be additional events that individually (tracewise) lead to the end but collectively result in a mismatch [100; 101; 102; 103]. **Why choose understandability and complexity? ** From our study, we find that the main motive and objective of process modelling is to present event log data to humans in a simple form. The quality of complexity covers many terminologies that give the idea of \"_not difficult_\". Understandability gives the idea of \"_easy to understand_\". These two qualities appreciate each other and provide a certain platform to clearly state whether a model is simple or complex without forgetting about metric constructs [104]. **Emphasis on using pre-processed data for analysis of events:** This is a crucial issue. As mentioned earlier in Section 2, the basic three are vital for process mining and modelling. However, the task data should undergo careful pre-processing, as this can cause issues when evaluating metrics. In our experiment, we faced an issue when there was the usage of English and Cyrillic alphabets together. Our pre-processing was designed for filtering and cleaning English alphabets. However, we overlooked the Cyrillic alphabet which led to more events being present than in the event log, resulting in process models with an increased number of activities to be generated. The evaluation of models with proper per-processing was found to have an equal number of activities as in the event log, thus avoiding irregular metric ratings. **Limitations of the work**: By performing this study and experiment, we found certain limitations in the technical aspect. In our work, we have focused on three human-centric qualities (understandability, interpretability and complexity). However, many more qualities can be explored and used to classify metrics (e.g., modifiability, expressiveness, compatibility, etc.). Classifying metrics based on such qualities requires an increased depth of research and narrow exploration strategies. ProFIT process mining toolkit currently works only using the fuzzy heuristic mining algorithm and visualises data using the direct flows graph (DFG). In comparison to other mining algorithms and visualisations, there are limitations with using this technique (e.g., the Petri-nets can show more event data that is hidden in DFGs and other mining algorithms may project the data in a different perspective). Hence, there is an opportunity to explore the various mining and visualisation techniques; however, our experiment focuses only on the behaviour of the fuzzy heuristic miner and the DFG visualisation.