## 5 Conclusions and Future Work  
Achieving a balance between complexity and interpretability is vital to fulfilling the goal of process mining. Process models can be understood by the end user only if they are fit for purpose. Hence, metrics and evaluation techniques mentioned in this work provide researchers with a guide to enter and dive into assessment techniques for process modelling. The taxonomy of metrics collectively cumulates all the viable measures proposed by fellow authors during the time frame of 1997 to 2023 and classifies them based on three human-centric process modelling qualities. We present a once-and-for-all solution to the vast amount of metrics in the community using the taxonomy. Researchers can use our work to find metrics and evaluation techniques instead of studying many articles to find the right fit. Together with the broad spectrum review, the taxonomy follows through by summarising the history of how to evaluate a process model and provides a steady learning curve for beginners in the domain. We have chosen the four most widely used metrics from the tracked literature and termed them the QUAD metrics. These metrics utilise universal attributes of a process model for evaluation and can be used to assess the vital properties of the model. The performance and behaviour of these metrics under various control conditions have been showcased using real-world healthcare event logs. The conclusions drawn from the experiment will aid researchers in the analysis and selection of metrics for future applications. Our implementations of the QUAD metrics are adaptable to datasets from all domains with reconfiguration (i.e., they are universal). Modellers can obtain significant insight into the interpretability of their models by evaluating their process graphs with these metrics. Therefore, we propose to use the QUAD metrics as a universal standard of evaluation for process models. The insight from these metrics can be used to fine-tune and craft visualisations specific to certain qualities or audiences. We believe that our experiment provides crucial information that will aid future researchers to progress in the evaluation phase of modelling.  
In future studies, we aim to create open-source access to our implementation of the QUAD metrics and introduce add-ons for universally used process mining software such as PM4 PY. We consider the insights from our study to be generalised input for the community and scalable to all domains. We aim to resolve the limitations of this study by including more process modelling qualities for metric analysis and implementing re-conceptualised versions of simplicity and generalisation metrics to account for both activity and path rates. We plan to create a universal filtering script that is applicable to all domain data types within process modelling.  
Conceptualisation, S.V.K.; methodology, A.T.S.I. and S.V.K.; software, A.T.S.I.; validation, S.V.K.; formal analysis, A.T.S.I. and S.V.K.; investigation, A.T.S.I; resources, S.V.K.; data curation, A.T.S.I; writing--original draft preparation, A.T.S.I; writing--review and editing, A.T.S.I. and S.V.K.; visualisation, A.T.S.I; supervision, S.V.K.; project administration, S.V.K.; funding acquisition, S.V.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Higher Education of Russian Federation, goszadanie no. 2019-1339.  
Not applicable.  
The data used in the study are publicly available via links provided in the corresponding sections.  
The authors declare no conflict of interest.  
Figure A2: Workflow for Precision.  
Figure A3: Workflow for Generalisation.  
Figure A4: Workflow for Simplicity.