## 1 Introduction  
Process mining has advanced rapidly within the past two decades and is presently shaping itself into a foundational domain. Since the coining of the term _\"Process Mining\"_, the amount of research and evolution that has taken place within is vast. These spheres of expansion are majorly classifiable into topics such as algorithmic approaches, visualisations, modularity, optimisation, data interpretation and analysis. Together, the progress from these spheres gave rise to the commercialisation and expansion of the domain from a theoretical concept into a practical reality. Experimental articles on data discovery algorithms and mining concepts have transformed into software toolkits and marketable products used in the field by organizations to track and improve their processes. The years leading up to 2010 depict that most research efforts were focused on the conceptual part of analysis aimed at creating new mining algorithms, improving visualisations and other core elements utilised in process modelling [1; 2; 3; 4; 5; 6]. Further on, the focus shifted towards enhancement and evaluation of generated models [7; 8; 9; 10]. This transition was in sync with the presence of better technology. Upon achieving progress, process modelling was employed in many fields at various levels of application (i.e. from basic procedural descriptions to supply chain routines). This brought along the issue of complexity. Excess or lack of information in the model proved to be a failure of process mining as a technique. The only remaining solutions is to analyse the model and determine if it is fit for the end user. Hence, by evaluating and measuring a process model, insight into the level of interpretability and the technical viability of the data is revealed. The cornerstone is being able _\"To make sure we achieve the objective\"_ (i.e. in principle, a better understanding of our data). In the pipeline of process modelling, the stages are generally in the order of data collection, application of algorithms, visualisation and evaluation. Our work aims to propose a solution on the part of process model evaluation. The main motivation for our study was derived from a situation where we wanted to measure a model. To do so we had to learn the fundamentals of process model assessment and comb through multiple articles to find a matching metric for our data. Further, we had to interpret the meaning of the metric in relation to a scale and continue searching until the right fit was found. This was a tedious and time-consuming task. When trying to find suitable measures, we encountered a never-ending supply of metrics described within individual works and a few summarised in review works. We did not find a clear solution such as an ISO standard that recommends the use of certain techniques or measures for the whole sector. Further, we observed that many metrics proposed by researchers in the community have both unique and universal applicability. This implies that every metric has its specific use and cannot be overlooked. However, from the perspective of universal modelling, we did not find a concise set of metrics that can be applied to all process models with universal attributes. Our work aims to address these issues by presenting a definitive solution to approach process model evaluation and metric-based assessment. Thus, our research objectives were crafted as follows:  
* OBJ 1: To propose a concise solution to the vast amount of metrics in the scientific community;
* OBJ 2: To demonstrate the use of metrics aimed to showcase the evaluation of process models. We have performed two individual literature reviews that provide insight into process model evaluation and its history. A taxonomy presents a comprehensive solution to the vast amount of metrics proposed in the community. In our work, we have focused on the technical aspect of model evaluation and have created a classification of metrics based on human-centric qualities of _understandability_, _complexity_ and _interpretability_.