## 2 Literature  
### Background  
As mentioned earlier in Section 1, process modelling has been flourishing since the early 2000s. Today, it has become a commercialised sector and is further developing into its own field. During this phase of transitioning into a sector, there came a stage when we needed to assess the fruition of results. This is where the evaluation of process models is primarily required. Much research has been carried out and numerous metrics have been proposed, yet there still exists an uncertainty on how to perfectly evaluate process models. The measurement of models is commonly performed using their characteristic attributes, but as the volume and variety of data grew, many secondary features were being introduced for modelling. By utilising non-universal features for assessment, newly developed metrics were restricted to the type of data that can be used for measurement. Therefore, there is a need to distinguish and classify metrics that can be used on models generated from universal event logs. In our work, we have prioritised selecting metrics and models that use universal elements for assessment and generation.  
Today, we have reached a point where metric evaluation can produce a numeric value of sorts that describes the model. However, the details of this rating are left to the user to decode (i.e., the meaning of the metric itself and its relational rating of the process model on that respective metric scale). This creates a complex environment for approaching metrics. Further, the volume of metrics already proposed in the community makes it difficult to specifically choose a metric without overlooking another. Process model qualities were created to address this issue. They were used to distinguish certain properties of the model that can be interpreted by the user via metrics. In our work, we have chosen three major qualities crucially relevant to describe the technical properties and the human perception of the model.  
We collectively address these issues using three crafted research questions. By answering these questions, we were able to achieve our research objectives:  
* RQ1: What are the methods to evaluate a process model?;
* RQ2: How has process model evaluation progressed over the years?;
* RQ3: How do we measure the understandability, complexity and interpretability of process models using metrics?  
Research questions RQ1, RQ2 and RQ3 are addressed as follows. _RQ1_ and _RQ2_ are answered using a broad spectrum study along with a meta-review. The broad review gives a ground-up perspective of model measurement and provides a simplified outlook on its evaluation and assessment. The meta-review is a state-of-the-art _review of reviews_. It presents a taxonomy of established metrics proposed by the community and aims to list all the viable metrics within a tabulation. _RQ3_ is answered in Section 2 using a diagram and via an experiment in Section 3. This answers the question of evaluating process models based on their _understandability_, _complexity and interpretability_. The experiment demonstrating the usage of evaluatory metrics on healthcare event logs is also described in Section 3. We have filtered down to four prime metrics that operate based on the fundamental principles of process modelling (i.e., using universal features). The latter objective of _having uniform metrics for the process mining domain_ as a whole is answered using the results generated from our experiment. Our work further aims to provide _\"A once and for all solution\"_ to the vast amount of metrics in the community and propose a standard of measurement for process modelling.