## 2 Literature
### Meta Review and Evolution
Upon filtering works based on inclusion and exclusion criteria, a total of 63 articles were obtained. After reading the full text, 36 articles were eligible for analysis and inclusion in the meta-review [65; 66; 67; 68; 69; 70]. We listed 140 metrics proposed by fellow authors within our meta-review. They are categorised based on their impact on the model qualities of understandability, complexity or interpretability. Additionally, we have included new fields that would aid researchers in selecting and implementing proposed metrics. The taxonomy of metrics consists of the following fields of data (the respective acronyms are included in parentheses):  
1. The **Name** of the metric (Metric);
2. The **Year** of proposal (Year);
3. **Authors** of the metric (Auth);
4. Working **Definition** (Def. );
5. Status of **Demonstration** by the authors _(Yes/No)_ (Demo): * Yes (Y)--A demonstration of the metric has been provided by the authors in their works; * No (N)--A demonstration of the metric has not been provided by the authors;
6. Type of **Metric** (Type): * _Scratch (S)--_Metrics that are completely designed by the author(s); * _Partially Derived (PD)--_Metrics that borrow ideologies but have their own mechanism; * _Derived (D)--_Metrics implemented based on existing concepts and measures;
7. Source to **Original paper** (Src). **Evaluation History:** Early literature collectively describes metrics as one of the _\"goals of models\"_[71]. We have come up with a simple representation of metrics in retrospect to process mining as a whole. Figure 3 represents the viewpoint of evaluation and its branching. Level 1 consists of the main branches of the field and splits into process discovery, knowledge mining and conformance checking. Level 2 consists of topics which come under each level 1 division. These topics are subdivisions that focus on specific aspects of evaluating models. _Knowledge mining_ consists of methods used to extract information from logs and datasets. _Conformance checking_ encompasses approaches to analyse the mining performance while discovering the event logs and extracting information. The _process discovery_ division deals with performing visualisations and implementation of algorithms to generate models. We believe that these subdivisions work hand-in-hand when dealing with evaluation. Level 3 is a combination of L1 and L2. It ideally performs the model quality analysis. Level 3 is classified as quality dimensions, evaluation and metrics. The goal of evaluation is to have metrics that can measure specific qualities of a process model. Hence, we have focused on model qualities that aid in improving the human perception of the model. We believe that understandability, complexity and interpretability can be coined as crucial qualities for human perception of process models [72]. To ensure that our taxonomy remains simple and allows for easy traceability to the original works, we adapted strategies to enhance the results:  
* Ensure the metrics listed were within the scope of modelling qualities: understandability, complexity and interpretability;
* Link metrics that were conceptually the same but with different authors and explicitly mention them;
* Perform a viability check of the measures (metrics listed should be ideal for universal application, i.e., should utilize the basic three);
* Scrutinise and check if the proposed metric is demonstrated by the author and can be replicated by prospective researchers;
* Mention redundant metrics where necessary;
* Include a mini description to improve readability;
* List the original source of the metric. The work by [4] in 1997 exhibits the necessity of evaluation in business process modelling. The authors emphasise representing factual data in a simplified manner (i.e., to improve the human perception of the data). With this in mind, if a generated process model does not provide a certain level of data interpretability, then the modelling procedure may be termed a failure. Authors of [73], in 1998, coined the term direct flows graph.