## 2 Literature
### Meta Review and Evolution
It is a more flexible and less accessory-oriented representation for process modelling. The use of DFG has simplified the learning curve to approach process modelling. It has also aided in minimising the gap between data modellers and the end user. Unlike Petri nets and process graphs, the DFG shows only required information such as flow, activities and other peripheral details. The logical section is hidden under a layer and need not be shown along with the final resultant. Using DFGs as a tool was scarce during the early 2000s, as it was a fresh concept still in adaptation. Figure 3: Branching of evaluation in process modelling. The boom in technology played its part in the rapid digitisation of the world. It gave rise to enormous amounts of process data usable for analysis and experimentation. As data increases, so does the complexity of the process model. Thus, the trend started from here; to resolve the complexity of modern processes, we need a way to distinguish simple and complex models, i.e., easy vs. hard to interpret. During this time, process mining was also gaining a foothold in the markets as it helped analysts visualise and understand their processes much better than traditional methods [74; 75]. Initially, measuring the complexity of a process model was performed by borrowing methods from neighbouring fields of software engineering and networking [76; 77; 78]. Lines of code and correctness are a few examples of metrics migrated for usage in process modelling. An increase in data generated resulted in these metrics reaching a saturation point, after which using these metrics became very tedious and cumbersome. Hence, frameworks and dimensions of measurements were proposed and developed, e.g., CPMQF [48]. Frameworks such as SEQUEL [51; 52] and QME [9] (Quality Assessment framework) suggested using notations, statistics of activities, algorithm variations and many more as features to analyse the metrics of a model. From the year 2006, many works summarised the progress in the field and crafted a path to create a bridge between process mining technologies and operational business processes. The works highlighted many issues faced in the sector based on the approaches, quantifiers, analysis of results, representation and differences within. Nevertheless, the principle remained the same: ensuring process information was conveyed easily using process models [79; 80; 81; 82]. Parallelly, the implementation of new algorithms took place with enhanced feedback. The results focused on improving the algorithms and not just the visualisations. Ref. [83] lists the most-ideal ways to judge the complexity of the model at the time. With insight into algorithms and better feedback, modelling transformed from a static to a dynamic phase. Mining and modelling were no longer only used on past data but also for simulation and designing future usage. The usage of non-universal data attributes was high during this time. These are fields of information that might not be available in datasets of various domains. This led to a shift in using the basic three as the minimal standard for any event log analysis. The 7 PMG [30] proposed seven basic guidelines for process modelling. It gave a baseline platform for evaluators and fellow modellers to generate a unified procedure for model development. The years beyond 2013 saw many researchers propose metrics linked to specific process modelling qualities (e.g., activity period, exclusiveness, generalisation, flexibility, etc.). At the same time, [PERSON] and [PERSON] published the process mining discovery and metrics books [84; 11]. [PERSON] explicitly explained and started the trend of using the four prominent metrics. Other authors proposed their metrics and drove efforts to review and revise older frameworks to develop concise and present-gen versions. The progressing years saw many authors start to use a handful of metrics as a scale to compare and quantify their proposed metrics.