## 2 Related Works
### Towards Process Model Optimization  
To addressing the problem of an optimal process model, we intend to construct a model that is simultaneously simple for understanding and accurately captures process behaviours. Ideally, finding a balance between these quality components should be as automated as possible, and the simplicity of the model can be achieved through its elements' abstraction/aggregation. Table 1 contains studies which reflect these issues to a different extent. Most of the papers are from the last 6 years, and several earlier works are presented as baselines. The proposed solutions are classified on the type of approach used and evaluated on the degree of its applicability to the aspect in question. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approach** & **Studies** & **Optimization** & **Methods** \\ \hline Log pre-processing & [PERSON] et al. [21], [PERSON] et al. [22], [PERSON] et al. [23], [PERSON] et al. [24], [PERSON] et al. [25], [PERSON] et al. [26] & \(\pm\) & Outlier (events, traces) detection and removal, region-based methods for repeated tasks, topic modelling, sequence labelling \\ \hline Behaviour filtering & [PERSON] et al. [27], [PERSON] et al. [28], [PERSON] et al. [29], [PERSON] et al. [26], [PERSON] et al. [30,31], [PERSON] et al. [32], [PERSON] et al. [33], [PERSON] et al. [34] & (manual) & Activity, precedence relation, cycles, and split/join filtering; conflict resolution; attribute accounting \\ \hline Aggregation & [PERSON] et al. [21], [PERSON] et al. [27], [PERSON] et al. [31], [PERSON] et al. [35], [PERSON] et al. [36] & \(\pm\) & Hierarchical event structure (e.g., ICD-10 codes, software code architecture), correlation metrics, model construction folding \\ \hline Clustering & [PERSON] et al. [37], [PERSON] et al. [38], [PERSON] et al. [39], [PERSON] et al. [40], [PERSON] et al. [41], [PERSON] et al. [42] & \(\pm\) & Trace and event clustering \\ \hline Optimization problem & [PERSON] et al. [35,43], [PERSON] et al. [44], [PERSON] et al. [45], [PERSON] et al. [46], [PERSON] et al. [47], [PERSON] et al. [48] & \(\check{\sim}\) & Linear programming, Pareto optimality, particle swarm optimization, etc. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies addressing process model optimization. Log pre-processing is a good starting point for enhancing both process visualization and model precision. This category of model enhancing techniques is applied before the discovery directly retrieving log information. [PERSON] et al. [21] demonstrate a systematic approach to event log preparation based on imperfection records patterns. Common quality issues compose such patterns. They are form-based event capture, unanchored event, inadvertent time travel, etc. The authors describe each pattern and show real-life cases, proposing possible solutions and potential limitations. For example, distorted labels, the most frequently observed pattern, negatively impact the readability and validity of process mining results, according to the questionnaire results. Activities that have the same semantics but do not match due to incorrect data entry or ununified recording systems should be treated as one. This could be done by letters capitalization agreement, similarity string search, or manual interventions (e.g., using a knowledge base [21] or rule base ontology [22]). More intelligent techniques, such as topic modelling [23] or conditional random fields [24], can facilitate moving from low-level to high-level events, aiding the comprehensibility of discovered process models. In [25], the authors propose an interval-based selection method to filter outliers, which are repeated events within the specified time period. Event outliers are defined based on the distribution of time intervals of consecutive events of the same activity. The time perspective regard has potential for the log pre-processing step; the proposed method has improved model precision without reducing its fitness.