# Event Log Preprocessing for Process Mining: A Review
## 4 Lessons Learned and Future Work  
Based on the literature review, some important outcomes and guidelines can be inferred. There is increasing interest in the study of preprocessing techniques for process mining from various domains (health, manufacturing, industry, etc.). They have demonstrated great success in building process models that are more simple to interpret and manipulate, causing many organizations to be interested in these types of techniques. This is more evident with the arrival of big data, having business processes with huge event logs, which could contain a high amount of imperfections and errors, such as missing values, duplicate events, evolutionary changes, fine-granular events, heterogeneity, noisy data outliers, and scoping. In this sense, the preprocessing techniques in process mining represent a fundamental basis to improve the execution and performance of process mining tasks required by experts in process models. In practice, process mining requires more than one type of preprocessing technique to improve the quality of the event log (as shown in column 2 of Table 4). This is because an event log can have different data cleaning requirements and a single technique could not address all possible issues. For example, if the event log is voluminous; that is, with a large number of events or cases, a suitable technique for this type of log is trace-clustering. This preprocessing technique divides the original log into small sub-logs, allowing to reduce the complexity of its handling and storage. If the event log size is of average size (normal), but there is high variability in the size of the set of traces that are formed from the log, it is highly possible that filtering techniques at the event/trace level are more suitable. On the other hand, in those event logs, where it is estimated that the duration of the activities of an event is too slow or too fast, the use of preprocessing techniques based on the study of the timestamp is suggested. From the review presented in this work, it is observed that the most commonly used preprocessing techniques are trace-clustering, and trace/event level filtering (see Figure 8), mainly due to the fact that they are easy to implement and adequately manage noise and incompleteness in the event logs, and also allow models to be identified from less-structured processes. On the one hand, the trace clustering technique is more suitable for the case where it is required to reduce the complexity of the discovered models. This technique is generally applied together with pattern identification or event abstraction techniques, since both are strongly linked to identifying associations or rules from observed behaviors, or acquired experiences in the event log. On the other hand, trace/event filtering techniques are sometimes applied in conjunction with timestamp-based techniques to achieve the identification and correction of missing or noisy values in the event log. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Techniques (C1)** & **Tools (C2)** & **Representation Schemes (C3)** & **Imperfection Types (C4)** & **Related Tasks (CS)** & **Information Type (C6)** \\ \hline Filtering-based & ProM, Hormone, RapidProM, Disco, Celonis & sequences of traces/ & noise and missing data & alignment & traces \\ Time-based & ProM, Apromore, RapidProM, Disco & activities & & & \\ Time-based & ProM, Apromore, RapidProM, Disco & graph structure and sequences of events & missing, noise, diverse, and duplicate data & & time attribute \\ Clustering & ProM, RapidProM & sequences of traces/ & noise and diversity data & abstraction & traces \\  & Disco, Celonis & events & & & \\ pattern-based & ProM & raw event log & noise and diversity data & abstraction/ alignment & traces \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Characteristics (C1–C6) on data preprocessing in the context of process mining. Several works on data preprocessing in process mining focus on the identification of specific noise patterns associated with the quality of the event log.