# Event Log Preprocessing for Process Mining: A Review
## 3 Research Methodology
### Systematic Review Process
#### 3.1.2 Exclusion Criteria  
The following are the criteria to discard research works that are not of interest for this revision.  
1. Works that are not related to process mining.
2. Works that do not focus on specific domains in the field of process mining (industry, manufacturing); that is, ad hoc techniques for a given domain.
3. Works that do not include evaluation and experimental results.  
After filtering by the queried topic and removing the duplicated retrieved papers, a total of 95 papers were obtained and analyzed, considering the inclusion criteria. On this set, and after applying the exclusion criteria, the result was a set of 70 papers. All of these are included in our qualitative analysis.  
Figure 2 shows the distribution of the selected works (in %), from 2006 to 2020, based on the year of publication (during a period of three years). Although Figure 2 reveals a slow growing tendency, it is difficult to determine if that tendency will be kept.  
As shown in Figure 3, 25% of the selected works were published in journals, 70% were reported at international conferences, and the rest are theses. Works formerly reported (2006-2008) have had great influence in the community, as Figure 4 reveals a total of around 2400 cites. Moreover, most recent works (2017-2020) reveal more than 350 cites.  
Figure 5 shows a network of closely related terms that describe the different addressed topics of event log preprocessing. This network was formed from the relationships identified between concepts included in the abstract of the surveyed works. The color intensity in the network nodes refers to more general or abstract terms that contain other more specific terms, all related to describing the handling of preprocessing in process mining. This network of terms could be useful for scientists to understand and organize the diversity of existing preprocessing techniques.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Search String** & **IEEE Xplore** & **Springer Link** & **Science Direct** \\ \hline (“filtering” OR “cleaning” OR “repairing” OR “refinement” OR “preprocessing”) “event log” “process mining” & 110 & 32,440 & 310 \\ (“filtering” OR “cleaning” OR “repairing” OR “repairing” OR “refinement” OR “preprocessing”) “trace” “process mining” & 227 & 63,897 & 401 \\ (“ordered” OR “aligning”) “event log” & 139 & & \\ (“process mining” & & & \\ \hline (“anomalous detection” OR “infrequent behavior” OR “inperfection”) “event log” “process mining” & 16 & 1813 & 87 \\ (“process mining” & & & \\ \hline (“anomalous detection” OR “infrequent behavior” OR “noisy” OR “imperfection”) “trace” & 24 & 2106 & 95 \\ (“process mining” & & & \\ \hline \end{tabular}
\end{table}
Table 1: Query details and results (number or retrieved papers) from the selected digital libraries.  
Figure 4: Citations, each three years of the surveyed papers.  
Figure 3: Percentage of publications associated with different surveyed sources in this survey.  
Figure 2: Publications, each three years of the surveyed papers.  
During the literature review, a content study was performed. In this study, we identified and classified the common and relevant characteristics found in the surveyed papers. Table 2 outlines a general view and a summary of the most significant characteristics (C1--techniques, C2--tools, C3--representation schemes, C4--imperfection types, C5--related tasks, and C6--types of information), which are described in greater detail in the next sections.