# Event Log Preprocessing for Process Mining: A Review
## 3 Research Methodology
### C6 Information Type  
What type of attributes or information the preprocessing techniques use to work? According to the grouping of event log preprocessing techniques presented in Section 3.2.1, many of these techniques use various information resources to derive improvements in the quality of event logs of the mined or already built models. On the one hand, the event log is considered the base element to be exploited for the process mining tasks, particularly the automatic discovery of process models, as well as the reference source in the conformance with the already built model. Many of the strategies for data preprocessing in process mining are focused on working with the set of instances or traces that are obtained from the collection of events registered for executions of the process in question. To this end, different approaches that work on imperfections located in this source have been proposed. Some works have focused on some of the attributes identified in the collection of events, such as the information from timestamp which defines the ordering of events within the trace. Hence, incorrect ordering of events can have adverse effects on the outcome of process mining analysis. Even, in some scenarios in process mining, the timestamp attribute has been used to identify irregular processes using activity-level durations and contextual information. Some of the attributes used during the preprocessing are: case ID, event label, timestamp, cost, resource, contextual information, additional event payload, among others (see Figure 7). If the information of these attributes is missing, incomplete, in disorder, noisy or inconsistent, the preprocessing techniques clean and repair them. [PERSON] et al. [15] use the identification of chaotic activities to improve the quality of the event log. These activities are described as activities that do not have a clear position in the process model and can occur spontaneously at any point in the process execution. If it is the case, the discovery of the rest of the process becomes complex. Some other works on event log preprocessing techniques [32; 49; 50; 71] make use of the running process model that has been previously built by the organization's specialists. Furthermore, most of the preprocessing techniques require certain parameter values or tuning for filtering or accepting events. This allows establishing certain decision thresholds and thus being able to determine if any event, trace or activity can be considered as inconsistent or noisy. Many of the noise event detection and visualization techniques, particularly the clustering techniques (Section 3.2.2), use a traces set as input, which facilitates the division of the original event log. From this set, distances between traces are calculated, so that traces that have high similarity are grouped within the same cluster and instances that have low similarity are in different groups. Some clustering strategies exploit the information associated with events, such as resource and contextual information to improve the partitioning of the event log. In the case of pattern-based preprocessing techniques, they mainly use the raw event log to identify concrete forms, which keeps recurring non-arbitrary contexts, with the timestamp attribute being the most used by these techniques. Within the transformation techniques (filtering), it is common to use a set of traces to identify problems associated with the missing or noisy values contained in the different attributes in the event log. Table 6 presents the relationships among the different characteristics (C1--techniques, C2--tools, C3--representation schemes, C4--imperfection types, C5--related tasks, and C6--types of information) of the preprocessing techniques surveyed in this work. As can be seen in the Table 6, filtering-based techniques are available in most of the process mining tools. However, the pattern-based techniques are only available through the ProM tool.