# Event Log Preprocessing for Process Mining: A Review
## 3 Research Methodology
### C1. Techniques
#### 3.2.2 Detection-Visualization Techniques
[29] propose determining event log quality by the description of a collection of eleven log imperfection patterns obtained from their experiences in preparing event logs. The definition of pattern is given as the abstraction from a concrete form, which keeps recurring in specific non-arbitrary contexts. [PERSON] et al. [52] describe an approach that combines the discovery of frequent execution patterns with a cluster based anomaly detection procedure. Special algorithms are used for decreasing the counting of spurious activities and for coding a method that simultaneously clusters a log and its associated S-patterns, respectively (patterns and clustering). WoMine-i [53] extracts, infrequently, components in the logs from the model specification (tasks sequences, selections, parallels, loops, etc.). WoMine-i performs an a priori search starting with the minimal patterns and reduces the search space by pruning the infrequent patterns. [PERSON] et al. [54] propose an iterative method for transforming traces that identify the looping constructs and sub-processes and replace the repeat occurrences by an abstracted entity. Other pattern-based approaches are presented in [14; 35; 39; 48; 54]. Additionally, some process mining algorithms [55; 56; 57; 58; 59; 60; 61] incorporate mechanisms of event log preprocessing (embedded techniques) as part of their approach. These algorithms implicitly attempt to detect noise traces, hidden tasks, duplicate activities in the event log, which can sometimes be attributed to event ordering imperfections. However, the decisions and detections made during the execution of some process mining tasks (discovery, conformance, or enhancement) are implicitly incorporated in the discovered process model. In this case, embedded preprocessing techniques are able to exploit their coupling to the discovery technique, allowing each step or iteration to verify and validate if the built process model is a solid model. This is revealed from some works [60; 62], where it is ensured that, from the identification of noisy data, as well as from the flexible configuration of parameters in the preprocessing techniques, it is possible to build more solid and robust models. Table 4 presents a general summary of some of the most popular event log preprocessing techniques previously discussed. In that table, we provide a notation to refer to a particular technique being used: \(A1\) (event/trace level filtering), \(A2\) (clustering), \(A3\) (pattern-based techniques), \(A4\) (Embedded techniques), \(A5\) (time-based techniques), \(B1\) (alignment), \(B2\) (abstraction). Table 4 also shows the particular task (discovery-\(D\), conformance-\(C\) or enhance-\(E\)) that is intended to be improved by including a preprocessing technique in that same order. In the table are also shown the main problems identified in the event log, such as missing data (_mis_), noise data (_noi_), diversity data (_div_), irrelevant data (_irr_), and duplicate data (_dup_). From Table 4, we can conclude that the trace clustering technique, and event/trace filtering are the two most frequently used techniques for the preprocessing task in process mining. Time-based preprocessing techniques have recently shown promising results in data preprocessing through the study, correction, and elimination of data associated with the timestamp attribute. Moreover, the table reveals that a vast majority of preprocessing techniques have been designed to improve the process model discovery, in order to improve the quality of the discovered models, reducing the complexity of the model through the management of clean data registered in the event log. Furthermore, about 60% of the studied techniques are available in process mining tools, such as ProM tool and a small percentage corresponds to individual applications that incorporate preprocessing techniques independently. Finally, Table 4 shows the two