# Event Log Preprocessing for Process Mining: A Review
## 1 Introduction  
Process mining is a relatively new study area that has gained significant attention among computer science and business process modeling communities [1]. It is a powerful tool for organizations to obtain actual models for better understanding of the real operation of their business processes and for better decision making. Process mining techniques allow automatic discovery, conformance, and improvement of process models implemented by organizations through the extraction of knowledge from event logs as well as from the available documentation of the process model [2]. In this context, an event log is a collection of time-stamped event records produced by the execution of a business process. Considering that the event log is the main input for process mining techniques, the quality of this information has a great impact on the resulting model. An event log with low quality (missing, erroneous or noisy values, duplicates, etc.) can lead to a complex, unstructured (spaghetti-type), and difficult to interpret model (as shown in Figure 0(a)); or a model that does not reflect the real behavior of the business process. Therefore, event log data preprocessing is considered a task that can substantially improve the performanceof process mining. According with [3], in the big-data era, process mining tasks can be strongly limited by the quality of event data and processing times. This has placed greater attention on preprocessing tasks. [PERSON] et al. [4] describe event log quality as a two-dimensional spectrum where the first dimension is concerned with the abstraction level (or granularity) of activities that are part of the process model. The second one is concerned with the accuracy of the timestamp (in terms of its granularity, directness of registration, and correctness) of the events registered in the log. [PERSON] et al. [5] present the data quality problem from a theoretical approach addressing the root causes and the social, material, and individual factors that contribute to low quality data in event data, which would be overlooked by existing data cleaning methods. Other works [6; 7] describe data quality as being a multi-dimensional concept associated with accuracy/correctness, completeness, unambiguity/understandability and timeliness. In this paper, we relate the event log quality with the identification, visualization, and correction, or elimination of incorrect, or noise, missing, duplicate, and irrelevant events. In real scenarios, some process mining tasks work under the assumption that behavior related to the execution of the running process is stored correctly within the event log, and that each instance of the process stored in the event log is already finished. However, real-world event logs contain noisy or corrupt data records, which can be generated by different factors: some traces are duplicated, incomplete, inconsistent, or reflect some other incorrect behavior. These problems can be caused by several factors, including errors in the data transmission, errors during storage, technology limitations, or transcript errors when events arrive in the wrong order. Sometimes, the noise may be associated to the presence of rare events due to handling of exceptional cases, incorrect recording of selected tasks in the execution of the process, or even for the incorrect assignment of timestamps. Constantly, it is difficult to distinguish between noise and infrequent correct behavior in an event log, resulting in a mined model with less fidelity to the real model. Different noise types in the event log impact negatively in quality issues, making that the process mining algorithm return complex, incomprehensible, or even inaccurate results. Therefore, to reduce these negative effects, event log preprocessing is a necessary task in the majority of process mining algorithms. The preprocessing of the event log attempts to detect and remove noise events, traces, or activities that contain such undesired behavior.