# Event Log Preprocessing for Process Mining: A Review
## 3 Research Methodology
### C4. Imperfection Types in the Event Log  
What kind of imperfections are commonly identified in the event logs? [PERSON] et al. [3] establish that the most real-life event logs tend to be fine-granular, heterogeneous, voluminous, incomplete, and noisy, which can cause major problems in the different process mining tasks. On the one hand, fine granularity is related to the level of detail at which events are recorded. This can vary widely without considering the desired levels of analysis. This type of imperfection is closely related to the quality issue of data diversity (Section 2) where there are diverse types of records with different granularity levels due to a lack of good standards and guidelines for logging. Often, the models produced from the discovery task are spaghetti-like and hard to comprehend due to fine granularity in the event log. Heterogeneity in the event log means that many of the real processes take place in diverse and unstructured environments, causing the generated event log to contain a heterogeneous mixture of these environments. Heterogeneity stems from operational processes that change over time to adapt to changing circumstances. The trace clustering techniques have been shown to be an effective way of dealing with heterogeneity. Inside the fine granularity issue, there are also imperfections in the event logs related to coarse granular timestamps, which implies that the ordering of events within the log may not conform to the actual ordering in which the events occurred in reality; mixed granular timestamps, where there are events for which the level of granularity of their timestamps is different; incorrect timestamps, scenarios where the recorded timestamp of (some or all) events in the log does not correspond to the real time at which the event occurred. Moreover, there are other common imperfections present in the event log associated to the data quality issue, such as missing attribute values or events missing anywhere within the trace, although they occurred in reality. There may also be problems in the event log associated with ambiguity between events, where multiple events have the same activity name. Another issue is the overlapping activity, where an instance of an activity is started and before it is completed, another instance of the same activity is started. Moreover, the presence of noisy data/outliers is common; that is, rare, exceptional, or anomalous execution behavior. Authors in [29] identified a series of imperfections patterns commonly encountered in preprocessing raw source logs. These patterns are defined from the problems that are identified when transforming raw data source logs into an event log that is 'clean' and usable for process mining analysis. Some of the imperfection patterns presented in [29] are directly related to the issue of missing event values or noisy values, for example when the events in a log are not explicitly linked to their respective case identifiers, or when there are key process steps missing in the event log being analyzed but are recorded elsewhere. To address this type of imperfection pattern, most of the event- or trace-level filtering techniques studied in Section 3.2.1 attempt to identify missing events and correct them, or eliminate outlier events from the event log. Another type of imperfection pattern also presented in [29] is related to problems in the timestamp attribute. This imperfection occurs when recording errors in the timestamp, or when the timestamp values are recorded in a different format from the expected (data diversity), or when recording events from electronic forms, such as the difference in the order of the events that were executed. Techniques to address the issues in the timestamp are mainly based on determining the impact that the timestamp information has to improve the quality of the event log.