  Event logs are invaluable for conducting process mining projects, offering
insights into process improvement and data-driven decision-making. However,
data quality issues affect the correctness and trustworthiness of these
insights, making preprocessing tasks a necessity. Despite the recognized
importance, the execution of preprocessing tasks remains ad-hoc, lacking
support. This paper presents a systematic literature review that establishes a
comprehensive repository of preprocessing tasks and their usage in case
studies. We identify six high-level and 20 low-level preprocessing tasks in
case studies. Log filtering, transformation, and abstraction are commonly used,
while log enriching, integration, and reduction are less frequent. These
results can be considered a first step in contributing to more structured,
transparent event log preprocessing, enhancing process mining reliability.

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