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

Turning Logs into Lumber: Preprocessing Tasks in Process Mining