Turning Logs into Lumber: Preprocessing Tasks in Process Mining

TL;DR

This paper systematically reviews preprocessing tasks in process mining, identifying common and less frequent tasks, to improve the transparency and reliability of event log analysis.

Contribution

It provides the first comprehensive repository of preprocessing tasks and their usage in case studies, promoting more structured data preparation in process mining.

Findings

Six high-level preprocessing tasks identified

Twenty low-level preprocessing tasks identified

Log filtering, transformation, and abstraction are most common

Abstract

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.