Towards interactive event log forensics: Detecting and quantifying timestamp imperfections

Abstract

Timestamp information recorded in event logs plays a crucial role in uncovering meaningful insights into business process performance and behaviour via Process Mining techniques. Inaccurate or incomplete timestamps may cause activities in a business process to be ordered incorrectly, leading to unrepresentative process models and incorrect process performance analyses. Thus, the quality of timestamps in an event log should be evaluated thoroughly before the event log is used for any Process Mining activity. To the best of our knowledge, research on the quality assessment of event logs remains scarce. Our work presents a user-guided and semi-automated approach for detecting and quantifying timestamp-related issues in event logs. We define 15 metrics related to timestamp quality across two axes: four levels of abstraction (event, activity, trace, log) and four quality dimensions (accuracy, completeness, consistency, uniqueness). The approach has been implemented as a prototype and evaluated regarding its design specification, instantiation, and usefulness in artificial and naturalistic settings by including experts from research and practice. Overall, our approach paves the way for a systematic and interactive enhancement of event log quality during the data preprocessing phase of Process Mining projects.