Analyzing and repairing overlapping work items in process logs

Awad, Ahmed; Zaki, Nesma M.; Francescomarino, Chiara Di

doi:10.1016/j.infsof.2016.08.010

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…We also detect overlapping events per resource provided that the start and end times of an activity that is executed by a resource are recorded in an event log. This can indicate imprecise recordings of start or end timestamps of activities if we assume that a resource does not multitask since the metric identifies activities that are started by a specific resource before they finished their prior activity [6]. M 4 : Precision (Quality dimension: accuracyabstraction level: event)aims to detect events containing coarse timestamps.…”

Section: Detection and Quantification Of Timestamp Imperfectionsmentioning

confidence: 99%

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Enhancing Event Log Quality: Detecting and Quantifying Timestamp Imperfections

Fischer

Goel

Andrews

et al. 2020

Lecture Notes in Computer Science

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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 analysis results. Thus, the quality of timestamps in an event log should be evaluated thoroughly before the log is used as input for any Process Mining activity. To the best of our knowledge, research on the (automated) quality assessment of event logs remains scarce. Our work presents an automated approach for detecting and quantifying timestamp-related issues (timestamp imperfections) in an event log. 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). We adopted the design science research paradigm and drew from knowledge related to data quality as well as event log quality. The approach has been implemented as a prototype within the open-source Process Mining framework ProM and evaluated using three real-life event logs and involving experts from practice. This approach paves the way for a systematic and interactive enhancement of timestamp imperfections during the data preprocessing phase of Process Mining projects.

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Section: Detection and Quantification Of Timestamp Imperfectionsmentioning

confidence: 99%

“…We also identified research that addresses the issue of overlapping events, which aims to detect resource overlap between events. For instance, according to the log, a nurse may begin a new patient transfer before completing the previous patient transfer [6]. As a result, for our approach, we specified the following objective: DO 2.…”

Section: Introductionmentioning

confidence: 99%