Retrieving batch organisation of work insights from event logs

Martin, Niels; Swennen, Marijke; Depaire, Benoît; Jans, Mieke; Caris, An; Vanhoof, Koen

doi:10.1016/j.dss.2017.02.012

Cited by 35 publications

(15 citation statements)

References 17 publications

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“…These insights come with limitations: First, we intend to minimize the risk that existing issues remain undetected (false negatives). This sensitivity, however, can cause the approach to identify issues that may be false alarms (false positives) such as uniqueness issues caused by batched events [20]. However, we mitigate potential over-detection by allowing users to review detected issues and plan to implement a white-list functionality as part of future work.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

Enhancing Event Log Quality: Detecting and Quantifying Timestamp Imperfections

Fischer

Goel

Andrews

et al. 2020

Lecture Notes in Computer Science

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“…Performance of cases synchronizing on shared resources can be analyzed through simulation models [16] or from queuing models [19] learned from event logs. Synchronization in batch activities can be studied through queue models [13], or through aggregating event logs into a matrix [10].…”

Section: Related Workmentioning

confidence: 99%

Unbiased, Fine-Grained Description of Processes Performance from Event Data

Denisov

Fahland

Aalst

2018

Lecture Notes in Computer Science

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“…While Wen et al (2013) focus on the control-flow, Nakatumba (2013) states that batching behaviour is present when two sets of activity instances on a particular working day are separated by a time period of more than one hour. A more systematic approach is proposed in Martin et al (2017). Based on a distinction between simultaneous, concurrent and sequential batch processing, an algorithm is proposed which detects batching behaviour.…”

Section: Batch Processingmentioning

confidence: 99%

“…Based on a distinction between simultaneous, concurrent and sequential batch processing, an algorithm is proposed which detects batching behaviour. Moreover, metrics such as the batch size are calculated which can be used to specify batching parameters in a simulation model (Martin et al 2017).…”

Section: Batch Processingmentioning

confidence: 99%