Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs

Suriadi, S.; Andrews, Robert; Hofstede, Arthur H. M. ter; Wynn, Moe Thandar

doi:10.1016/j.is.2016.07.011

Cited by 190 publications

(149 citation statements)

References 31 publications

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“…In practice, event logs often contain noise, e.g., out-of-order events, exceptional behavior, or recording errors [4]. Including all such infrequent events in the process discovery often leads to unusable, complex models.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Mannhardt

Leoni

Reijers

et al. 2017

Lecture Notes in Computer Science

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Section: Introductionmentioning

confidence: 99%

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Mannhardt

Leoni

Reijers

et al. 2017

Lecture Notes in Computer Science

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“…This can be considered as a form of non-explicit uncertainty: no measure or indication on the nature of the uncertainty is given in the event log. The work of Suriadi et al [11] provides a taxonomy of this type of issues in event logs, laying out a series of data patterns that model errors in process data. In these cases, and if this behavior is infrequent enough to allow the event log to remain meaningful, the most common way for existing process mining techniques to deal with missing data is by filtering out the affected traces and performing discovery and conformance checking on the resulting filtered event log.…”

Section: Related Workmentioning

confidence: 99%

Mining Uncertain Event Data in Process Mining

Pegoraro

Aalst

2019

2019 International Conference on Process Mining (ICPM)

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0000−0002−8997−7517] and Wil M.P. van der Aalst [0000−0002−0955−6940]Abstract. Nowadays, more and more process data are automatically recorded by information systems, and made available in the form of event logs. Process mining techniques enable process-centric analysis of data, including automatically discovering process models and checking if event data conform to a certain model. In this paper we analyze the previously unexplored setting of uncertain event logs: logs where quantified uncertainty is recorded together with the corresponding data. We define a taxonomy of uncertain event logs and models, and we examine the challenges that uncertainty poses on process discovery and conformance checking. Finally, we show how upper and lower bounds for conformance can be obtained aligning an uncertain trace onto a regular process model.

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“…Real life events logs often contain all sorts of data quality issues [34], include incorrectly logged events, events that are logged in the wrong order, and events that took place without being logged. Instances of such data quality issues are often referred to as noise.…”

Section: Related Workmentioning

confidence: 99%

Discovering more precise process models from event logs by filtering out chaotic activities

2018

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Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that business process. Real life event logs can contain chaotic activities. These activities are independent of the state of the process and can, therefore, happen at rather arbitrary points in time. We show that the presence of such chaotic activities in an event log heavily impacts the quality of the process models that can be discovered with process discovery techniques. The current modus operandi for filtering activities from event logs is to simply filter out infrequent activities. We show that frequency-based filtering of activities does not solve the problems that are caused by chaotic activities. Moreover, we propose a novel technique to filter out chaotic activities from event logs. We evaluate this technique on a collection of seventeen reallife event logs that originate from both the business process management domain and the smart home environment domain. As demonstrated, the developed activity filtering methods enable the discovery of process models that are more behaviorally specific compared to process models that are discovered using standard frequency-based filtering.

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Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs

Cited by 190 publications

References 31 publications

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Mining Uncertain Event Data in Process Mining

Discovering more precise process models from event logs by filtering out chaotic activities

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