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Erschienen in: Business & Information Systems Engineering 6/2019

04.12.2018 | Research Paper

Towards Confirmatory Process Discovery: Making Assertions About the Underlying System

verfasst von: Gert Janssenswillen, Benoît Depaire

Erschienen in: Business & Information Systems Engineering | Ausgabe 6/2019

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Abstract

The focus in the field of process mining, and process discovery in particular, has thus far been on exploring and describing event data by the means of models. Since the obtained models are often directly based on a sample of event data, the question whether they also apply to the real process typically remains unanswered. As the underlying process is unknown in real life, there is a need for unbiased estimators to assess the system-quality of a discovered model, and subsequently make assertions about the process. In this paper, an experiment is described and discussed to analyze whether existing fitness, precision and generalization metrics can be used as unbiased estimators of system fitness and system precision. The results show that important biases exist, which makes it currently nearly impossible to objectively measure the ability of a model to represent the system.

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Fußnoten
1
In practice, these costs can be configured for each activity type individually, to reflect that certain deviations should be penalized more than others.
 
2
Optimal alignments are the alignments for which the cost is minimized.
 
3
The types of noise used have been defined based on existing literature (Maruster 2003). However, for future experiments, a more elaborate reasoning for what qualifies as realistic noise is necessary. For example, the swapping of random activities is not really a realistic event. A detailed discussion of what can be regarded as noise is out of the scope of this paper.
 
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Metadaten
Titel
Towards Confirmatory Process Discovery: Making Assertions About the Underlying System
verfasst von
Gert Janssenswillen
Benoît Depaire
Publikationsdatum
04.12.2018
Verlag
Springer Fachmedien Wiesbaden
Erschienen in
Business & Information Systems Engineering / Ausgabe 6/2019
Print ISSN: 2363-7005
Elektronische ISSN: 1867-0202
DOI
https://doi.org/10.1007/s12599-018-0567-8

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