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2016 | OriginalPaper | Buchkapitel

A Visual Approach to Spot Statistically-Significant Differences in Event Logs Based on Process Metrics

verfasst von : Alfredo Bolt, Massimiliano de Leoni, Wil M. P. van der Aalst

Erschienen in: Advanced Information Systems Engineering

Verlag: Springer International Publishing

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Abstract

This paper addresses the problem of comparing different variants of the same process. We aim to detect relevant differences between processes based on what was recorded in event logs. We use transition systems to model behavior and to highlight differences. Transition systems are annotated with measurements, used to compare the behavior in the variants. The results are visualized as transitions systems, which are colored to pinpoint the significant differences. The approach has been implemented in ProM, and the implementation is publicly available. We validated our approach by performing experiments using real-life event data. The results show how our technique is able to detect relevant differences undetected by previous approaches while it avoids detecting insignificant differences.

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Vorheriges Kapitel ADInnov: An Intentional Method to Instil Innovation in Socio-Technical Ecosystems

Nächstes Kapitel Business Process Performance Mining with Staged Process Flows

Note that the example transition system used in this figure is different than previous examples, and it is used for illustration purposes only.

This is not observable in the picture but, in the implementation, by clicking on a state/transition, one can read this information in a popup equivalent to the two shown in Fig. 6.

According to the Italian laws, if a fine is not paid in full within 90 days, a penalty is added so that the due amount doubles.

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Titel: A Visual Approach to Spot Statistically-Significant Differences in Event Logs Based on Process Metrics
verfasst von: Alfredo Bolt
Massimiliano de Leoni
Wil M. P. van der Aalst
Verlag: Springer International Publishing
Buch: Advanced Information Systems Engineering
Print ISBN: 978-3-319-39695-8

Electronic ISBN: 978-3-319-39696-5

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-39696-5_10

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