Time Series Petri Net Models View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2017

AUTHORS

Andreas Solti , Laura Vana , Jan Mendling

ABSTRACT

Operational support as an area of process mining aims to predict the performance of individual cases and the overall business process. Although seasonal effects, delays and performance trends are well-known to exist for business processes, there is up until now no prediction model available that explicitly captures seasonality. In this paper, we introduce time series Petri net models. These models integrate the control flow perspective of Petri nets with time series prediction. Our evaluation on the basis of our prototypical implementation demonstrates the merits of this model in terms of better accuracy in the presence of time series effects. More... »

PAGES

124-141

References to SciGraph publications

Book

TITLE

Data-Driven Process Discovery and Analysis

ISBN

978-3-319-53434-3
978-3-319-53435-0

From Grant

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-53435-0_6

DOI

http://dx.doi.org/10.1007/978-3-319-53435-0_6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1074229038


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