Abstract
Operational support as an area of process mining aims to predict the
temporal 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 this. 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.
temporal 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 this. 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.
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 5th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2015), Vienna, Austria, December 9-11, 2015. |
| Herausgeber*innen | Paolo Ceravolo, Stefanie Rinderle-Ma |
| Erscheinungsort | Wien |
| Verlag | CEUR Workshop Proceedings |
| Seiten | 109 - 123 |
| Publikationsstatus | Veröffentlicht - 2015 |