Time Series Petri Net Models - Enrichment and Prediction

Andreas Solti; Laura Vana Gür; Jan Mendling

Time Series Petri Net Models - Enrichment and Prediction

Andreas Solti
, Laura Vana Gür
, Jan Mendling

Institute for Data, Process and Knowledge Management

Publication: Chapter in book/Conference proceeding › Contribution to conference proceedings

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.

Original language	English
Title of host publication	Proceedings of the 5th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2015), Vienna, Austria, December 9-11, 2015.
Editors	Paolo Ceravolo, Stefanie Rinderle-Ma
Place of Publication	Wien
Publisher	CEUR Workshop Proceedings
Pages	109 - 123
Publication status	Published - 2015

Cite this

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title = "Time Series Petri Net Models - Enrichment and Prediction",

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.",

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Time Series Petri Net Models - Enrichment and Prediction. / Solti, Andreas; Vana Gür, Laura; Mendling, Jan.
Proceedings of the 5th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2015), Vienna, Austria, December 9-11, 2015.. ed. / Paolo Ceravolo, Stefanie Rinderle-Ma. Wien: CEUR Workshop Proceedings, 2015. p. 109 - 123.

Publication: Chapter in book/Conference proceeding › Contribution to conference proceedings

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AU - Solti, Andreas

AU - Vana Gür, Laura

AU - Mendling, Jan

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

UR - http://ceur-ws.org/Vol-1527/paper8.pdf

M3 - Contribution to conference proceedings

SP - 109

EP - 123

BT - Proceedings of the 5th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2015), Vienna, Austria, December 9-11, 2015.

A2 - Paolo Ceravolo, Stefanie Rinderle-Ma, null

PB - CEUR Workshop Proceedings

CY - Wien

ER -

Time Series Petri Net Models - Enrichment and Prediction

Abstract

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Cite this