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.
Paolo Ceravolo; Stefanie Rinderle-Ma. 5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Dec 2015, Vienna, Austria. Springer International Publishing, Lecture Notes in Business Information Processing, LNBIP-244, pp.124-141, 2017, Data-Driven Process Discovery and Analysis. 〈10.1007/978-3-319-53435-0_6〉
https://hal.inria.fr/hal-01651885
Contributeur : Hal Ifip
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Soumis le : mercredi 29 novembre 2017 - 16:06:33
Dernière modification le : mercredi 29 novembre 2017 - 16:34:51
Andreas Solti, Laura Vana, Jan Mendling. Time Series Petri Net Models. Paolo Ceravolo; Stefanie Rinderle-Ma. 5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Dec 2015, Vienna, Austria. Springer International Publishing, Lecture Notes in Business Information Processing, LNBIP-244, pp.124-141, 2017, Data-Driven Process Discovery and Analysis. 〈10.1007/978-3-319-53435-0_6〉. 〈hal-01651885〉
