P3-folder: Optimal model simplification for improving accuracy in process performance prediction

Arik Senderovich, Alexander Shleyfman, Matthias Weidlich, Avigdor Gal, Avishai Mandelbaum

Research output: Contribution to journalConference articlepeer-review


Operational process models such as generalised stochastic Petri nets (GSPNs) are useful when answering performance queries on business processes (e.g. ‘how long will it take for a case to finish?’). Recently, methods for process mining have been developed to discover and enrich operational models based on a log of recorded executions of processes, which enables evidence-based process analysis. To avoid a bias due to infrequent execution paths, discovery algorithms strive for a balance between over-fitting and under-fitting regarding the originating log. However, state-of-the-art discovery algorithms address this balance solely for the control-flow dimension, neglecting possible over-fitting in terms of performance annotations. In this work, we thus offer a technique for performance-driven model reduction of GSPNs, using structural simplification rules. Each rule induces an error in performance estimates with respect to the original model. However, we show that this error is bounded and that the reduction in model parameters incurred by the simplification rules increases the accuracy of process performance prediction. We further show how to find an optimal sequence of applying simplification rules to obtain a minimal model under a given error budget for the performance estimates. We evaluate the approach with a realworld case in the healthcare domain, showing that model simplification indeed yields significant improvements in time prediction accuracy.

Original languageEnglish
Pages (from-to)418-436
Number of pages19
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9850 LNCS
StatePublished - 2016
Externally publishedYes
EventInternational Conference on Business Process Management, BPM 2016 - Rio de Janeiro, Brazil
Duration: 18 Sep 201622 Sep 2016

Bibliographical note

Funding Information:
This work was partially supported by the German Research Foundation (DFG), grant WE 4891/1-1.

Publisher Copyright:
© Springer International Publishing Switzerland 2016.


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