History-dependent stochastic Petri nets

H. Schonenberg, N. Sidorova, W.M.P. Aalst, van der, K.M. Hee, van

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

4 Citations (Scopus)
3 Downloads (Pure)


Stochastic Petri Nets are a useful and well-known tool for performance analysis. However, an implicit assumption in the different types of Stochastic Petri Nets is the Markov property. It is assumed that a choice in the Petri net only depends on the current state and not on earlier choices. For many real-life processes, choices made in the past can influence choices made later in the process. For example, taking one more iteration in a loop might increase the probability to leave the loop, etc. In this paper, we introduce a novel framework where probability distributions depend not only on the marking of the net, but also on the history of the net. We also describe a number of typical abstraction functions for capturing relevant aspects of the net’s history and show how we can discover the probabilistic mechanism from event logs, i.e. real-life observations are used to learn relevant correlations. Finally, we present how our nets can be modelled and simulated using CPN Tools and discuss the results of some simulation experiments.
Original languageEnglish
Title of host publicationPerspectives of systems informatics : 7th International Andrei Ershov Memorial Conference, PSI 2009, Novosibirsk, Russia, June 15-19, 2009 : revised papers
EditorsA. Pnueli, I. Virbitskaite, A. Voronkov
Place of PublicationBerlin
ISBN (Print)978-3-642-11485-4
Publication statusPublished - 2010

Publication series

NameLecture Notes in Computer Science


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