Abstract
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.
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Schonenberg, H., Sidorova, N., van der Aalst, W., van Hee, K. (2010). History-Dependent Stochastic Petri Nets. In: Pnueli, A., Virbitskaite, I., Voronkov, A. (eds) Perspectives of Systems Informatics. PSI 2009. Lecture Notes in Computer Science, vol 5947. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11486-1_31
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DOI: https://doi.org/10.1007/978-3-642-11486-1_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11485-4
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