Free choice Petri nets
Modelling with Generalized Stochastic Petri Nets
ACM SIGMETRICS Performance Evaluation Review - Special issue on Stochastic Petri Nets
Generalized Stochastic Petri Nets Revisitied: Random Switches and Priorities
PNPM '87 The Proceedings of the Second International Workshop on Petri Nets and Performance Models
On the integration of delay and throughput measures in distributed processing models
On the integration of delay and throughput measures in distributed processing models
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
Coloured Petri Nets and CPN Tools for modelling and validation of concurrent systems
International Journal on Software Tools for Technology Transfer (STTT)
Discovering colored Petri nets from event logs
International Journal on Software Tools for Technology Transfer (STTT)
Supporting Flexible Processes through Recommendations Based on History
BPM '08 Proceedings of the 6th International Conference on Business Process Management
ICATPN'07 Proceedings of the 28th international conference on Applications and theory of Petri nets and other models of concurrency
Business trend analysis by simulation
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
Time prediction based on process mining
Information Systems
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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.