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Measure of Uncertainty in Process Models Using Stochastic Petri Nets and Shannon Entropy

Institute of System Engineering and Informatics, University of Pardubice, Pardubice 532 10, Czech Republic
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Academic Editor: Raúl Alcaraz Martínez
Entropy 2016, 18(1), 33; https://doi.org/10.3390/e18010033
Received: 4 May 2015 / Revised: 6 January 2016 / Accepted: 10 January 2016 / Published: 19 January 2016
(This article belongs to the Section Information Theory, Probability and Statistics)
When modelling and analysing business processes, the main emphasis is usually put on model validity and accuracy, i.e., the model meets the formal specification and also models the relevant system. In recent years, a series of metrics has begun to develop, which allows the quantification of the specific properties of process models. These characteristics are, for instance, complexity, comprehensibility, cohesion, and uncertainty. This work is focused on defining a method that allows us to measure the uncertainty of a process model, which was modelled by using stochastic Petri nets (SPN). The principle of this method consists of mapping of all reachable marking of SPN into the continuous-time Markov chain and then calculating its stationary probabilities. The uncertainty is then measured as the entropy of the Markov chain (it is possible to calculate the uncertainty of the specific subset of places as well as of whole net). Alternatively, the uncertainty index is quantified as a percentage of the calculated entropy against maximum entropy (the resulting value is normalized to the interval <0,1>). The calculated entropy can also be used as a measure of the model complexity. View Full-Text
Keywords: uncertainty; entropy; stochastic Petri nets; modelling; complexity metrics uncertainty; entropy; stochastic Petri nets; modelling; complexity metrics
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Ibl, M.; Čapek, J. Measure of Uncertainty in Process Models Using Stochastic Petri Nets and Shannon Entropy. Entropy 2016, 18, 33.

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