Abstract
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.
Highlights
A research area that focuses on the analysis of process models began to develop and since a number of various specific metrics has been defined that allow the quantification of different characteristics of process models
The aim of this work is to define a method that allows us to quantify the uncertainty of any process model represented with stochastic Petri nets
The result can be roughly interpreted as the situation that the uncertainty of the example stochastic Petri net (SPN) reaches 90.15% of the maximum
Summary
A research area that focuses on the analysis of process models began to develop and since a number of various specific metrics has been defined that allow the quantification of different characteristics of process models. These characteristics include, for instance, complexity [1,2], uncertainty [3], cohesion [4] or fairness [5]. A procedure (process) is designed for the analysis and evaluation of uncertainty of any process model represented using stochastic Petri nets (SPN). By considering the probability of branching, it is possible to quantify the dynamic uncertainty, which takes into account both the model structure and the branching probabilities
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