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Annotating sBPMN Elements with their Likelihood of Occurrence

Annotating sBPMN Elements with their Likelihood of Occurrence

Published: 2017 September

Buchtitel: SALAD∂Semantics2017
Verlag: Springer
Organisation: SEMANTiCS conference

Referierte Veröffentlichung


Process Mining is a research discipline that aims to analyze business processes based on event logs. The event logs are among others used to create models for predicting the next activity of a given process instance. Existing models use Bayesian Networks or Markov Chains to predict the next activity in a workflow. These models require knowledge about the occurence of activities in the business process, which is usually based on expert knowledge or based on previous workflows from event logs. Based on previous work, we will i) represent a business process in sBPMN and extend our annotation tool to ii) compute the likelihood of occurrence of activities in a business process and check for stochastic dependency in a process and iii) use the generated knowledge to annotate the business process.

Download: Media:ISemantics2017 Weller.pdf


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