|Annotating sBPMN elements with their likelihood of occurrence
|Annotation;Markov Chain;Process Model;sBPMN
|Journal / Series / Working Paper (HSU):
|CEUR Workshop Proceedings
|Workshops of SEMANTiCS 2017, Amsterdam, Netherlands, September 11 and 14, 2017
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 workow. These models require knowledge about the occurence of activities in the business process, which is usually based on expert knowledge or based on previous workows 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.
Free Open Access
|Organization Units (connected with the publication):
|Karlsruhe Institute of Technology
|Appears in Collections:
|6 - Publication references (only metadata) of your publications before HSU
Show full item record
Items in openHSU are protected by copyright, with all rights reserved, unless otherwise indicated.