Title: Annotating sBPMN elements with their likelihood of occurrence
Authors: Weller, Tobias
Maleshkova, Maria 
Language: eng
Keywords: Annotation;Markov Chain;Process Model;sBPMN
Issue Date: 1-Sep-2017
Publisher: CEUR
Document Type: Conference Object
Journal / Series / Working Paper (HSU): CEUR Workshop Proceedings
Volume: 2063
Publisher Place: Amsterdam
Conference: 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
URL: https://www.aifb.kit.edu/images/f/fc/ISemantics2017_Weller.pdf
ISSN: 16130073
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