Annotating sBPMN elements with their likelihood of occurrence
Publication date
2017-09-01
Document type
Conference paper
Author
Weller, Tobias
Organisational unit
Karlsruhe Institute of Technology
Scopus ID
ISSN
Conference
Workshops of SEMANTiCS 2017, Amsterdam, Netherlands, September 11 and 14, 2017
Series or journal
CEUR Workshop Proceedings
Periodical volume
2063
Peer-reviewed
✅
Part of the university bibliography
Nein
Keyword
Annotation
Markov Chain
Process Model
sBPMN
Abstract
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.
Description
Free Open Access
Version
Not applicable (or unknown)
Access right on openHSU
Metadata only access