Learning the Control-Flow of a Business Process Using ICN-Based Process Models

Authors:
Aubrey J. Rembert;Clarence (Skip) Ellis
Affiliations:
IBM T.J. Watson Research Center, Hawthorne, USA NY 10532;Department of Computer Science, University of Colorado at Boulder, Boulder, US 80306
Venue:
ICSOC-ServiceWave '09 Proceedings of the 7th International Joint Conference on Service-Oriented Computing
Year:
2009

Citing 7
Cited 1

Discovering models of software processes from event-based data

ACM Transactions on Software Engineering and Methodology (TOSEM)
Mining Process Models from Workflow Logs

EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Workflow mining with InWoLvE

Computers in Industry - Special issue: Process/workflow mining
Workflow Mining: Discovering Process Models from Event Logs

IEEE Transactions on Knowledge and Data Engineering
Probabilistic workflow mining

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Genetic process mining: an experimental evaluation

Data Mining and Knowledge Discovery
Automatic discovery of workflow models

Automatic discovery of workflow models

Business process mining from e-commerce web logs

BPM'13 Proceedings of the 11th international conference on Business Process Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a process mining algorithm that discovers Activity Precedence Graphs (APG), which are control-flow models in the Generalized Information Control Net (ICN) family of models. Unlike many other control-flow models discovered by process mining algorithms, APGs can be integrated with other business process perspectives.