A preliminary version of this paper appeared in the International Workshop on Workflow Management in Service and Cloud Computing (2010).
Special Issue Paper
Mining batch processing workflow models from event logs†
Article first published online: 19 FEB 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Concurrency and Computation: Practice and Experience
Special Issue: Second international workshop on workflow management in service and cloud computing (WMSC2010)
Volume 25, Issue 13, pages 1928–1942, 10 September 2013
How to Cite
Wen, Y., Chen, Z., Liu, J. and Chen, J. (2013), Mining batch processing workflow models from event logs. Concurrency Computat.: Pract. Exper., 25: 1928–1942. doi: 10.1002/cpe.2991
- Issue published online: 18 JUL 2013
- Article first published online: 19 FEB 2013
- Manuscript Accepted: 13 DEC 2012
- Manuscript Revised: 20 SEP 2012
- Manuscript Received: 20 JUL 2012
- NSFC. Grant Number: 90818004, 61073186, 61100054, 61073104
- Program for New Century Excellent Talents in University. Grant Number: NCET-10-0140
- Excellent Youth Found of Hunan Scientific Committee. Grant Number: 11JJ1011
- Scientific Research Fund of Hunan Provincial Education Department. Grant Number: 09K085
- Planned Science and Technology Project of Hunan Province of China. Grant Number: 2011FJ3133
- batch processing;
- process mining;
- batch processing area;
The employment of batch processing in workflow is to model and schedule activity instances in multiple workflow cases of the same workflow type to optimize business processes execution dynamically. Although our previous works have preliminarily investigated its model and implementation, it is still necessary to deal with its model design problem. Process mining techniques allow for the automated discovery of process models from event logs and have received notable attentions in researches recently. Following these researches, this paper proposes an approach to mine batch processing workflow models from event logs by considering the batch processing relations among activity instances in multiple workflow cases. The notion of batch processing feature and its corresponding mining algorithm are also presented for discovering the batch processing area in the model by using the input and output data information of activity instances in events. The algorithms presented in this paper can help to enhance the applicability of existing process mining approaches and broaden the process mining spectrum. Copyright © 2013 John Wiley & Sons, Ltd.