Process mining techniques enable the discovery and analysis of business processes, identifying opportunities for improvement. However, processes are often comprised of separately managed procedures that have separate log files, impossible to mine in an integrative manner. A preprocessing step that merges logfiles is quite straightforward when the logs have common case IDs. However, when cases in the different logs have many-to-many relationships among them this is more challenging. In this paper we present an approach for merging event logs which is capable of dealing with all kinds of relationships between logs, one-to-one or many-to-many. The approach matches cases in the logs, using temporal relations and text mining techniques. We have implemented the algorithm and tested it on a comprehensive set of synthetic logs.
|Title of host publication||Business Process Management Workshops BPM 2014 International Workshops, Revised Papers|
|Editors||Fabiana Fournier, Jan Mendling|
|Number of pages||12|
|State||Published - 2015|
|Event||International Workshops on Business Process Management Workshops, BPM 2014 - Eindhoven, Netherlands|
Duration: 7 Sep 2014 → 8 Sep 2014
|Name||Lecture Notes in Business Information Processing|
|Conference||International Workshops on Business Process Management Workshops, BPM 2014|
|Period||7/09/14 → 8/09/14|
Bibliographical noteFunding Information:
This research was partly supported by the Israel Science Foundation, grant 856/13.
© Springer International Publishing Switzerland 2015.
- End-to-end process
- Merging logfiles
- Multiple instances
- Process mining
ASJC Scopus subject areas
- Control and Systems Engineering
- Management Information Systems
- Business and International Management
- Information Systems
- Modeling and Simulation
- Information Systems and Management