at Google ScholarTitle data
Sturm, Christian ; Schönig, Stefan ; Jablonski, Stefan:
A MapReduce Approach for Mining Multi-Perspective Declarative Process Models.
In:
Proceedings of the 20th International Conference on Enterprise Information Systems. Volume 2. ICEIS. -
Funchal, Madeira
: SCITEPRESS
,
2018
. - pp. 585-595
ISBN 978-989-758-298-1
DOI: https://doi.org/10.5220/0006710305850595
Abstract in another language
Automated process discovery aims at generating a process model from an event log. Such models can be represented as a set of declarative constraints where temporal coherencies can also be intertwined with dependencies upon value ranges of data parameters and resource characteristics. Existing mining tools do not support multi-perspective constraint discovery or are not efficient enough. In this paper, we propose an efficient mining framework for discovering multi-perspective declarative models that builds upon the distributed processing method MapReduce. Mining performance and effectiveness have been tested on several real-life event logs.
Further data
| Item Type: | Article in a book |
|---|---|
| Refereed: | Yes |
| Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV > Chair Applied Computer Science IV - Univ.-Prof. Dr.-Ing. Stefan Jablonski Faculties Faculties > Faculty of Mathematics, Physics und Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV |
| Result of work at the UBT: | Yes |
| DDC Subjects: | 000 Computer Science, information, general works > 004 Computer science |
| Date Deposited: | 21 Dec 2017 07:56 |
| Last Modified: | 31 Jan 2024 11:13 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/41064 |