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Abstract
Process mining is the area of research that embraces the automated discovery, conformance checking and enhancement of process models. Declarative process mining approaches offer capabilities to automatically discover models of flexible processes from event logs. However, they often suffer from performance issues with real-life event logs, especially when constraints to be discovered go beyond a standard repertoire of templates. By leveraging relational database performance technology, a new approach based on SQL querying has been recently introduced, to improve performance though still keeping the nature of discovered constraints customisable. In this paper, we provide an in-depth analysis of configuration parameters that allow for a speed-up of the answering time and a decrease of storage space needed for query processing. Thereupon, we provide configuration recommendations for process mining with SQL on relational databases.
Originalsprache | Englisch |
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Titel des Sammelwerks | Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC 2019 |
Herausgeber*innen | Chih-Cheng Hung and George A. Papadopoulos |
Erscheinungsort | Limassol, Cyprus |
Verlag | ACM Press |
Seiten | 94 - 97 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2019 |
Österreichische Systematik der Wissenschaftszweige (ÖFOS)
- 102022 Softwareentwicklung
- 102
- 502
- 502050 Wirtschaftsinformatik
Projekte
- 1 Abgeschlossen
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Cyber-Physical Social Systems for City-wide Infrastructures
Cecconi, A., Di Ciccio, C., Fernandez Garcia, J. D., Mendling, J. & Polleres, A.
1/10/17 → 31/03/20
Projekt: Forschungsförderung