bei Google ScholarTitelangaben
Sturm, Christian ; Schönig, Stefan:
Big Data Meets Process Science : Distributed Mining of MP-Declare Process Models.
In: Hammoudi, Slimane ; Śmiałek, Michał ; Camp, Olivier ; Filipe, Joaquim
(Hrsg.):
Enterprise Information Systems. -
Cham
: Springer
,
2019
. - S. 396-423
ISBN 978-3-030-26169-6
DOI: https://doi.org/10.1007/978-3-030-26169-6_19
Abstract
Process mining techniques allow the user to build a process model representing the process behavior as recorded in the logs. Standard process discovery techniques produce as output a procedural process model. Recently, several approaches have been developed to extract declarative process models from logs and have been proven to be more suitable to analyze flexible processes, which frequently depend on human decisions and are less predictable. However, when analyzing declarative constraints from other perspective than the control flow, such as data and resources, existing process mining techniques turned out to be inefficient. Thus, computational performance remains a key challenge of declarative process discovery. In this paper, we present a high-performance approach for the discovery of multi-perspective declarative process models that is built upon the distributed big data processing method MapReduce. Compared to recent work we provide an in-depth analysis of an implementation approach based on Hadoop, a powerful BigData-Framework, and describe detailed information on the implemented prototype. We evaluated effectiveness and efficiency of the approach on real-life event logs.
Weitere Angaben
| Publikationsform: | Aufsatz in einem Buch |
|---|---|
| Begutachteter Beitrag: | Ja |
| Institutionen der Universität: | Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Angewandte Informatik IV > Lehrstuhl Angewandte Informatik IV - Univ.-Prof. Dr.-Ing. Stefan Jablonski Fakultäten Fakultäten > Fakultät für Mathematik, Physik und Informatik Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Angewandte Informatik IV |
| Titel an der UBT entstanden: | Ja |
| Themengebiete aus DDC: | 000 Informatik,Informationswissenschaft, allgemeine Werke > 004 Informatik |
| Eingestellt am: | 30 Jul 2019 11:48 |
| Letzte Änderung: | 30 Jul 2019 11:48 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/51615 |