Full Support for Efficiently Mining Multi-Perspective Declarative Constraints from Process Logs

Title data

Sturm, Christian ; Fichtner, Myriel ; Schönig, Stefan:
Full Support for Efficiently Mining Multi-Perspective Declarative Constraints from Process Logs.
In: Information. Vol. 10 (2019) Issue 1 . - 29.
ISSN 2078-2489
DOI: https://doi.org/10.3390/info10010029

Official URL:

Abstract in another language

Declarative process management has emerged as an alternative solution for describing flexible workflows. In turn, the modelling opportunities with languages such as Declare are less intuitive and hard to implement. The area of process discovery covers the automatic discovery of process models. It has been shown that the performance of process mining algorithms, particularly when considering the multi-perspective declarative process models, are not satisfactory. State-of-the-art mining tools do not support multi-perspective declarative models at this moment. We address this open research problem by proposing an efficient mining framework that leverages the latest big data analysis technology and builds upon the distributed processing method MapReduce. The paper at hand further completes the research on multi-perspective declarative process mining by extending our previous work in various ways; in particular, we introduce algorithms and descriptions for the full set of commonly accepted types of MP-Declare constraints. Additionally, we provide a novel implementation concept allowing an easy introduction and discovery of customised constraint templates. We evaluated the mining performance and effectiveness of the presented approach on several real-life event logs. The results highlight that, with our efficient mining technique, multi-perspective declarative process models can be extracted in reasonable time.

Further data

Item Type:	Article in a journal
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:	07 Feb 2019 07:48
Last Modified:	02 Oct 2023 13:35
URI:	https://eref.uni-bayreuth.de/id/eprint/47221