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

Institute for Computer Science, University of Bayreuth, 95447 Bayreuth, Germany
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This paper is an extended version of conference paper: Sturm C., Schönig S. and Jablonski S. A MapReduce Approach for Mining Multi-Perspective Declarative Process Models. In Proceedings of the 20th International Conference on Enterprise Information Systems, 2018.
Information 2019, 10(1), 29; https://doi.org/10.3390/info10010029
Received: 21 November 2018 / Revised: 5 January 2019 / Accepted: 10 January 2019 / Published: 15 January 2019
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. View Full-Text
Keywords: declarative process management; process mining; process discovery; mp-declare; mapreduce; big data declarative process management; process mining; process discovery; mp-declare; mapreduce; big data
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MDPI and ACS Style

Sturm, C.; Fichtner, M.; Schönig, S. Full Support for Efficiently Mining Multi-Perspective Declarative Constraints from Process Logs. Information 2019, 10, 29. https://doi.org/10.3390/info10010029

AMA Style

Sturm C, Fichtner M, Schönig S. Full Support for Efficiently Mining Multi-Perspective Declarative Constraints from Process Logs. Information. 2019; 10(1):29. https://doi.org/10.3390/info10010029

Chicago/Turabian Style

Sturm, Christian, Myriel Fichtner, and Stefan Schönig. 2019. "Full Support for Efficiently Mining Multi-Perspective Declarative Constraints from Process Logs" Information 10, no. 1: 29. https://doi.org/10.3390/info10010029

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