Please use this identifier to cite or link to this item: doi:10.22028/D291-33411
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Title: Process Mining and the Black Swan: An Empirical Analysis of the Influence of Unobserved Behavior on the Quality of Mined Process Models
Author(s): Rehse, Jana-Rebecca
Fettke, Peter
Loos, Peter
Editor(s): Teniente, Ernest
Weidlich, Matthias
Language: English
Title: Business Process Management Workshops : BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers
Startpage: 256
Endpage: 268
Publisher/Platform: Springer
Year of Publication: 2018
Place of publication: Cham
Title of the Conference: BPM 2017
Place of the conference: Barcelona, Spain
Publikation type: Conference Paper
Abstract: In this paper, we present the epistomological problem of induction, illustrated by the metaphor of the black swan, and its relevance for Process Mining. The quality of mined models is typically measured in terms of four dimensions, namely fitness, precision, simplicity, and generalization. Both precision and generalization rely on the definition of “unobserved behavior”, i.e. traces not contained in the log. This paper is intended to analyze the influence of unobserved behavior, the potential black swan, has on the quality of mined models. We conduct an empirical analysis to investigate the relation between a system, its observed and unobserved behavior and the mined models. The results show that the unobserved behavior, mainly determined by the nature of the unknown system, can have a significant impact on the quality assessment of mined models, hence eliciting the need to explicate and discuss the assumptions underlying the notions of unobserved behavior in more depth.
DOI of the first publication: 10.1007/978-3-319-74030-0_19
URL of the first publication: https://link.springer.com/chapter/10.1007/978-3-319-74030-0_19
Link to this record: hdl:20.500.11880/30729
http://dx.doi.org/10.22028/D291-33411
ISBN: 978-3-319-74030-0
978-3-319-74029-4
Date of registration: 24-Feb-2021
Notes: Lecture notes in business information processing ; 308
Faculty: HW - Fakultät für Empirische Humanwissenschaften und Wirtschaftswissenschaft
Department: HW - Wirtschaftswissenschaft
Professorship: HW - Prof. Dr. Peter Loos
Collections:Die Universitätsbibliographie

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