The imprecisions of precision measures in process mining

Citation data:

Information Processing Letters, ISSN: 0020-0190, Vol: 135, Page: 1-8

Publication Year:
2018
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Repository URL:
http://arxiv.org/abs/1705.03303
DOI:
10.1016/j.ipl.2018.01.013
Author(s):
Tax, Niek; Lu, Xixi; Sidorova, Natalia; Fahland, Dirk; van der Aalst, Wil M. P.
Publisher(s):
Elsevier BV
Tags:
Mathematics; Computer Science; Computer Science - Databases; Computer Science - Artificial Intelligence; Computer Science - Logic in Computer Science; Computer Science - Software Engineering
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article description
In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been proposed throughout the years, no research has been done to validate whether these measures achieve the intended aim of quantifying over-approximation in a consistent way for all models and logs. This paper fills this gap by postulating a number of axioms for quantifying precision consistently for any log and any model. Further, we show through counter-examples that none of the existing measures consistently quantifies precision.