TY - JOUR
T1 - The imprecisions of precision measures in process mining
AU - Tax, N.
AU - Lu, X.
AU - Sidorova, N.
AU - Fahland, D.
AU - van der Aalst, W.M.P.
PY - 2018/7
Y1 - 2018/7
N2 - 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.
AB - 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.
KW - Design of algorithms
KW - Formal languages and automata
KW - Petri nets
KW - Process mining
UR - http://www.scopus.com/inward/record.url?scp=85042203273&partnerID=8YFLogxK
U2 - 10.1016/j.ipl.2018.01.013
DO - 10.1016/j.ipl.2018.01.013
M3 - Article
AN - SCOPUS:85042203273
SN - 0020-0190
VL - 135
SP - 1
EP - 8
JO - Information Processing Letters
JF - Information Processing Letters
ER -