Measuring the interestingness of temporal logic behavioral specifications in process mining
作者：
Highlights：
• Exploitation of declarative linear-temporal-logic rules shaped as if-A-then-B rules.
• Association rule mining interestingness measures applied to temporal logic formulae.
• Measures build upon a probabilistic interpretation of linear temporal logic formulae.
• Computing the measures requires linear time and space in the size of the input data.
• Measures have different reactions in presence of noise depending on the given rule.
摘要
•Exploitation of declarative linear-temporal-logic rules shaped as if-A-then-B rules.•Association rule mining interestingness measures applied to temporal logic formulae.•Measures build upon a probabilistic interpretation of linear temporal logic formulae.•Computing the measures requires linear time and space in the size of the input data.•Measures have different reactions in presence of noise depending on the given rule.
论文关键词：Declarative process mining,Specification mining,Association rule mining,Interestingness measures,Temporal rules
论文评审过程：Received 14 March 2021, Revised 21 September 2021, Accepted 13 October 2021, Available online 1 November 2021, Version of Record 26 March 2022.
论文官网地址：https://doi.org/10.1016/j.is.2021.101920
