Entropic relevance: A mechanism for measuring stochastic process models discovered from event data
作者：
Highlights：
• The stochastic aspects of logs and models are important in conformance checking.
• Entropic relevance is a measure for stochastic conformance checking.
• It is grounded in the minimum description length model selection principle.
• It is fundamentally different from other conformance checking measures.
• It implements a compromise between classical conformance checking measures.
摘要
•The stochastic aspects of logs and models are important in conformance checking.•Entropic relevance is a measure for stochastic conformance checking.•It is grounded in the minimum description length model selection principle.•It is fundamentally different from other conformance checking measures.•It implements a compromise between classical conformance checking measures.
论文关键词：Process mining,Conformance checking,Stochastic conformance checking,Model inference,Model quality,Process learning,Explainable AI
论文评审过程：Received 25 February 2021, Revised 23 September 2021, Accepted 14 October 2021, Available online 5 November 2021, Version of Record 26 March 2022.
论文官网地址：https://doi.org/10.1016/j.is.2021.101922
