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Title: Root Cause Analysis in Process Mining with Probabilistic Temporal Logic
Authors: VAN HOUDT, Greg 
DEPAIRE, Benoit 
MARTIN, Niels 
Issue Date: 2022
Publisher: Springer
Source: Jorge Munoz-Gama, Xixi Lu (Ed.), Process Mining Workshops ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 – November 4, 2021, Revised Selected Papers, Springer, p.73-84
Series/Report: Lecture Notes in Business Information Processing
Series/Report no.: 433
Abstract: Process mining is a research domain that enables businesses to analyse and improve their processes by extracting insights from event logs. While determining the root causes of, for example, a negative case outcome can provide valuable insights for business users, only limited research has been conducted to uncover true causal relations within the process mining field. Therefore, this paper proposes AITIA-PM, a novel technique to measure cause-effect relations in event logs based on causality theory. The AITIA-PM algorithm employs probabilistic temporal logic to formally yet flexibly define hypotheses and then automatically tests them for causal relations from data. We demonstrate this by applying AITIA-PM on a real-life dataset. The case study shows that, after a well-thought-out hypotheses definition and information extraction, the AITIA-PM algorithm can be applied on rich event logs, expanding the possibilities of meaningful root cause analysis in a process mining context.
Keywords: Process Mining;Root Cause Analysis;Probabilistic Temporal Logic;Event Log
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ISBN: 9783030985806
DOI: 10.1007/978-3-030-98581-3_6
ISI #: 000787744500006
Category: C1
Type: Proceedings Paper
Appears in Collections:Research publications

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