Feature Recommendation for Structural Equation Model Discovery in Process Mining

• URL: http://arxiv.org/abs/2108.07795v1
• Date: Fri, 13 Aug 2021 12:23:01 GMT
• Title: Feature Recommendation for Structural Equation Model Discovery in Process Mining
• Authors: Mahnaz Sadat Qafari and Wil van der Aalst
• Abstract summary: We propose a method for finding the set of (aggregated) features with a possible effect on the problem. We have implemented the proposed method as a plugin in ProM and we have evaluated it using two real and synthetic event logs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Process mining techniques can help organizations to improve their operational processes. Organizations can benefit from process mining techniques in finding and amending the root causes of performance or compliance problems. Considering the volume of the data and the number of features captured by the information system of today's companies, the task of discovering the set of features that should be considered in root cause analysis can be quite involving. In this paper, we propose a method for finding the set of (aggregated) features with a possible effect on the problem. The root cause analysis task is usually done by applying a machine learning technique to the data gathered from the information system supporting the processes. To prevent mixing up correlation and causation, which may happen because of interpreting the findings of machine learning techniques as causal, we propose a method for discovering the structural equation model of the process that can be used for root cause analysis. We have implemented the proposed method as a plugin in ProM and we have evaluated it using two real and synthetic event logs. These experiments show the validity and effectiveness of the proposed methods.

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