Academic Journal
Causal Learning: Monitoring Business Processes Based on Causal Structures
| العنوان: | Causal Learning: Monitoring Business Processes Based on Causal Structures |
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| المؤلفون: | Fernando Montoya, Hernán Astudillo, Daniela Díaz, Esteban Berríos |
| المصدر: | Entropy, Vol 26, Iss 10, p 867 (2024) |
| بيانات النشر: | MDPI AG, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Science LCC:Astrophysics LCC:Physics |
| مصطلحات موضوعية: | causal graph, causal attribution of anomalies, causal attribution of distributional change, business process monitoring, business process mining, Science, Astrophysics, QB460-466, Physics, QC1-999 |
| الوصف: | Conventional methods for process monitoring often fail to capture the causal relationships that drive outcomes, making hard to distinguish causal anomalies from mere correlations in activity flows. Hence, there is a need for approaches that allow causal interpretation of atypical scenarios (anomalies), allowing to identify the influence of operational variables on these anomalies. This article introduces (CaProM), an innovative technique based on causality techniques, applied during the planning phase in business process environments. The technique combines two causal perspectives: anomaly attribution and distribution change attribution. It has three stages: (1) process events are collected and recorded, identifying flow instances; (2) causal learning of process activities, building a directed acyclic graphs (DAGs) represent dependencies among variables; and (3) use of DAGs to monitor the process, detecting anomalies and critical nodes. The technique was validated with a industry dataset from the banking sector, comprising 562 activity flow plans. The study monitored causal structures during the planning and execution stages, and allowed to identify the main factor behind a major deviation from planned values. This work contributes to business process monitoring by introducing a causal approach that enhances both the interpretability and explainability of anomalies. The technique allows to understand which specific variables have caused an atypical scenario, providing a clear view of the causal relationships within processes and ensuring greater accuracy in decision-making. This causal analysis employs cross-sectional data, avoiding the need to average multiple time instances and reducing potential biases, and unlike time series methods, it preserves the relationships among variables. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1099-4300 |
| Relation: | https://www.mdpi.com/1099-4300/26/10/867; https://doaj.org/toc/1099-4300 |
| DOI: | 10.3390/e26100867 |
| URL الوصول: | https://doaj.org/article/727d16dc1c364d0695e2eee36a3e62ee |
| رقم الانضمام: | edsdoj.727d16dc1c364d0695e2eee36a3e62ee |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 10994300 |
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| DOI: | 10.3390/e26100867 |