Process Mining Workshops ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23-28, 2022, Revised Selected Papers /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Montali, Marco (Editor), Senderovich, Arik (Editor), Weidlich, Matthias (Editor)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer Nature Switzerland : Imprint: Springer, 2023.
• Edition: 1st ed. 2023.
• Series: Lecture Notes in Business Information Processing, 468
• Subjects: Data mining.; Information technology > Management.; Machine learning.; Medical informatics.; Data Mining and Knowledge Discovery.; Business Process Management.; Machine Learning.; Computer Application in Administrative Data Processing.; Health Informatics.
• Online Access: Link to Metadata

This open access book constitutes revised selected papers from the International Workshops held at the 4th International Conference on Process Mining, ICPM 2022, which took place in Bozen-Bolzano, Italy, during October 23-28, 2022. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 42 papers included in this volume were carefully reviewed and selected from 89 submissions. They stem from the following workshops: - 3rd International Workshop on Event Data and Behavioral Analytics (EDBA) - 3rd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) - 3rd International Workshop on Responsible Process Mining (RPM) (previously known as Trust, Privacy and Security Aspects in Process Analytics) - 5th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) - 3rd International Workshop on Streaming Analytics for Process Mining (SA4PM) - 7th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) - 1st International Workshop on Education meets Process Mining (EduPM) - 1st International Workshop on Data Quality and Transformation in Process Mining (DQT-PM).