Process Mining Workshops : : ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 – November 4, 2021, Revised Selected Papers / / edited by Jorge Munoz-Gama, Xixi Lu.

• Superior document: Lecture Notes in Business Information Processing, 433
• Author: International Conference on Process Mining.
• TeilnehmendeR: Munoz-Gama, Jorge.; Lu, Xixi.; International Conference on Process Mining.
• Place / Publishing House: Cham : : Springer International Publishing :, Imprint: Springer,, 2022.
• Year of Publication: 2022
• Edition: 1st ed. 2022.
• Language: English
• Series: Lecture Notes in Business Information Processing, 433
• Physical Description: 1 online resource (xiv, 410 pages) :; illustrations (chiefly color).
• Notes: Includes author index.

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. 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 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included.

Table of Contents

Rethinking the Input for Process Mining: Insights from the XES Survey and Workshop -- Probability Estimation of Uncertain Process Trace Realizations -- Visualizing Trace Variants From Partially Ordered Event Data -- Analyzing Multi-level BOM-structured Event Data -- Linac: A Smart Environment Simulator of Human Activities -- Root Cause Analysis in Process Mining with Probabilistic Temporal Logic -- xPM: A Framework for Process Mining with Exogenous Data -- A Bridging Model for Process Mining and IoT -- Exploiting Instance Graphs and Graph Neural Networks for next activity prediction -- Can deep neural networks learn process modelstructure? An assessment framework and analysis -- Remaining Time Prediction for Processes with Inter-Case Dynamics -- Event Log Sampling for Predictive Monitoring -- Active Anomaly Detection for Key Item Selection in Process Auditing -- Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach -- Quantifying Explainability in Outcome-Oriented Predictive Process Monitoring -- Online Prediction of Aggregated Retailer Consumer Behaviour -- PErrCas: Process Error Cascade Mining in Trace Streams -- Continuous performance evaluation for business process outcome monitoring -- An Event Data Extraction Approach from SAP ERP for Process Mining -- Towards a Natural Language Conversational Interface for Process Mining -- On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization -- Verifying guideline compliance in clinical treatment using multi-perspective conformance checking: a case study -- Patient Discharge Classification based on the Hospital Treatment Process -- Combining the Clinical and Operational Perspectives in Heterogeneous Treatment E ect Inference in Healthcare Processes -- Interactive Process Mining Applied in a Cardiology Outpatient Department -- Discovering care pathways for multi-morbid patients using event graphs -- Process Mining in Trusted Execution Environments: Towards Hardware Guarantees for Trust-aware Inter-organizational Process Analysis -- Quantifying the Re-identification Risk in Published Process Models -- Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities.