Intro
Preface
Organization
Keynote Abstracts
What Have the Romans Ever Done for Us? The Ancient Antecedents of Business Process Management
Artificial Intelligence-based Declarative Process Synthesis for BPM
Contents
Keynote Paper
Process Automation and Process Mining in Manufacturing
1 Introduction
2 Automating Legacy vs. Automating Greenfield Scenarios in Manufacturing
2.1 Automating Legacy Scenarios
2.2 Automating Greenfield Scenarios
3 The Human Aspect in Process Automation
4 Process Mining and Automation: Are They Twins?

5 Discussion and Outlook
References
Tutorials
Cognitive Effectiveness of Representations for Process Mining
1 Introduction
2 Visual Representations for Process Mining
3 Cognitive Effectiveness of Process Mining Outputs
4 Evaluating Process Mining from a Cognitive Angle
5 Conclusion
References
RuM: Declarative Process Mining, Distilled
1 Introduction
2 Declarative Process Mining with RuM
3 Considerations About Declarative Process Mining
4 Research Opportunities
References
Applications of Automated Planning for Business Process Management

1 Why Automated Planning for Business Processes?
2 Automated Planning for BPM
2.1 Automated Generation of Process Models
2.2 Trace Alignment
2.3 Process Adaptation
2.4 Interpretability and Authoring Tools
3 Conclusions
References
Artifact-Driven Process Monitoring: A Viable Solution to Continuously and Autonomously Monitor Business Processes
1 Introduction to Process Monitoring
1.1 Challenges in Process Monitoring
2 Artifact-Driven Monitoring in a Nutshell
2.1 E-GSM Modeling Language
2.2 From BPMN to E-GSM

2.3 SMARTifact: An Artifact-Driven Monitoring Platform
References
Process Discovery
Weighing the Pros and Cons: Process Discovery with Negative Examples
1 Introduction
2 Process Notations and Unary Discovery
3 Process Discovery as Binary Classification
4 Rejection Miners
5 Cases with Negative Examples
5.1 DCR Solutions: Test-Driven Modelling
5.2 Dreyer Foundation: Process Engineering
6 Experimental Results
6.1 Results
7 Conclusion
References
A Method for Debugging Process Discovery Pipelines to Analyze the Consistency of Model Properties

1 Introduction
2 Basic Terminology and Problem Illustration
3 Debugging of Process Discovery Pipelines
3.1 Sampling the Pipeline
3.2 Measuring the Property Consistency for a Single Execution
3.3 Analyzing the Property Consistency for the Pipeline
4 Experiment
4.1 Experimental Design
4.2 Results
5 Related Work
6 Conclusion
References
Extracting Decision Models from Textual Descriptions of Processes
1 Introduction
2 Related Work
3 Preliminaries
3.1 Decision Model and Notation (DMN)
3.2 Natural Language Processing and Annotation
3.3 TRegex