Unearthing the real process behind the event data : the case for increased process realism / Gert Janssenswillen.

Part I Introduction
1 Process Realism
1.1 Introduction to Process Mining
1.1.1 Business Process Management
1.1.2 The emergence of process mining
1.1.3 Perspectives
1.1.4 Tools
1.1.5 Towards Evidence-based Business Process Management
1.2 The case for Process Realism
1.2.1 Motivation
1.2.2 Research objective
1.3 Methodology and Outline
1.3.1 Process Model Quality
1.3.2 Process Analytics
Part II Process Model Quality
2 Introduction to Conformance Checking
2.1 Introduction to Process Mining
2.1.1 Preliminaries
2.1.2 Process
2.1.3 Event log
2.1.4 Model
2.2 Quality Dimensions
2.2.1 Fitness
2.2.2 Precision
2.2.3 Generalization
2.2.4 Simplicity
2.3 Quality Measures
2.3.1 Fitness
2.3.2 Precision
2.3.3 Generalization
2.4 Conclusion
2.5 Further Reading
3 Calculating the Number of Distinct Paths in a Block-Structured Model
3.1 Introduction
3.2 Formal Algorithm
3.2.1 Assumptions and used notations
3.2.2 Generic approach
3.2.3 Block Functions
3.2.4 Limitations
3.3 Implementation
3.3.1 Preliminaries
3.3.2 Algorithm
3.3.3 Extended Block Functions
3.3.4 Silent transitions and duplicate tasks
3.4 Performance
3.5 Conclusion and future work
3.6 Further Reading
4 Comparative Study of Quality Measures
4.1 Introduction
4.2 Problem Statement
4.3 Methodology
4.3.1 Generate systems
4.3.2 Calculate the number of paths
4.3.3 Simulate logs
4.3.4 Discover models
4.3.5 Measure quality
4.3.6 Statistical Analysis
4.4 Results
4.4.1 Feasibility.-4.4.2 Validity
4.4.3 Sensitivity
4.5 Discussion
4.6 Conclusion
4.7 Further Reading
5 Reassessing the Quality Framework
5.1 Introduction
5.2 Exploratory versus confirmatory process discovery
5.2.1 Problem statement
5.3 Methodology
5.3.1 Generate systems
5.3.2 Simulate logs
5.3.3 Discover models
5.3.4 Measure log-quality
5.3.5 Measure system-quality
5.3.6 Statistical analysis
5.4 Results
5.4.1 Log versus system-perspective
5.4.2 Generalization
5.5 Discussion
5.6 Conclusion
5.7 Further Reading
6 Towards Mature Conformance Checking
6.1 Synthesis
6.1.1 Fitness
6.1.2 Precision
6.1.3 Generalization
6.2 Future research
6.2.1 System-fitness and system-precision
6.2.2 Improving the Experimental Setup
Part III Process Analytics
7 Reproducible Process Analytics
7.1 Introduction
7.2 Problem Statement
7.3 Requirements Definition
7.3.1 Functionality requirements
7.3.2 Design Requirements
7.4 Design and Development of Artefact
7.4.1 Core packages
7.4.2 Supplementary packages
7.5 Demonstration of Artefact
7.5.1 Event data extraction
7.5.2 Data Processing
7.5.3 Mining and Analysis
7.6 Discussion
7.7 Conclusion
7.8 Further Reading
8 Student Trajectories in Higher Education
8.1 Learning analytics and process mining
8.2 Data Understanding
8.3 Followed versus prescribed trajectories
8.3.1 Root causes
8.3.2 Impact
8.4 Failure Patterns
8.4.1 Bags
8.4.2 High-level analysis
8.4.3 Low-level analysis
8.5 Understanding Trajectory Decisions
8.6 Discussion
8.7 Conclusion
8.8 Further Reading
9 Process-Oriented Analytics in Railway Systems
9.1 Introduction
9.2 Problem statement and related work
9.3 Methodology
9.3.1 Rerouting severity
9.3.2 Rerouting diversity
9.3.3 Discovering patterns
9.4 Results
9.4.1 Rerouting severity
9.4.2 Rerouting diversity
5 Discussion
9.6 Conclusions
9.7 Further Reading
Part IV Conclusions
10 Conclusions and Recommendations for Future Research
10.1 Process Model Quality
10.1.1 Lessons Learned
10.1.2 Recommendations for Future Research
10.2 Process Analytics
10.2.1 Lessons Learned
10.2.2 Recommendations for Future Research
Afterword
A Additional Figures and Tables Chapter 4
B Function Index bupaR packages
B.1 bupaR
B.2 edeaR
B.3 evendataR
B.4 xesreadR
B.5 processmapR
B.6 processmonitR
B.7 petrinetR
B.8 ptR
B.9 discoveR
C Scripts Chapter 8
D Scripts Chapter 9
References.