This book is a revised version of the PhD dissertation written by the author at Queensland University of Technology.
It presents research in the field of process mining, with a focus of developing data-driven methods to discover insights about human resources and their groups in an organizational business process context. It provides an overview on mining organizational models from event logs and introduces a set of novel ideas, framework, and approaches proposed to enhance the state-of-the-art. The book is suitable for researchers and practitioners in the fields of business process management and process mining.
In 2024, the PhD dissertation won the BPM Dissertation Award, granted to outstanding PhD theses in the field of Business Process Management.
1 Introduction.- 1.1 Process Mining.- 1.2 Mining Organizational Models
from Event Logs.- 1.3 Outlook.- 2 Framework for Organizational Model Mining.-
2.1 Preliminaries.- 2.2 Execution Context.- 2.3 Organizational Model.- 2.4
Discovering Organizational Models.- 2.5 Evaluating Organizational Models.-
2.5.1 Fitness.- 2.5.2 Precision.- 2.6 Analyzing Organizational Models.- 2.7
Discussion.- 3 Learning Execution Contexts.- 3.1 Preliminaries.- 3.2 Problem
Modeling.- 3.2.1 Categorization Rules.- 3.2.2 Quality Measures for Execution
Contexts.- 3.2.3 Problem Statement.- 3.3 Problem Solution.- 3.3.1 Deriving
Attribute Specification.- 3.3.2 Inducing Rules via Simulated Annealing.- 3.4
Evaluation.- 3.4.1 Event Log Datasets.- 3.4.2 Experiment Setup.- 3.4.3
Evaluation against the Baselines.- 3.4.4 Evaluation between tree-based and
SA-based.- 3.4.5 Summary.- 3.5 Discussion.- 4 Discovering Organizational
Models.- 4.1 A Three-Phased Discovery Approach.- 4.1.1 Determining Execution
Contexts.- 4.1.2 Discovering Resource Grouping.- 4.1.3 Profiling Resource
Groups.- 4.2 Implementation.- 4.3 Evaluation.- 4.3.1 Experiment Setup.- 4.3.2
Model Evaluation and Comparison.- 4.3.3 Model Diagnosis.- 4.3.4 Summary.- 4.4
Discussion.- 5 Applying Organizational Models to Workforce Analytics.- 5.1
Preliminaries.- 5.2 Resource Group Work Profiles.- 5.2.1 Work Profile
Indicators.- 5.2.2 Extracting and Analyzing Work Profiles.- 5.3 Case Study:
One Process, Five Municipalities.- 5.3.1 Group-level Analysis.- 5.3.2
Within-Group Analysis.- 5.3.3 Summary.- 5.4 Discussion.- 6 Epilogue.- 6.1
Conclusions.- 6.2 Future Work.- References.
Jing Roy Yang is a postdoctoral research fellow at Queensland University of Technology (QUT), Australia. His research focuses on discovering knowledge from process execution data to support improved decision-making, especially knowledge about (human) resources, and process automation.
