Mastering Organizational Dynamics Using Process Mining

Erscheinungsjahr 2025
ISBN: 978-3-031-93529-9
Verlag: Springer

Buch, Englisch, 107 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 201 g

Reihe: Lecture Notes in Business Information Processing

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.

Zielgruppe

Research

Autoren/Hrsg.

Yang, Roy Jing

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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.

Über Autor(innen)

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.