{"product_id":"9783031935299","title":"Mastering Organizational Dynamics Using Process Mining (Lecture Notes in Business Information Processing 552) (2025. xii, 109 S. XII, 109 p. 29 illus. 235 mm)","description":"\u003cp\u003eThis book is a revised version of the PhD dissertation written by the author at Queensland University of Technology. \u003c\/p\u003e\u003cp\u003eIt 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.\u003c\/p\u003e\u003cp\u003eIn 2024, the PhD dissertation won the BPM Dissertation Award , granted to outstanding PhD theses in the field of Business Process Management.\u003c\/p\u003e \u003cp\u003e1 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.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eJing Roy Yang \u003c\/strong\u003eis 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.\u003c\/p\u003e","brand":"SPRINGER, BERLIN; SPRINGER NATURE SWITZERLAND; SPRING","offers":[{"title":"Default Title","offer_id":48865626980587,"sku":"00000_00000_00000_00000","price":119.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0758\/4484\/5803\/files\/9783031935299-1.jpg?v=1781717936","url":"https:\/\/kinokuniya.com.sg\/products\/9783031935299","provider":"Books Kinokuniya Singapore","version":"1.0","type":"link"}