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  • Mastering Organizational Dynamics Using Process Mining

    Mastering Organizational Dynamics Using Process Mining by Yang, Roy Jing;

    Series: Lecture Notes in Business Information Processing; 552;

      • GET 20% OFF

      • The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
      • Publisher's listprice EUR 62.05
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        25 735 Ft (24 509 Ft + 5% VAT)
      • Discount 20% (cc. 5 147 Ft off)
      • Discounted price 20 588 Ft (19 607 Ft + 5% VAT)

    25 735 Ft

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    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Long description:

    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.

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    Table of Contents:

    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.

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