Incremental Process Discovery / by Daniel Schuster.

Format:

Book

Author/Creator:

Schuster, Daniel, author.

Series:

Lecture Notes in Business Information Processing, 1865-1356 ; 540

Language:

English

Subjects (All):

Information technology--Management.

Information technology.

Business Process Management.

Computer Application in Administrative Data Processing.

Local Subjects:

Business Process Management.

Computer Application in Administrative Data Processing.

Physical Description:

1 online resource (0 pages)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

This book constitutes the revised version of the award-winning PhD dissertation written by the author at RWTH Aachen, Germany. It presents a framework for incremental process discovery that allows users to learn and refine process models from event data iteratively. Next to process discovery and event data handling, it also contributes to conformance checking, a further fundamental process mining task. Eventually, it presents Cortado, an open-source process mining software tool that implements the algorithms and techniques proposed in an integrated and comprehensive fashion. This part also includes a case study applying Cortado and, therefore, the various contributions of this thesis in a real-life scenario. In 2024, this PhD dissertation won the “Best Process Mining PhD Dissertation Award” by the IEEE Task Force for Process Mining, granted to outstanding PhD theses in this field.

Contents:

Opening and fundamentals

incremental process discovery

facilitating interaction with event data

realization and application

closure.

Notes:

Includes bibliographical references.

ISBN:

9783031805653

3031805658

OCLC:

1514626196