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Incremental Process Discovery
Details
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.
Won the 2024 Best Process Mining PhD Dissertation Award by the IEEE Task Force for Process Mining Presents a framework for incremental process discovery enabling users to learn & refine process models from event data Introduces Cortado, an open-source process mining tool that implements the presented algorithms and techniques
Inhalt
Opening and fundamentals.- incremental process discovery.- facilitating interaction with event data.- realization and application.- closure.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09783031805646
- Genre Information Technology
- Lesemotiv Verstehen
- Anzahl Seiten 367
- Größe H21mm x B155mm x T235mm
- Jahr 2025
- EAN 9783031805646
- Format Kartonierter Einband
- ISBN 978-3-031-80564-6
- Titel Incremental Process Discovery
- Autor Daniel Schuster
- Untertitel Lecture Notes in Business Information Processing 540
- Gewicht 581g
- Herausgeber Springer
- Sprache Englisch