Incremental Process Discovery
Schuster, Daniel
Sold by Revaluation Books, Exeter, United Kingdom
AbeBooks Seller since January 6, 2003
New - Soft cover
Condition: New
Ships from United Kingdom to U.S.A.
Quantity: 2 available
Add to basketSold by Revaluation Books, Exeter, United Kingdom
AbeBooks Seller since January 6, 2003
Condition: New
Quantity: 2 available
Add to basket383 pages. 9.25x6.10x9.21 inches. In Stock.
Seller Inventory # x-303180564X
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.
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.
"About this title" may belong to another edition of this title.
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