Incremental Process Discovery (eBook, PDF)
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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 incl...
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.
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.
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