Incremental Process Discovery / by Daniel Schuster

Von:

Schuster, Daniel [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Lecture Notes in Business Information Processing ; 540Verlag: Cham : Springer Nature Switzerland, 2025Verlag: Cham : Imprint: Springer, 2025Auflage: 1st ed. 2025Beschreibung: 1 Online-Ressource(XVI, 367 p. 156 illus., 76 illus. in color.)ISBN:

9783031805653

Schlagwörter:

Information technology

Andere physische Formen: 9783031805646 | 9783031805660 | Erscheint auch als: 9783031805646 Druck-Ausgabe | Erscheint auch als: 9783031805660 Druck-AusgabeDDC-Klassifikation:

658.4038 23

DOI: DOI: 10.1007/978-3-031-80565-3Online-Ressourcen:

Zugang im Netz des KIT

Zusammenfassung: Opening and fundamentals -- incremental process discovery -- facilitating interaction with event data -- realization and application -- closure.Zusammenfassung: 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.PPN: PPN: 1922928607Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SCS | ZDB-2-SXCS | ZDB-2-LNB

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