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Grouping Local Process Models
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2023
Online
DOI: 10.48550/arXiv.2311.03040
DOI: 10.18154/RWTH-2023-11415
URL: https://publications.rwth-aachen.de/record/974540/files/974540.pdf
Einrichtungen
Inhaltliche Beschreibung (Schlagwörter)
Artificial Intelligence (cs.AI) (Genormte SW) ; FOS: Computer and information sciences (Genormte SW) ; I.5.3 (Genormte SW) ; Machine Learning (cs.LG) (Genormte SW) ; Local Process Models (frei) ; Model clustering (frei) ; Model grouping (frei) ; Model similarity (frei) ; Process Mining (frei) ; Process model comparison (frei)
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External link: 
Fulltext by arXiv.org
Dokumenttyp
Preprint
Format
online
Sprache
English
Externe Identnummern
arXiv: arXiv:2311.03040
Interne Identnummern
RWTH-2023-11415
Datensatz-ID: 974540
Beteiligte Länder
Germany
Related:
Contribution to a book/Contribution to a conference proceedings
Grouping Local Process Models
Process Mining Workshops : ICPM 2023 International Workshops, Rome, Italy, October 23-27, 2023, revised selected papers / Johannes De Smedt, Pnina Soffer editors
8. International Workshop on Process Querying, Manipulation, and Intelligence, PQMI 2023, RomeRome, Italy, 23 Oct 2023 - 23 Oct 2023
5. International Conference on Process Mining, ICPM 2023, RomeRome, Italy, 23 Oct 2023 - 27 Oct 2023
Cham, Switzerland : Springer, Lecture notes in business information processing 503, 419-430 (2024) [10.1007/978-3-031-56107-8_32]
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