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Explainable concept drift in process mining., , , и . Inf. Syst., (марта 2023)Discovering high-quality process models despite data scarcity., , , , , , , , и . ER (Companion), том 3618 из CEUR Workshop Proceedings, CEUR-WS.org, (2023)OPerA: Object-Centric Performance Analysis., , и . ER, том 13607 из Lecture Notes in Computer Science, стр. 281-292. Springer, (2022)Addressing Convergence, Divergence, and Deficiency Issues., и . Business Process Management Workshops, том 492 из Lecture Notes in Business Information Processing, стр. 496-507. Springer, (2023)A Framework for Explainable Concept Drift Detection in Process Mining., , , , , и . BPM, том 12875 из Lecture Notes in Computer Science, стр. 400-416. Springer, (2021)ocpa: A Python library for object-centric process analysis., , и . Softw. Impacts, (декабря 2022)Preserving complex object-centric graph structures to improve machine learning tasks in process mining., , и . Eng. Appl. Artif. Intell., (2023)Precision and Fitness in Object-Centric Process Mining., и . ICPM, стр. 128-135. IEEE, (2021)Defining Cases and Variants for Object-Centric Event Data., , , , и . ICPM, стр. 128-135. IEEE, (2022)A Framework for Extracting and Encoding Features from Object-Centric Event Data., , , , и . ICSOC, том 13740 из Lecture Notes in Computer Science, стр. 36-53. Springer, (2022)