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Author: Admin | 2025-04-27
871–880.[66]Alexander Seeliger, Timo Nolle, and Max Mühlhäuser. 2017. Detecting concept drift in processes using graph metrics on process graphs. In Proceedings of the 9th Conference on Subject-Oriented Business Process Management. Association for Computing Machinery, New York, NY, Article 6, 10 pages.[67]Florian Stertz and Stefanie Rinderle-Ma. 2018. Process histories—detecting and representing concept drifts based on event streams. In On the Move to Meaningful Internet Systems—OTM (Lecture Notes in Computer Science). H. Panetto, C. Debruyne, H. Proper, C. Ardagna, D. Roman, and R. Meersman (Eds.), Vol. 11229, Springer, 318–335.[68]Florian Stertz and Stefanie Rinderle-Ma. 2019. Detecting and identifying data drifts in process event streams based on process histories. In Information Systems Engineering in Responsible Information Systems. Vol. 350, Springer, 133–144.[69]Gabriel Marques Tavares, Paolo Ceravolo, Victor G. Turrisi Da Costa, Ernesto Damiani, and Sylvio Barbon Jr. 2019. Overlapping analytic stages in online process mining. In Proceedings of the 2019 IEEE International Conference on Services Computing. IEEE, 167–175.[70]Charles Truong, Laurent Oudre, and Nicolas Vayatis. 2020. Selective review of offline change point detection methods. Signal Processing 167 (2020), 107299. https://www.sciencedirect.com/science/article/abs/pii/S0165168419303494?via%3Dihub.[71]Wil Van der Aalst. 2016. Process Mining: Data Science in Action. Springer, Berlin. 1–467.[72]Wil van der Aalst, Arya Adriansyah, Ana Karla Alves de Medeiros, Franco Arcieri, Thomas Baier, Tobias Blickle, Jagadeesh Chandra Bose, Peter van den Brand, Ronald Brandtjen, Joos Buijs, Andrea Burattin, Josep Carmona, Malu Castellanos, Jan Claes, Jonathan Cook, Nicola Costantini, Francisco Curbera, Ernesto Damiani, Massimiliano de Leoni, Pavlos Delias, Boudewijn F. van Dongen, Marlon Dumas, Schahram Dustdar, Dirk Fahland, Diogo R. Ferreira, Walid Gaaloul,
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