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This can make Predictive Process Monitoring too rigid to deal with the variability of processes working in real environments that continuously evolve and\/or exhibit new variant behaviours over time. As a solution to this problem, we evaluate the use of three different strategies that allow the periodic rediscovery or incremental construction of the predictive model so as to exploit new available data. The evaluation focuses on the performance of the new learned predictive models, in terms of accuracy and time, against the original one, and uses a number of real and synthetic datasets with and without explicit Concept Drift. The results provide an evidence of the potential of incremental learning algorithms for predicting process monitoring in real environments.<\/jats:p>","DOI":"10.1007\/s10115-022-01666-9","type":"journal-article","created":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T15:03:42Z","timestamp":1647875022000},"page":"1385-1416","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["How do I update my model? On the resilience of Predictive Process Monitoring models to change"],"prefix":"10.1007","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7318-6833","authenticated-orcid":false,"given":"Williams","family":"Rizzi","sequence":"first","affiliation":[]},{"given":"Chiara","family":"Di\u00a0Francescomarino","sequence":"additional","affiliation":[]},{"given":"Chiara","family":"Ghidini","sequence":"additional","affiliation":[]},{"given":"Fabrizio Maria","family":"Maggi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,21]]},"reference":[{"key":"1666_CR1","doi-asserted-by":"publisher","unstructured":"3TU Data Center, (2011) BPI Challenge 2011 Event Log. https:\/\/doi.org\/10.4121\/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54","DOI":"10.4121\/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54"},{"issue":"2","key":"1666_CR2","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/s13740-019-00105-3","volume":"8","author":"CO Back","year":"2019","unstructured":"Back CO, Debois S, Slaats T (2019) Entropy as a measure of log variability. 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