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Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>\n            Changes, planned or unexpected, are common during the execution of real-life processes. Detecting these changes is a must for optimizing the performance of organizations running such processes. Most of the algorithms present in the\n            <jats:italic>state-of-the-art<\/jats:italic>\n            focus on the detection of sudden changes, leaving aside other types of changes. In this article, we will focus on the automatic detection of gradual drifts, a special type of change, in which the cases of two models overlap during a period of time. The proposed algorithm relies on conformance checking metrics to carry out the automatic detection of the changes, performing also a fully automatic classification of these changes into sudden or gradual. The approach has been validated with a synthetic dataset consisting of 120 logs with different distributions of changes, getting better results in terms of detection and classification accuracy, delay, and change region overlapping than the main\n            <jats:italic>state-of-the-art<\/jats:italic>\n            algorithms.\n          <\/jats:p>","DOI":"10.1145\/3716169","type":"journal-article","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T13:22:01Z","timestamp":1738675321000},"page":"1-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Gradual Drift Detection in Process Models Using Conformance Metrics"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4149-919X","authenticated-orcid":false,"given":"V\u00edctor","family":"Gallego-Fontenla","sequence":"first","affiliation":[{"name":"Centro Singular de Investigaci\u00f3n en Tecnolox\u00edas Intelixentes, 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