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To efficiently analyze time series by process mining and to convey confidence into the analysis result, requires bridging challenges. The purpose of this article is to discuss these challenges and to present initial solutions.<\/jats:p>","DOI":"10.1007\/s00287-022-01470-3","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T09:05:41Z","timestamp":1658394341000},"page":"223-228","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Confidence-driven communication of process mining on time series"],"prefix":"10.1007","volume":"45","author":[{"given":"Agnes","family":"Koschmider","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natascha","family":"Oppelt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marie","family":"Hundsd\u00f6rfer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,21]]},"reference":[{"key":"1470_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gsf.2020.04.011","author":"X Wei","year":"2020","unstructured":"Wei\u00a0X, Yang\u00a0H\u2011Q, Zhang\u00a0L, Yao\u00a0Y\u2011P (2020) Machine learning for pore-water pressure time-series prediction: application of recurrent neural networks. 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