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BINet: Multi-perspective Business Process Anomaly Classification., , , and . CoRR, (2019)DeepAlign: Alignment-based Process Anomaly Correction using Recurrent Neural Networks., , , and . CoRR, (2019)Intelligent Computer-assisted Process Mining. TU Darmstadt, Germany, (2020)base-search.net (ftulbdarmstadt:oai:tuprints.ulb.tu-darmstadt.de:11915).Case2vec: Advances in Representation Learning for Business Processes., , , and . ICPM Workshops, volume 406 of Lecture Notes in Business Information Processing, page 162-174. Springer, (2020)Discovering Process-Based Drivers for Case-Level Outcome Explanation., , , , and . ICPM Workshops, volume 503 of Lecture Notes in Business Information Processing, page 165-178. Springer, (2023)Inferring a Multi-perspective Likelihood Graph from Black-Box Next Event Predictors., , , and . CAiSE, volume 13295 of Lecture Notes in Computer Science, page 19-35. Springer, (2022)Learning of Process Representations Using Recurrent Neural Networks., , , and . CAiSE, volume 12751 of Lecture Notes in Computer Science, page 109-124. Springer, (2021)ProcessExplorer: Intelligent Process Mining Guidance., , , and . BPM, volume 11675 of Lecture Notes in Computer Science, page 216-231. Springer, (2019)BINet: Multi-perspective business process anomaly classification., , , and . Inf. Syst., (2022)A Method for Debugging Process Discovery Pipelines to Analyze the Consistency of Model Properties., , , , and . BPM, volume 12875 of Lecture Notes in Computer Science, page 65-84. Springer, (2021)