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εm - Process Analysis using a Meta Modeling Tool.

, , , , and . ER Forum/Demos, volume 1979 of CEUR Workshop Proceedings, page 334-337. CEUR-WS.org, (2017)

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Cause vs. Effect in Context-Sensitive Prediction of Business Process Instances., , , , , and . CoRR, (2020)εm - Process Analysis using a Meta Modeling Tool., , , , and . ER Forum/Demos, volume 1979 of CEUR Workshop Proceedings, page 334-337. CEUR-WS.org, (2017)Prediction of Business Process Instances with Dynamic Bayesian Networks., , , , , and . SIMPDA, volume 2270 of CEUR Workshop Proceedings, page 50-54. CEUR-WS.org, (2018)XNAP: Making LSTM-Based Next Activity Predictions Explainable by Using LRP., , , , , and . Business Process Management Workshops, volume 397 of Lecture Notes in Business Information Processing, page 129-141. Springer, (2020)Cause vs. effect in context-sensitive prediction of business process instances., , , , , and . Inf. Syst., (2021)Effect of Transparency and Trust on Acceptance of Automatic Online Comment Moderation Systems., , and . CBI (1), page 429-435. IEEE, (2019)Building an Integrated Comment Moderation System - Towards a Semi-automatic Moderation Tool., , , , , and . HCI (15), volume 12195 of Lecture Notes in Computer Science, page 71-86. Springer, (2020)A Framework of Business Process Monitoring and Prediction Techniques., , and . Wirtschaftsinformatik, AISeL, (2021)An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs., , , , , , , and . CoRR, (2020)Bringing Light Into the Darkness - A Systematic Literature Review on Explainable Predictive Business Process Monitoring Techniques., , , , , and . ECIS, (2021)