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Explainable predictive business process monitoring using gated graph neural networks.

, , , and . J. Decis. Syst., 29 (Supplement): 312-327 (2020)

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Closing the Gap between Smart Manufacturing Applications and Data Management., , , and . Wirtschaftsinformatik (Community Tracks), page 120-136. GITO Verlag, (2020)Conformance checking: A state-of-the-art literature review., , , and . CoRR, (2020)Cause vs. effect in context-sensitive prediction of business process instances., , , , , and . Inf. Syst., (2021)Seven Paradoxes of Business Process Management in a Hyper-Connected World., , , , , , , , , and 14 other author(s). Bus. Inf. Syst. Eng., 63 (2): 145-156 (2021)Explainable predictive business process monitoring using gated graph neural networks., , , and . J. Decis. Syst., 29 (Supplement): 312-327 (2020)From predictive to prescriptive process monitoring: Recommending the next best actions instead of calculating the next most likely events., , , , and . Wirtschaftsinformatik (Zentrale Tracks), page 364-368. GITO Verlag, (2020)Conformance checking: a state-of-the-art literature review., , , and . S-BPM ONE, page 4:1-4:10. ACM, (2019)The MobIS-Challenge 2019 A Report on the WI-2019-Workshop on Model-Based Compliance in Information Systems., , , , , , , , , and 1 other author(s). Enterp. Model. Inf. Syst. Archit. Int. J. Concept. Model., (2020)Cause vs. Effect in Context-Sensitive Prediction of Business Process Instances., , , , , and . CoRR, (2020)Prediction of Business Process Instances with Dynamic Bayesian Networks., , , , , and . SIMPDA, volume 2270 of CEUR Workshop Proceedings, page 50-54. CEUR-WS.org, (2018)