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Towards Reliable Predictive Process Monitoring · Study

WorkTowards Reliable Predictive Process Monitoring

Christopher Klinkmüller2018

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Workbronze2018

Towards Reliable Predictive Process Monitoring

Christopher Klinkmüller · Nick van Beest · Ingo M. Weber

14 citations·0/0 files stored·Lecture notes in business information processing

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Business Process Modeling and Analysis Software Testing and Debugging Techniques Data Mining Algorithms and Applications

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References

2011Process Mining 2007Conformance checking of processes based on monitoring real behavior 2017Predictive Business Process Monitoring with LSTM Neural Networks 2010Time prediction based on process mining 2017Predicting process behaviour using deep learning 2014Predictive Monitoring of Business Processes 2015Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes 2016Clustering-Based Predictive Process Monitoring

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2020An Empirical Investigation of Different Classifiers, Encoding, and Ensemble Schemes for Next Event Prediction Using Business Process Event Logs 2018Probability Based Heuristic for Predictive Business Process Monitoring

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2022How do I update my model? On the resilience of Predictive Process Monitoring models to changearticle · 10 shared references 2023Automatically reconciling the trade-off between prediction accuracy and earliness in prescriptive business process monitoringarticle · 10 shared references 2019From knowledge-driven to data-driven inter-case feature encoding in predictive process monitoringarticle · 9 shared references 2019Proactive Process Adaptation Using Deep Learning Ensemblesother · 9 shared references 2018Genetic algorithms for hyperparameter optimization in predictive business process monitoringarticle · 8 shared references 2018Temporal stability in predictive process monitoringarticle · 8 shared references

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Year2018

Typeother

Published2018-01-01

VenueLecture notes in business information processing

ISSN-L1865-1348

Languageen

Citations14

Files0 public / 0

ShelfBusiness Process Modeling and Analysis

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doi10.1007/978-3-319-92901-9_15

openalex_workW2805835245

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