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Business process remaining time prediction using explainable reachability graph from gated RNNs
16 references·0 citing·38 second hop
Business process remaining time prediction using explainable reachability graph from gated RNNs (2022)Business process remaining time predictio…Deep Learning for Predictive Business Process Monitoring: Review and Benchmark (2023)Deep Learning for Predictive …Predictive Process Monitoring (2022)Predictive Process MonitoringLearning uncertainty with artificial neural networks for predictive process monitoring (2022)Learning uncertainty with art…Building interpretable models for business process prediction using shared and specialised attention mechanisms (2022)Building interpretable models…Adaptive learning for reliability analysis using Support Vector Machines (2022)Adaptive learning for reliabi…Exploring Interpretable Predictive Models for Business Processes (2020)Exploring Interpretable Predi…Explainable predictive business process monitoring using gated graph neural networks (2020)Explainable predictive busine…Explainability in Predictive Process Monitoring: When Understanding Helps Improving (2020)Explainability in Predictive …An extended model for remaining time prediction in manufacturing systems using process mining (2020)An extended model for remaini…Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring (2019)Survey and Cross-benchmark Co…Predictive Business Process Monitoring – Remaining Time Prediction using Deep Neural Network with Entity Embedding (2019)Predictive Business Process M…Outcome-Oriented Predictive Process Monitoring (2019)Outcome-Oriented Predictive P…Learning Accurate LSTM Models of Business Processes (2019)Learning Accurate LSTM Models…Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review (2019)Applications of Bayesian netw…Predictive Business Process Monitoring with LSTM Neural Networks (2017)Predictive Business Process M…LSTM networks for data-aware remaining time prediction of business process instances (2017)LSTM networks for data-aware …Reengineering the corporation: A manifesto for business revolution (1993)Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications (2000)Process Mining (2011)Explainable Predictive Process Monitoring (2020)Procedures for Performing Systematic Reviews (2004)Another look at measures of forecast accuracy (2006)Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction (2020)A systematic literature review on state-of-the-art deep learning methods for process prediction (2021)Business process variant analysis: Survey and classification (2021)Handbook of human reliability analysis with emphasis on nuclear power plant applications (1985)Improving the analysis of dependable systems by mapping fault trees into Bayesian networks (2001)Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches (2011)Uncertainty handling in fault tree based risk assessment: State of the art and future perspectives (2019)Evolution of Safety and Security Risk Assessment methodologies towards the use of Bayesian Networks in Process Industries (2021)Machine learning-based methods in structural reliability analysis: A review (2021)Process Mining (2016)Fundamentals of Business Process Management (2018)The biggest business process management problems to solve before we die (2023)Towards comprehensive support for organizational mining (2008)Predicting process behaviour using deep learning (2017)Design Science in Information Systems Research1 (2004)Positioning and Presenting Design Science Research for Maximum Impact1 (2013)A Multi-View Deep Learning Approach for Predictive Business Process Monitoring (2021)Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning (2021)A Systematic Review of Anomaly Detection for Business Process Event Logs (2023)Introduction to time series analysis and forecasting (2008)Process Mining: Discovery, Conformance and Enhancement of Business Processes (2011)Replaying history on process models for conformance checking and performance analysis (2012)Performance analysis and time prediction in manufacturing systems (2020)Process Mining for Dynamic Modeling of Smart Manufacturing Systems: Data Requirements (2022)The ProM Framework: A New Era in Process Mining Tool Support (2005)Time prediction based on process mining (2010)Foundations of Process Event Data (2022)A review of AI and machine learning contribution in business process management (process enhancement and process improvement approaches) (2024)Predictive process monitoring: concepts, challenges, and future research directions (2024)Measurement Reliability and Validity (2000)Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring (2023)Machine learning in business process management: A systematic literature review (2024)
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References

  • 2017Predictive Business Process Monitoring with LSTM Neural Networks554 cited
  • 2019Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review391 cited
  • 2019Outcome-Oriented Predictive Process Monitoring352 cited
  • 2019Learning Accurate LSTM Models of Business Processes241 cited
  • 2019Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring207 cited
  • 2022Predictive Process Monitoring129 cited
  • 2017LSTM networks for data-aware remaining time prediction of business process instances118 cited
  • 2023Deep Learning for Predictive Business Process Monitoring: Review and Benchmark104 cited
  • 2020Explainable predictive business process monitoring using gated graph neural networks84 cited
  • 2020An extended model for remaining time prediction in manufacturing systems using process mining64 cited
  • 2020Explainability in Predictive Process Monitoring: When Understanding Helps Improving60 cited
  • 2022Adaptive learning for reliability analysis using Support Vector Machines57 cited
  • 2022Building interpretable models for business process prediction using shared and specialised attention mechanisms47 cited
  • 2020Exploring Interpretable Predictive Models for Business Processes41 cited

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  • 2022Learning uncertainty with artificial neural networks for predictive process monitoring39 cited
  • 2019Predictive Business Process Monitoring – Remaining Time Prediction using Deep Neural Network with Entity Embedding30 cited
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