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Predicting process performance: A white‐box approach based on process models
16 references·3 citing·34 second hop
Predicting process performance: A white‐box approach based on process models (2019)Predicting process performance: A white‐b…Fundamentals of Business Process Management (2018)Fundamentals of Business Proc…Predictive Monitoring of Business Processes: A Survey (2017)Predictive Monitoring of Busi…Predictive Business Process Monitoring with LSTM Neural Networks (2017)Predictive Business Process M…Comprehensible Predictive Models for Business Processes1 (2016)Comprehensible Predictive Mod…Queue mining for delay prediction in multi-class service processes (2015)Queue mining for delay predic…Prediction of business process durations using non-Markovian stochastic Petri nets (2015)Prediction of business proces…Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes (2015)Complex Symbolic Sequence Enc…A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs (2015)A general process mining fram…Predictive Monitoring of Business Processes (2014)Predictive Monitoring of Busi…A recommendation system for predicting risks across multiple business process instances (2014)A recommendation system for p…Prediction of Remaining Service Execution Time Using Stochastic Petri Nets with Arbitrary Firing Delays (2013)Prediction of Remaining Servi…Time prediction based on process mining (2010)Time prediction based on proc…Cycle Time Prediction: When Will This Case Finally Be Finished? (2008)Cycle Time Prediction: When W…Another look at measures of forecast accuracy (2006)Another look at measures of f…Quality of service for workflows and web service processes (2004)Quality of service for workfl…Operations Research Calculations Handbook (2001)Operations Research Calculati…Digital Twins of Business Processes: A Research Manifesto (2024)Digital Twins of Business Pro…XNAP: Making LSTM-Based Next Activity Predictions Explainable by Using LRP (2020)XNAP: Making LSTM-Based Next …Cause vs. effect in context-sensitive prediction of business process instances (2020)Cause vs. effect in context-s…A Declarative Approach for Flexible Business Processes Management (2006)DECLARE: Full Support for Loosely-Structured Processes (2007)Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring (2019)Workflow mining: A survey of issues and approaches (2003)Workflow mining: discovering process models from event logs (2004)Conformance checking of processes based on monitoring real behavior (2007)Process Mining (2012)Competing against time: how time-based competition is reshaping global markets (1990)Adeptflex—Supporting Dynamic Changes of Workflows Without Losing Control (1998)Business process management (BPM) standards: a survey (2009)e-GovQual: A multiple-item scale for assessing e-government service quality (2011)Fuzzy VIKOR method: A case study of the hospital service evaluation in Taiwan (2014)Business Intelligence and Analytics: From Big Data to Big Impact (2012)Positioning and Presenting Design Science Research for Maximum Impact1 (2013)Big Data: A Survey (2014)Smart Products: Conceptual Review, Synthesis, and Research Directions* (2020)Business Process Intelligence (2003)Process Mining (2016)A systematic literature review on state-of-the-art deep learning methods for process prediction (2021)Replaying history on process models for conformance checking and performance analysis (2012)Discovering Block-Structured Process Models from Event Logs - A Constructive Approach (2013)Fundamentals of Business Process Management (2013)Intra and Inter-case Features in Predictive Process Monitoring: A Tale of Two Dimensions (2017)Predictive Process Monitoring (2022)The Six Core Elements of Business Process Management (2010)Outcome-Oriented Predictive Process Monitoring (2019)Strategy archetypes for digital transformation: Defining meta objectives using business process management (2020)Automatic Time Series Forecasting: The forecast Package for R (2008)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)Clustering-Based Predictive Process Monitoring (2016)Predictive Process Monitoring Methods: Which One Suits Me Best? (2018)Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction (2020)Learning Accurate LSTM Models of Business Processes (2019)
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References

  • 2006Another look at measures of forecast accuracy5,736 cited
  • 2018Fundamentals of Business Process Management1,831 cited
  • 2004Quality of service for workflows and web service processes866 cited
  • 2017Predictive Business Process Monitoring with LSTM Neural Networks554 cited
  • 2010Time prediction based on process mining507 cited
  • 2014Predictive Monitoring of Business Processes305 cited
  • 2017Predictive Monitoring of Business Processes: A Survey279 cited
  • 2015A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs251 cited
  • 2015Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes191 cited
  • 2016Comprehensible Predictive Models for Business Processes1188 cited
  • 2014A recommendation system for predicting risks across multiple business process instances136 cited
  • 2008Cycle Time Prediction: When Will This Case Finally Be Finished?132 cited
  • 2013Prediction of Remaining Service Execution Time Using Stochastic Petri Nets with Arbitrary Firing Delays129 cited
  • 2015Queue mining for delay prediction in multi-class service processes123 cited
  • 2001Operations Research Calculations Handbook

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123 cited
  • 2015Prediction of business process durations using non-Markovian stochastic Petri nets121 cited
  • Cited by

    • 2024Digital Twins of Business Processes: A Research Manifesto32 cited
    • 2020XNAP: Making LSTM-Based Next Activity Predictions Explainable by Using LRP30 cited
    • 2020Cause vs. effect in context-sensitive prediction of business process instances28 cited