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Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times
16 references·0 citing·38 second hop
Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times (2018)Looking into the TESSERACT: Time-drifts i…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…Process Mining: Data Science in Action (2016)Process Mining: Data Science …A survey of methods for time series change point detection (2016)A survey of methods for time …Queue mining for delay prediction in multi-class service processes (2015)Queue mining for delay predic…BPI Challenge 2015 (2015)BPI Challenge 2015Queue Mining – Predicting Delays in Service Processes (2014)Queue Mining – Predicting Del…Data-aware remaining time prediction of business process instances (2014)Data-aware remaining time pre…Time Constraints in Workflow Systems (2013)Time Constraints in Workflow …Prediction of Remaining Service Execution Time Using Stochastic Petri Nets with Arbitrary Firing Delays (2013)Prediction of Remaining Servi…Dealing With Concept Drifts in Process Mining (2013)Dealing With Concept Drifts i…Handling Concept Drift in Process Mining (2011)Handling Concept Drift in Pro…Time prediction based on process mining (2010)Time prediction based on proc…Beyond Process Mining: From the Past to Present and Future (2010)Beyond Process Mining: From t…Supporting Flexible Processes through Recommendations Based on History (2008)Supporting Flexible Processes…Cycle Time Prediction: When Will This Case Finally Be Finished? (2008)Cycle Time Prediction: When W…YAWL: yet another workflow language (2004)Process Flexibility: A Survey of Contemporary Approaches (2008)Wanna improve process mining results? (2013)Single-Entry Single-Exit decomposed conformance checking (2014)CONTINUOUS INSPECTION SCHEMES (1954)Process Mining (2011)Detecting sudden and gradual drifts in business processes from execution traces (2017)Adoption, use and management of process mining in practice (2020)A Survey on Concept Drift in Process Mining (2021)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)Predictive Monitoring of Business Processes (2014)Adeptflex—Supporting Dynamic Changes of Workflows Without Losing Control (1998)The ProM Framework: A New Era in Process Mining Tool Support (2005)Beyond rigidity – dynamic process lifecycle support (2009)A recommendation system for predicting risks across multiple business process instances (2014)Genetic Process Mining (2005)Business process mining: An industrial application (2006)Genetic process mining: an experimental evaluation (2007)Using Convolutional Neural Networks for Predictive Process Analytics (2019)Business Process Intelligence (2003)Process Mining (2016)Predictive Process Monitoring Methods: Which One Suits Me Best? (2018)A systematic literature review on state-of-the-art deep learning methods for process prediction (2021)Performance modeling and analysis of workflow (2004)Deadline-based escalation in process-aware information systems (2007)Elastic Business Process Management: State of the art and open challenges for BPM in the cloud (2014)Selective review of offline change point detection methods (2019)Sequential (Quickest) Change Detection: Classical Results and New Directions (2021)Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle (2021)Process mining techniques and applications – A systematic mapping study (2019)Examining the interplay between big data analytics and contextual factors in driving process innovation capabilities (2020)Process mining for healthcare: Characteristics and challenges (2022)Learning Accurate LSTM Models of Business Processes (2019)Outcome-Oriented Predictive Process Monitoring (2019)Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring (2019)
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References

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  • 2008Cycle Time Prediction: When Will This Case Finally Be Finished?132 cited
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  • 2014Queue Mining – Predicting Delays in Service Processes92 cited
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BPI Challenge 2015
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