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A Robust and Accurate Approach to Detect Process Drifts from Event Streams · Study

WorkA Robust and Accurate Approach to Detect Process Drifts from Event Streams

Yang Lu2021

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Workgreen2021

A Robust and Accurate Approach to Detect Process Drifts from Event Streams

Yang Lu · Qifan Chen · Simon Kar-Sing Poon

12 citations·0/0 files stored·Lecture notes in computer science

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Business Process Modeling and Analysis Software Testing and Debugging Techniques Robotic Process Automation Applications

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References

2016Process Mining: Data Science in Action 2016Data Science in Action 2011Handling Concept Drift in Process Mining 2013Dealing With Concept Drifts in Process Mining 2004Process mining : extending the alpha-algorithm to mine short loops 2017Detecting sudden and gradual drifts in business processes from execution traces 2015Fast and Accurate Business Process Drift Detection 2012Online Techniques for Dealing with Concept Drift in Process Mining

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2023Integrated detection and localization of concept drifts in process mining with batch and stream trace clustering supportarticle · 19 shared references 2025Machine learning-based detection of concept drift in business processesarticle · 17 shared references 2023An Experimental Evaluation of Process Concept Drift Detectionarticle · 16 shared references 2021A Framework for Explainable Concept Drift Detection in Process Miningother · 12 shared references 2020CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Miningreview · 12 shared references 2020LCDD: Detecting Business Process Drifts Based on Local Completenessarticle · 10 shared references

Details

Year2021

Typeother

Published2021-01-01

VenueLecture notes in computer science

ISSN-L0302-9743

Languageen

Citations12

Files0 public / 0

ShelfBusiness Process Modeling and Analysis

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doi10.1007/978-3-030-85469-0_24

openalex_workW3136904625

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