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Learning of Process Representations Using Recurrent Neural Networks · Study

WorkLearning of Process Representations Using Recurrent Neural Networks

Alexander Seeliger2021

Work2021

Learning of Process Representations Using Recurrent Neural Networks

Alexander Seeliger · Stefan Luettgen · Timo Nolle · Max Mühlhäuser

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

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Business Process Modeling and Analysis Data Mining Algorithms and Applications Time Series Analysis and Forecasting

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References

2017Predicting process behaviour using deep learning 2009Trace Clustering in Process Mining 2006Discovering expressive process models by clustering log traces 2009Context Aware Trace Clustering: Towards Improving Process Mining Results 2010Trace Clustering Based on Conserved Patterns: Towards Achieving Better Process Models 2018Analyzing business process anomalies using autoencoders 2021Business process variant analysis: Survey and classification 2019BINet: Multi-perspective business process anomaly classification

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2023Global conformance checking measures using shallow representation and deep learning 2022Semi-Supervised Discovery of DNN-Based Outcome Predictors from Scarcely-Labeled Process Logs

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Details

Year2021

Typeother

Published2021-01-01

VenueLecture notes in computer science

ISSN-L0302-9743

Languageen

Citations10

Files0 public / 0

ShelfBusiness Process Modeling and Analysis

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doi10.1007/978-3-030-79382-1_7

openalex_workW3173178406

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