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Learning Accurate LSTM Models of Business Processes · Study

WorkLearning Accurate LSTM Models of Business Processes

Manuel Camargo2019

Work2019

Learning Accurate LSTM Models of Business Processes

Manuel Camargo · Marlon Dumas · Oscar González-Rojas

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

No readable copy stored yet — this record is metadata only.

Business Process Modeling and Analysis Time Series Analysis and Forecasting Robotic Process Automation Applications

Abstract

No abstract was published for this record.

Citations in this library

Stored only when both papers are in the corpus — 241 citations exist upstream.

References

2017Predictive Business Process Monitoring with LSTM Neural Networks 2017Predicting process behaviour using deep learning 2008Towards comprehensive support for organizational mining

Cited by

2021A systematic literature review on state-of-the-art deep learning methods for process prediction 2022Predictive Process Monitoring 2020Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction 2023Deep Learning for Predictive Business Process Monitoring: Review and Benchmark 2021A Multi-View Deep Learning Approach for Predictive Business Process Monitoring 2024Predictive process monitoring: concepts, challenges, and future research directions 2020Process Outcome Prediction: CNN vs. LSTM (with Attention)2021Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning

Details

Year2019

Typeother

Published2019-01-01

VenueLecture notes in computer science

ISSN-L0302-9743

Languageen

Citations241

Files0 public / 0

ShelfBusiness Process Modeling and Analysis

Identifiers

doi10.1007/978-3-030-26619-6_19

openalex_workW2969940319

Links

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