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An interdisciplinary comparison of sequence modeling methods for next-element prediction · Study

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ArticleAn interdisciplinary comparison of sequence modeling methods for next-element prediction

Niek Tax2020

OA

Articlegreen2020

An interdisciplinary comparison of sequence modeling methods for next-element prediction

Niek Tax · Irene Teinemaa · Sebastiaan J. van Zelst

58 citations·0/0 files stored·Software & Systems Modeling

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

Abstract

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Citations in this library

Stored only when both papers are in the corpus — 58 citations exist upstream.

References

2016Process Mining 2005The ProM Framework: A New Era in Process Mining Tool Support 2012Replaying history on process models for conformance checking and performance analysis 2013Discovering Block-Structured Process Models from Event Logs - A Constructive Approach 2017Predictive Business Process Monitoring with LSTM Neural Networks 2011Flexible Heuristics Miner (FHM)2014Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour 2008Process mining: a two-step approach to balance between underfitting and overfitting

Cited by

2023Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring 2020Predictive Process Mining Meets Computer Vision 2020Supporting Automatic System Dynamics Model Generation for Simulation in the Context of Process Mining 2022Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning 2024A review of AI and machine learning contribution in business process management (process enhancement and process improvement approaches)2021Incremental Predictive Process Monitoring: The Next Activity Case 2021Discovering generative models from event logs: data-driven simulation vs deep learning 2020A Graph-Based Approach to Interpreting Recurrent Neural Networks in Process Mining

Details

Year2020

Typearticle

Published2020-04-07

VenueSoftware & Systems Modeling

ISSN-L1619-1366

Languageen

Citations58

Files0 public / 0

ShelfBusiness Process Modeling and Analysis

Identifiers

doi10.1007/s10270-020-00789-3

openalex_workW2898799686

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