Process Data Properties Matter: Introducing Gated Convolutional Neural Networks (GCNN) and Key-Value-Predict Attention Networks (KVP) for Next Event Prediction with Deep Learning
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%0 Journal Article
%1 heinrich2021process
%A Heinrich, Kai
%A Zschech, Patrick
%A Janiesch, Christian
%A Bonin, Markus
%D 2021
%J Decision Support Systems
%K bpm bwljp1 myown
%P 113494
%R 10.1016/j.dss.2021.113494
%T Process Data Properties Matter: Introducing Gated Convolutional Neural Networks (GCNN) and Key-Value-Predict Attention Networks (KVP) for Next Event Prediction with Deep Learning
%V 143
@article{heinrich2021process,
added-at = {2021-01-08T15:59:25.000+0100},
author = {Heinrich, Kai and Zschech, Patrick and Janiesch, Christian and Bonin, Markus},
biburl = {https://www.bibsonomy.org/bibtex/260fe84929a26a3817b7aba451691ca47/famerlor},
doi = {10.1016/j.dss.2021.113494},
interhash = {bffcea0f2bc174cdf4cdac7d28f129ba},
intrahash = {60fe84929a26a3817b7aba451691ca47},
journal = {Decision Support Systems},
keywords = {bpm bwljp1 myown},
pages = 113494,
timestamp = {2022-10-26T12:35:03.000+0200},
title = {Process Data Properties Matter: Introducing Gated Convolutional Neural Networks (GCNN) and Key-Value-Predict Attention Networks (KVP) for Next Event Prediction with Deep Learning},
volume = 143,
year = 2021
}