4383145
doi
10.5281/zenodo.4383145
oai:zenodo.org:4383145
user-fau
Foerstel, Stefan
University of Applied Sciences Amberg-Weiden
Kittler, Thomas
infoteam Software AG
Kurzyukov, Andrey
Siemens Healthcare GmbH
Schwinn, Leo
Friedrich-Alexander-University Erlangen-Nürnberg
Zanca, Dario
Friedrich-Alexander-University Erlangen-Nürnberg
Hipp, Tobias
Siemens Healthcare GmbH
Da Jun, Sun
Siemens Shanghai Medical Equipment Ltd.
Schrapp, Michael
Siemens Healthcare GmbH
Rothgang, Eva
University of Applied Sciences Amberg-Weiden
Eskofier, Bjoern
Friedrich-Alexander-University Erlangen-Nürnberg
Industrial Benchmark Dataset for Customer Escalation Prediction
Nguyen, An
Friedrich-Alexander-University Erlangen-Nürnberg
url:https://github.com/annguy/customer-sentiment-monitor
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
machine learning, imbalanced data, industrial benchmark
<p>This is a real-world industrial benchmark dataset from a major medical device manufacturer for the prediction of customer escalations. The dataset contains features derived from IoT (machine log) and enterprise data including labels for escalation from a fleet of thousands of customers of high-end medical devices. </p>
<p>The dataset accompanies the publication "System Design for a Data-driven and Explainable Customer Sentiment Monitor" (submitted). We provide an anonymized version of data collected over a period of two years.</p>
<p>The dataset should fuel the research and development of new machine learning algorithms to better cope with real-world data challenges including sparse and noisy labels, and concept drifts. Additional challenges is the optimal fusion of enterprise and log based features for the prediction task. Thereby, interpretability of designed prediction models should be ensured in order to have practical relevancy. </p>
<p><strong>Supporting software</strong></p>
<p>Kindly use the corresponding <a href="https://github.com/annguy/customer-sentiment-monitor">GitHub repository</a> (https://github.com/annguy/customer-sentiment-monitor) to design and benchmark your algorithms. </p>
<p> </p>
<p><strong>Citation and Contact</strong><br>
</p>
<p>If you use this dataset please cite the following publication:</p>
<p><br>
</p>
<pre><code>@ARTICLE{9520354,
author={Nguyen, An and Foerstel, Stefan and Kittler, Thomas and Kurzyukov, Andrey and Schwinn, Leo and Zanca, Dario and Hipp, Tobias and Jun, Sun Da and Schrapp, Michael and Rothgang, Eva and Eskofier, Bjoern},
journal={IEEE Access},
title={System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data},
year={2021},
volume={9},
number={},
pages={117140-117152},
doi={10.1109/ACCESS.2021.3106791}}</code></pre>
<p> </p>
<p>If you would like to get in touch, please contact an.nguyen@fau.de.<br>
</p>
Zenodo
2020-12-21
info:eu-repo/semantics/other
4383144
user-fau
1.0
1630931028.12995
278302923
md5:09c502e523695b1d7b36ec161ffc780e
https://zenodo.org/records/4383145/files/feature_matrix_LSTM.pickle
274286272
md5:bddc15f0811ef3de0d0476b16741cd9c
https://zenodo.org/records/4383145/files/feature_matrix.pickle
public
https://github.com/annguy/customer-sentiment-monitor
Is referenced by
url
10.5281/zenodo.4383144
isVersionOf
doi