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Authors: Sascha Bosse ; Abdulrahman Nahhas ; Matthias Pohl and Klaus Turowski

Affiliation: Very Large Business Applications Lab, Faculty of Computer Science Otto-von-Guericke University Magdeburg and Germany

ISBN: 978-989-758-369-8

Keyword(s): Cloud Computing, Optimization, as-a-Service, Business Analytics.

Abstract: Many organizations try to apply analytics in order to improve their business processes. More and more cloud services are offered to support these efforts. However, the support of prescriptive analytics is weak. While concepts for such an optimization-as-a-service exist, these require much expert knowledge in solution methods. In this paper, a workflow for optimization-as-a-service is proposed that utilizes an optimization knowledge base in which machine learning techniques are applied to automatically select and parametrize suitable solution algorithms. This would allow consumers to use the service without expert knowledge while reducing operational costs for providers.

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Paper citation in several formats:
Bosse, S.; Nahhas, A.; Pohl, M. and Turowski, K. (2019). Towards an Automated Optimization-as-a-Service Concept.In Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-369-8, pages 339-343. DOI: 10.5220/0007746303390343

@conference{iotbds19,
author={Sascha Bosse. and Abdulrahman Nahhas. and Matthias Pohl. and Klaus Turowski.},
title={Towards an Automated Optimization-as-a-Service Concept},
booktitle={Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2019},
pages={339-343},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007746303390343},
isbn={978-989-758-369-8},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Towards an Automated Optimization-as-a-Service Concept
SN - 978-989-758-369-8
AU - Bosse, S.
AU - Nahhas, A.
AU - Pohl, M.
AU - Turowski, K.
PY - 2019
SP - 339
EP - 343
DO - 10.5220/0007746303390343

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