An Architecture for Efficient Integration and Harmonization of Heterogeneous, Distributed Data Sources Enabling Big Data Analytics

Andreas Kirmse, Vadim Kraus, Max Hoffmann, Tobias Meisen

2018

Abstract

We present a lightweight integration architecture as an enabler for the application of process optimization via Big Data analytics and machine learning in large scale, multi-site manufacturing companies by harmonizing heterogeneous data sources. The reference implementation of the architecture is entirely based on open-source software and makes use of message queuing techniques in combination with Big Data related storage and extraction technologies. The approach specifically targets challenges related to different network zones and security levels in enterprise information architectures and across divergent production sites.

Paper Citation

in Harvard Style

Kirmse A., Kraus V., Hoffmann M. and Meisen T. (2018). An Architecture for Efficient Integration and Harmonization of Heterogeneous, Distributed Data Sources Enabling Big Data Analytics.In Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-298-1, pages 175-182. DOI: 10.5220/0006776701750182

in Bibtex Style

@conference{iceis18,
author={Andreas Kirmse and Vadim Kraus and Max Hoffmann and Tobias Meisen},
title={An Architecture for Efficient Integration and Harmonization of Heterogeneous, Distributed Data Sources Enabling Big Data Analytics},
booktitle={Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2018},
pages={175-182},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006776701750182},
isbn={978-989-758-298-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - An Architecture for Efficient Integration and Harmonization of Heterogeneous, Distributed Data Sources Enabling Big Data Analytics
SN - 978-989-758-298-1
AU - Kirmse A.
AU - Kraus V.
AU - Hoffmann M.
AU - Meisen T.
PY - 2018
SP - 175
EP - 182
DO - 10.5220/0006776701750182