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Authors: Andreas Kirmse ; Vadim Kraus ; Max Hoffmann and Tobias Meisen

Affiliation: RWTH Aachen University, Germany

ISBN: 978-989-758-298-1

ISSN: 2184-4992

Keyword(s): Big Data, Ingestion and Integration Pattern, Hadoop, OPC UA, Industry 4.0.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Collaboration and e-Services ; Complex Systems Modeling and Simulation ; Coupling and Integrating Heterogeneous Data Sources ; Data Engineering ; Databases and Information Systems Integration ; Distributed Database Systems ; e-Business ; Enterprise Information Systems ; Health Information Systems ; Information Systems Analysis and Specification ; Integration/Interoperability ; Interoperability ; Knowledge Management ; Knowledge Management and Information Sharing ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Sensor Networks ; Simulation and Modeling ; Society, e-Business and e-Government ; Software Agents and Internet Computing ; Software and Architectures ; Symbolic Systems ; Web Information Systems and Technologies

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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 ISSN 2184-4992, pages 175-182. DOI: 10.5220/0006776701750182

@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},
issn={2184-4992},
}

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
IS - 2184-4992
AU - Kirmse, A.
AU - Kraus, V.
AU - Hoffmann, M.
AU - Meisen, T.
PY - 2018
SP - 175
EP - 182
DO - 10.5220/0006776701750182

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