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Authors: Qishan Yang 1 ; Mouzhi Ge 2 and Markus Helfert 1

Affiliations: 1 Insight Centre for Data Analytics, Dublin City University, Dublin and Ireland ; 2 Faculty of Informatics, Masaryk University, Brno and Czech Republic

ISBN: 978-989-758-372-8

Keyword(s): Data Warehouse, Architecture, Classification, Modeling, Big Data, Archimate.

Abstract: With decades of development and innovation, data warehouses and their architectures have been extended to a variety of derivatives in various environments to achieve different organisations’ requirements. Although there are some ad-hoc studies on data warehouse architecture (DWHA) investigations and classifications, limited research is relevant to systematically model and classify DWHAs. Especially in the big data era, data is generated explosively. More emerging architectures and technologies are leveraged to manipulate and manage big data in this domain. It is therefore valuable to revisit and investigate DWHAs with new innovations. In this paper, we collect 116 publications and model 73 disparate DWHAs using Archimate, then 9 representative DWHAs are identified and summarised into a ”big picture”. Furthermore, it proposes a new classification model sticking to state-of-the-art DWHAs. This model can guide researchers and practitioners to identify, analyse and compare differences and trends of DWHAs from componental and architectural perspectives. (More)

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Paper citation in several formats:
Yang, Q.; Ge, M. and Helfert, M. (2019). Analysis of Data Warehouse Architectures: Modeling and Classification.In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-372-8, pages 604-611. DOI: 10.5220/0007728006040611

@conference{iceis19,
author={Qishan Yang. and Mouzhi Ge. and Markus Helfert.},
title={Analysis of Data Warehouse Architectures: Modeling and Classification},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2019},
pages={604-611},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007728006040611},
isbn={978-989-758-372-8},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Analysis of Data Warehouse Architectures: Modeling and Classification
SN - 978-989-758-372-8
AU - Yang, Q.
AU - Ge, M.
AU - Helfert, M.
PY - 2019
SP - 604
EP - 611
DO - 10.5220/0007728006040611

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