Analysis of Data Warehouse Architectures: Modeling and Classification

QISHAN, Yang, Mouzhi GE and Markus HELFERT. Analysis of Data Warehouse Architectures: Modeling and Classification. In Proceedings of the 21st International Conference on Enterprise Information Systems. Crete, Greece: SciTePress, 2019, p. 604-611. ISBN 978-989-758-372-8. Available from: https://dx.doi.org/10.5220/0007728006040611.

Basic information

• Original name: Analysis of Data Warehouse Architectures: Modeling and Classification
• Authors: QISHAN, Yang (372 Ireland), Mouzhi GE (156 China, guarantor, belonging to the institution) and Markus HELFERT (276 Germany).
• Edition: Crete, Greece, Proceedings of the 21st International Conference on Enterprise Information Systems, p. 604-611, 8 pp. 2019.
• Publisher: SciTePress

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: Spain
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: printed version "print"
• RIV identification code: RIV/00216224:14330/19:00109110
• Organization unit: Faculty of Informatics
• ISBN: 978-989-758-372-8
• Doi: http://dx.doi.org/10.5220/0007728006040611
• UT WoS: 000570422800062
• Keywords in English: Data Warehouse; Architecture; Classification; Modeling; Big Data; Archimate
• Tags: firank_B
• Tags: International impact, Reviewed

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.