Analysis of data warehouse architectures: modeling and classification
Yang, Qishan, Ge, Mouzhi and Helfert, MarkusORCID: 0000-0001-6546-6408
(2019)
Analysis of data warehouse architectures: modeling and classification.
In: 21st International Conference on Enterprise Information Systems (ICEIS), 3 - 5 May, 2019, Heraklion, Crete, Greece.
ISBN 978-989-758-372-8
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 representativeDWHAs 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.
Metadata
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Conference
Refereed:
Yes
Uncontrolled Keywords:
Data Warehouse; Architecture; Classification; Modeling; Big Data; Archimate
Filipe, Joaquim, Smialek, Michal, Brodsky, Alexander and Hammoudi, Slimane, (eds.)
Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS 2019).
2.
Scitepress. ISBN 978-989-758-372-8
This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:
This publication is supported by the Science Foundation Ireland grant SFI/12/RC/2289 to Insight Centre for Data Analytics. INSIGHT is funded under the SFI Research Centres Programme and is co-funded under the European Regional Development Fund.
ID Code:
23520
Deposited On:
03 Jul 2019 11:48 by
Qishan Yang
. Last Modified 03 Jul 2019 11:48