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@inproceedings{1654178,
author = {Yang, Qishan and Ge, Mouzhi and Helfert, Markus},
address = {Antwerp, Belgium},
booktitle = {Proceedings of the 22nd IEEE International Conference on Business Informatics - CBI 2020},
doi = {http://dx.doi.org/10.1109/CBI49978.2020.00033},
keywords = {data warehouse architecture; reliable feature; taxonomy},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Antwerp, Belgium},
isbn = {978-1-7281-9926-9},
pages = {241-249},
publisher = {IEEE},
title = {Developing Reliable Taxonomic Features for Data Warehouse Architectures},
year = {2020}
}
TY - JOUR
ID - 1654178
AU - Yang, Qishan - Ge, Mouzhi - Helfert, Markus
PY - 2020
TI - Developing Reliable Taxonomic Features for Data Warehouse Architectures
PB - IEEE
CY - Antwerp, Belgium
SN - 9781728199269
KW - data warehouse architecture
KW - reliable feature
KW - taxonomy
N2 - Since there is a large variety of data warehouse architectures with different structures and components, it is very difficult and time-consuming to systematically analyse them and obtain insights from those architectures. One effective way to understand those architectures is using a taxonomy to classify them. However, most of the taxonomic features are derived in an ad-hoc way and the reliability of those features is unknown. This paper therefore is to develop a set of reliable features by modeling different data warehouse architectures and further generate the structural knowledge represented by a taxonomy. This taxonomy is further validated by evaluating two real-world data warehouse architectures from IBM and Facebook.
ER -
YANG, Qishan, Mouzhi GE and Markus HELFERT. Developing Reliable Taxonomic Features for Data Warehouse Architectures. Online. In \textit{Proceedings of the 22nd IEEE International Conference on Business Informatics - CBI 2020}. Antwerp, Belgium: IEEE, 2020, p.~241-249. ISBN~978-1-7281-9926-9. Available from: https://dx.doi.org/10.1109/CBI49978.2020.00033.
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