Developing Reliable Taxonomic Features for Data Warehouse Architectures

Varování

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Autoři	YANG Qishan GE Mouzhi HELFERT Markus
Rok publikování	2020
Druh	Článek ve sborníku
Konference	Proceedings of the 22nd IEEE International Conference on Business Informatics - CBI 2020
Fakulta / Pracoviště MU	Fakulta informatiky
Citace
Doi	http://dx.doi.org/10.1109/CBI49978.2020.00033
Klíčová slova	data warehouse architecture; reliable feature; taxonomy
Popis	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.