Publication details
Developing Reliable Taxonomic Features for Data Warehouse Architectures
| Authors | |
|---|---|
| Year of publication | 2020 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 22nd IEEE International Conference on Business Informatics - CBI 2020 |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1109/CBI49978.2020.00033 |
| Keywords | data warehouse architecture; reliable feature; taxonomy |
| Description | 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. |