Developing Reliable Taxonomic Features for Data Warehouse
Architectures

D 2020

Developing Reliable Taxonomic Features for Data Warehouse Architectures

YANG, Qishan; Mouzhi GE and Markus HELFERT

Basic information

Original name

Developing Reliable Taxonomic Features for Data Warehouse Architectures

Authors

YANG, Qishan (372 Ireland); Mouzhi GE (156 China, guarantor, belonging to the institution) and Markus HELFERT (276 Germany)

Edition

Antwerp, Belgium, Proceedings of the 22nd IEEE International Conference on Business Informatics - CBI 2020, p. 241-249, 9 pp. 2020

Publisher

IEEE

Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

is not subject to a state or trade secret

Publication form

electronic version available online

RIV identification code

RIV/00216224:14330/20:00115621

Organization unit

Faculty of Informatics

ISBN

978-1-7281-9926-9

ISSN

DOI

https://doi.org/10.1109/CBI49978.2020.00033

UT WoS

000621582600026

EID Scopus

2-s2.0-85089272745

Keywords in English

data warehouse architecture; reliable feature; taxonomy

Abstract

In the original language

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.

Cite

YANG, Qishan; Mouzhi GE and Markus HELFERT. Developing Reliable Taxonomic Features for Data Warehouse Architectures. Online. In 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://doi.org/10.1109/CBI49978.2020.00033.

@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 = {https://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  - CONF
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://doi.org/10.1109/CBI49978.2020.00033.

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