Analysis of Data Warehouse Architectures: Modeling and
Classification

D 2019

Analysis of Data Warehouse Architectures: Modeling and Classification

QISHAN, Yang; Mouzhi GE and Markus HELFERT

Basic information

Original name

Analysis of Data Warehouse Architectures: Modeling and Classification

Authors

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

Edition

Crete, Greece, Proceedings of the 21st International Conference on Enterprise Information Systems, p. 604-611, 8 pp. 2019

Publisher

SciTePress

Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Spain

Confidentiality degree

is not subject to a state or trade secret

Publication form

printed version "print"

RIV identification code

RIV/00216224:14330/19:00109110

Organization unit

Faculty of Informatics

ISBN

978-989-758-372-8

DOI

http://dx.doi.org/10.5220/0007728006040611

UT WoS

000570422800062

EID Scopus

2-s2.0-85067515216

Keywords in English

Data Warehouse; Architecture; Classification; Modeling; Big Data; Archimate

Abstract

In the original language

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 representative DWHAs 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.

Cite

QISHAN, Yang; Mouzhi GE and Markus HELFERT. Analysis of Data Warehouse Architectures: Modeling and Classification. In Proceedings of the 21st International Conference on Enterprise Information Systems. Crete, Greece: SciTePress, 2019, p. 604-611. ISBN 978-989-758-372-8. Available from: https://dx.doi.org/10.5220/0007728006040611.

@inproceedings{1495036,
   author = {Qishan, Yang and Ge, Mouzhi and Helfert, Markus},
   address = {Crete, Greece},
   booktitle = {Proceedings of the 21st International Conference on Enterprise Information Systems},
   doi = {http://dx.doi.org/10.5220/0007728006040611},
   keywords = {Data Warehouse; Architecture; Classification; Modeling; Big Data; Archimate},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Crete, Greece},
   isbn = {978-989-758-372-8},
   pages = {604-611},
   publisher = {SciTePress},
   title = {Analysis of Data Warehouse Architectures: Modeling and Classification},
   year = {2019}
}

TY  - CONF
ID  - 1495036
AU  - Qishan, Yang - Ge, Mouzhi - Helfert, Markus
PY  - 2019
TI  - Analysis of Data Warehouse Architectures: Modeling and Classification
PB  - SciTePress
CY  - Crete, Greece
SN  - 9789897583728
KW  - Data Warehouse
KW  - Architecture
KW  - Classification
KW  - Modeling
KW  - Big Data
KW  - Archimate
N2  - 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 representative DWHAs 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.
ER  -

QISHAN, Yang; Mouzhi GE and Markus HELFERT. Analysis of Data Warehouse Architectures: Modeling and Classification. In \textit{Proceedings of the 21st International Conference on Enterprise Information Systems}. Crete, Greece: SciTePress, 2019, p.~604-611. ISBN~978-989-758-372-8. Available from: https://dx.doi.org/10.5220/0007728006040611.

Přehled o publikaci