Analysis of Data Warehouse Architectures: Modeling and
Classification

D 2019

Analysis of Data Warehouse Architectures: Modeling and Classification

QISHAN, Yang, Mouzhi GE a Markus HELFERT

Základní údaje

Originální název

Analysis of Data Warehouse Architectures: Modeling and Classification

Autoři

QISHAN, Yang (372 Irsko), Mouzhi GE (156 Čína, garant, domácí) a Markus HELFERT (276 Německo)

Vydání

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

Nakladatel

SciTePress

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Španělsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Kód RIV

RIV/00216224:14330/19:00109110

Organizační jednotka

Fakulta informatiky

ISBN

978-989-758-372-8

DOI

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

UT WoS

000570422800062

Klíčová slova anglicky

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

Štítky

firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 6. 5. 2020 15:31, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Citovat

QISHAN, Yang, Mouzhi GE a 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, s. 604-611. ISBN 978-989-758-372-8. Dostupné z: 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  - JOUR
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 a 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, s.~604-611. ISBN~978-989-758-372-8. Dostupné z: https://dx.doi.org/10.5220/0007728006040611.

Podrobný výpis o publikaci