Journal of Information Systems Engineering and Management

Research in Big Data Warehousing using Hadoop
Abderrazak Sebaa 1 * , Fatima Chikh 1, Amina Nouicer 1, Abdelkamel Tari 1
More Detail
1 LIMED laboratory, Computer Science Department, University of Bejaia, Bejaia, ALGERIA
* Corresponding Author
Research Article

Journal of Information Systems Engineering and Management, 2017 - Volume 2 Issue 2, Article No: 10
https://doi.org/10.20897/jisem.201710

Published Online: 30 Mar 2017

Views: 3504 | Downloads: 2779

How to cite this article
APA 6th edition
In-text citation: (Sebaa et al., 2017)
Reference: Sebaa, A., Chikh, F., Nouicer, A., & Tari, A. (2017). Research in Big Data Warehousing using Hadoop. Journal of Information Systems Engineering and Management, 2(2), 10. https://doi.org/10.20897/jisem.201710
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Sebaa A, Chikh F, Nouicer A, Tari A. Research in Big Data Warehousing using Hadoop. J INFORM SYSTEMS ENG. 2017;2(2):10. https://doi.org/10.20897/jisem.201710
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Sebaa A, Chikh F, Nouicer A, Tari A. Research in Big Data Warehousing using Hadoop. J INFORM SYSTEMS ENG. 2017;2(2), 10. https://doi.org/10.20897/jisem.201710
Chicago
In-text citation: (Sebaa et al., 2017)
Reference: Sebaa, Abderrazak, Fatima Chikh, Amina Nouicer, and Abdelkamel Tari. "Research in Big Data Warehousing using Hadoop". Journal of Information Systems Engineering and Management 2017 2 no. 2 (2017): 10. https://doi.org/10.20897/jisem.201710
Harvard
In-text citation: (Sebaa et al., 2017)
Reference: Sebaa, A., Chikh, F., Nouicer, A., and Tari, A. (2017). Research in Big Data Warehousing using Hadoop. Journal of Information Systems Engineering and Management, 2(2), 10. https://doi.org/10.20897/jisem.201710
MLA
In-text citation: (Sebaa et al., 2017)
Reference: Sebaa, Abderrazak et al. "Research in Big Data Warehousing using Hadoop". Journal of Information Systems Engineering and Management, vol. 2, no. 2, 2017, 10. https://doi.org/10.20897/jisem.201710
ABSTRACT
Traditional data warehouses have played a key role in decision support system until the recent past. However, the rapid growing of the data generation by the current applications requires new data warehousing systems: volume and format of collected datasets, data source variety, integration of unstructured data and powerful analytical processing. In the age of the Big Data, it is important to follow this pace and adapt the existing warehouse systems to overcome the new issues and challenges. In this paper, we focus on the data warehousing over big data. We discuss the limitations of the traditional ones. We present its alternative technologies and related future work for data warehousing
KEYWORDS
REFERENCES
---
LICENSE
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.