ISSN: 2222-6990
Open access
The data warehouses are considered modern ancient techniques, since the early days for the relational databases, the idea of the keeping a historical data for reference when it needed has been originated, and the idea was primitive to create archives for the historical data to save these data, despite of the usage of a special techniques for the recovery of these data from the different storage modes. This research applied of structured databases for a trading company operating across the continents, has a set of branches each one has its own stores and showrooms, and the company branch’s group of sections with specific activities, such as stores management, showrooms management, accounting management, contracts and other departments. It also assumes that the company center exported software to manage databases for all branches to ensure the safety performance, standardization of processors and prevent the possible errors and bottlenecks problems. Also the research provides this methods the best requirements have been used for the applied of the data warehouse (DW), the information that managed by such an applied must be with high accuracy. It must be emphasized to ensure compatibility information and hedge its security, in schemes domain, been applied to a comparison between the two schemes (Star and Snowflake Schemas) with the concepts of multidimensional database. It turns out that Star Schema is better than Snowflake Schema in (Query complexity, Query performance, Foreign Key Joins),And finally it has been concluded that Star Schema center fact and change, while Snowflake Schema center fact and not change.
AbuAli, A. N., & Abu-Addose, H. Y. (2010). Data warehouse critical success factors. European Journal of Scientific Research, 42(2), 326–335.
Besterfield, D. H., Besterfield-Michna, C., Besterfield, G. H., & Besterfield-Sacre, M. (n.d.) (2009). Total quality management. 1995. Prentice-Hall Inc, Englewood Cliffs.
Bhansali, N. (2009). Strategic data warehousing: achieving alignment with business. Auerbach Publications.
Chau, K. W., Cao, Y., Anson, M., & Zhang, J. (2003). Application of data warehouse and decision support system in construction management. Automation in Construction, 12(2), 213–224.
Inmon, H. W. (2005). Building the data warehouse, Fourth Edition Published by Wiley Publishing, Inc., Indianapolis, Indiana. John wiley & sons.
Kimball, R., Reeves, L., Ross, M., & Thornthwaite, W. (1998). The data warehouse lifecycle toolkit: expert methods for designing, developing, and deploying data warehouses. John Wiley & Sons.
Manjunath, T. N., & Hegadi, R. S. (2013). Data Quality Assessment Model for Data Migration Business Enterprise. International Journal of Engineering and Technology (IJET), 5(1).
Manjunath, T. N., Hegadi, R. S., & Ravikumar, G. K. (2011). Analysis of data quality aspects in datawarehouse systems. International Journal of Computer Science and Information Technologies, 2(1), 477–485.
Orr, K. (1998). Data quality and systems theory. Communications of the ACM, 41(2), 66–71.
Prat, N., Comyn-Wattiau, I., & Akoka, J. (2011). Combining objects with rules to represent aggregation knowledge in data warehouse and OLAP systems. Data & Knowledge Engineering, 70(8), 732–752.
Santoso, L. W., & Gunadi, K. (2007). A proposal of data quality for data warehouses environment.
Jurnal Informatika, 7(2), 143–148.
Singhal, A. (2007). Data warehousing and data mining techniques for cyber security (Vol. 31).
Springer Science & Business Media.
Tayi, G. K., & Ballou, D. P. (1998). Examining data quality. Communications of the ACM, 41(2), 54–57.
Vassiliadis, P., Bouzeghoub, M., & Quix, C. (2000). Towards quality-oriented data warehouse usage and evolution. Information Systems, 25(2), 89–115.
Wang, J. (2009). Encyclopedia of Data Warehousing and Mining, Second Edition. Published by Information Science Reference. United States of America, I A-Data P.
Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5–33.
Yu, H., Xiao-yi, Z., Zhen, Y., & Guo-quan, J. (2009). A universal data cleaning framework based on user model. In Computing, Communication, Control, and Management, 2009. CCCM 2009. ISECS International Colloquium on (Vol. 2, pp. 200–202). IEEE.
In-Text Citation: (Mohammed, 2019)
To Cite this Article: Mohammed, K. I. (2019). Data Warehouse Design and Implementation Based on Star Schema vs. Snowflake Schema. International Journal of Academic Research in Business and Social Sciences, 9(14), 25–38.
Copyright: © 2019 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode