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Review on the article “An approach for incorporating quality- based cost- benefit analysis in data warehouse design”
This research seeks to address the issue of identifying the set of data which has the potential for producing net value for the organization through a model that helps to perform the cost benefit analysis.
Typical data warehouse store a set of data tables which are of 2 types: base table and materialized view that are generated from the existing data tables. Data warehouse is used to support a set of decision support views. There is a need to identify the set of DSV’s that provide the most net value to the organization and this will determine the set of data tables that should be stored in the data warehouse. DSV’s are used to perform particular tasks that are beneficial for the organization and also helps to achieve the organization goals.
In the data warehouse formulation phase who needs what data has to be considered which is identifying the needs of the IT and business users of the DW. According to Kalfus et al, all data are essential and must be stored and interesting patterns can be extracted from it. But the disadvantage of this is if all the data is stored the space consumed by it will be very high. Data warehouse quality is also an important factor for those who design the use of systems that support decision makers. The cost for improving the quality is high.
The cost benefit analysis can be used to identify the DSV’s that give the most net value to the organization, suitable quality levels and the data tables that are to be stored to develop these DSV’s . Each data table can be developed at different quality levels. Many factors affect the cost of the base table, materialized views and DSV’s. So these costs need to be identified in order to get the true costs.
The value of a DSV is determined by initially identifying the goals of the

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