I. Introduction
Although the purpose of data warehouses is to store historical data, they can get saturated after a few years of operation. To overcome this problem, the solution is generally to archive older data when new data arrive if cost of maintaining older data cannot be justified economically. This solution is not satisfactory because analyses based on long term historical data become impossible. As a matter of fact analysis of data cannot be done on archived data without re-loading them in the data warehouse; and the cost of loading back a large dataset of archived data is too high to be operated just for one analysis. The archiving of data makes them hard to manage and query efficiently. So, achived data must be considered as lost data in a busness intelligence perspective.