Measures at different levels

I have a fact table that contains the sales order lines. Also in the present is made is of the reserved shares which will be used to fill the sales order lines. Each order line can be filled with several stock locations. Gluttony, I have a fact table that looks something like this for a single command line:

Order line number, amount of command-line, batch Stock number, quantity of booking

ORD_LINE1, 35, LOT1, 10
ORD_LINE1, 35, BATCH2, 17
ORD_LINE1, 35, LOT3, 8

My problem is that the measure of the amount of command line, when totaled in OBIEE gives me 105 (35 * 3) - when it should be 35.

How can I display a total of 35 for amount of command line, although they show 35 for amount of booking?

My underlying SQL query is very sensitive and so would stay away from the analytical functions. Is there something I can do with hierarchies?

It comes to OBIEE 10 g

Edited by: 934462 may 6, 2013 03:09

Edited by: 934462 may 6, 2013 03:15

It seems that your amount depends on only the number of command line while 'reserved' measure depends on many and line number order. So, you have combined measures of two different grains in the same table. Follow these steps:

1 fix the problem at the level of the database of this division in two separate facts and model them as two logical facts connected via a number of command line only one dimension. It's cleaner, and it should run faster.
OR
2. you can copy the amount ordered as a new logical column but not prescribe any rules of aggregation. Align on the presentation layer, and use it as a common command line attribute in your report. Given that this column would be part of the group by, he can sacrifice some performance.
OR
3 the extent of the quantity ordered, go to the tab of aggregation, see "Based on Dimensions", in the new list, choose MAX() as the rule of aggregation for dimension of command lines (create a logical and associated table dummy command line attribute hierarchy if you don't have them) and SUM() as rule of aggregation for the size of the location of stock. It's a little experimental, I doubt how useful it would be in your scenario.

Tags: Business Intelligence

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