In my cube I have
Earnings as measure with MIN aggregation
Dimension: [Localization].[Type].&[center]
Dimension: {[Date].[Year].&[2017], [Date].[Year].&[2018]}
My query is:
What are the minimum earnings of the person who decides to buy
apartments in the city center, excluding 5% of the lowest, within
last 2 years?
Now my mdx query looks like that:
SELECT
[Measures].[MinEarnings] ON COLUMNS
FROM [cube]
WHERE
(
BottomCount ([Localization].[Type].&[center], 95, [Measures].[MinEarnings]),
{[Date].[Year].&[2017], [Date].[Year].&[2018]}
)
I have two problems:
Some earnings are 0 - how can I skip them in calculations?
If my query correctly excludes 5% of the lowest earnings?
First of all you should use toppercent not bottomcount. you want the min salary of a person who is not in last 5% not last 5. Toppercent will give you the top 95%.
Secondly to filter 0 you can use the following syntax
toppercent (
filter([Localization].[Type].&[center], [Measures].[MinEarnings]>0)
, 95, [Measures].[MinEarnings])
Even now placing the code in the where clause might not work, however try it. I would suggest that you move the toppercent to rows , then order it, then take the top1
topcount(
order(
toppercent (
filter([Localization].[Type].&[center], [Measures].[MinEarnings]>0)
,95, [Measures].[MinEarnings])
,[Measures].[MinEarnings],asc)
,1)
I have an example which gives the minum sales amount of cities, notice i have replaced nulls with 0 to make it as close as possible to your case
with member [Measures].[Internet Sales Amount2]
as
case when ([Measures].[Internet Sales Amount])=null then 0 else [Measures].[Internet Sales Amount] end
select [Measures].[Internet Sales Amount2]
on columns ,
topcount(order(toppercent(filter([Customer].[City].[City],[Measures].[Internet Sales Amount2]>0),95,[Measures].[Internet Sales Amount2]),[Measures].[Internet Sales Amount2],asc),1)
on rows
from [Adventure Works]
where [Customer].[Country].&[Canada]
in the picture below is the result before topcount 1
Related
I am trying to do Cumulative Sum/ Running Total using the function below so the user can use any date level, but the requirement is to ignore some of the attributes from the calculation.
Function used:
SUM(NULL:Axis(1).Item(0).Item(Axis(1).Item(0).Count-1).Hierarchy.CurrentMember, [Measures].[ Number of Ticket])
Example:
The table below shows the Cumulative Sum as expected
Example - 1
Here by adding another attribute, Program Remaining, as shown below, its changes the Cumulative behavior, Because Excel will add that attribute to the grouping so it reset the cumulative sum:
Example - 2
Is there a way that I can exclude the Program Remaining attribute from the calculation (I have another 4 attributes that I want to exclude) so that the cumulative can be increased just like the first table even with adding these attribute.
I really appreciate any help
Try using the below sample query
with
member
[Measures].[Internet Sales AmountRunningtotal]
as
case when [Measures].[Internet Sales Amount] = null then null
else
sum({[Product].[Subcategory].firstchild:[Product].[Subcategory].currentmember},[Measures].[Internet Sales Amount])
end
select {[Measures].[Internet Sales Amount],
[Measures].[Internet Sales AmountRunningtotal]
} on columns,
non empty
([Date].[Calendar Year].[Calendar Year],[Date].[Calendar Quarter of Year].[Calendar Quarter of Year],
[Product].[Category].[Category],[Product].[Subcategory].[Subcategory])
on
rows
from
[Adventure Works]
So I explored the sample sent, Your only solutions is to keep the date in the inner most position, your query qouted above will break if you are not using entire members of an attribute.
I want to calculate the grand total of a given measure in MDX by a column dimension (time dimension)
This is my MDX code:
SUM(
([DIM_TIME].[PERIOD]),
[Measures].[VALUE]
)
Basically, this is what I want to get :
But this is what I get:
Thanks for any possible help
Take a look at the sample query
with member
[Measures].[Internet Sales AmountTotal]
as
case when [Product].[Category].currentmember is [Product].[Category].defaultmember then null
else ([Product].[Category].defaultmember,[Measures].[Internet Sales Amount])
end
select
({[Date].[Calendar Year].&[2012],[Date].[Calendar Year].&[2013]},
{[Measures].[Internet Sales Amount],[Measures].[Internet Sales AmountTotal]})
on columns,
{[Product].[Category].members}
on rows
from [Adventure Works]
The following script is from this MSDN article http://msdn.microsoft.com/en-us/library/ms145988.aspx
SELECT
[Measures].[Internet Sales Amount] ON 0,
NONEMPTY(
[Customer].[Customer].[Customer].MEMBERS,
{([Date].[Calendar].[Date].&[20010701], [Measures].[Internet Sales Amount])}
) ON 1
FROM [Adventure Works]
I basically took the script and replaced the elements from the above with equivalent elements from our cube.
What would be the reason that my script is not limiting it's results to the date specified in the NONEMPTY function but is returning all customers and their lifetime sales amount?
Try this:
SELECT
[Measures].[Internet Sales Amount] ON 0,
NONEMPTY(
[Customer].[Customer].[Customer].MEMBERS
) ON 1
FROM [Adventure Works]
where
[Date].[Calendar].[Date].&[20010701]
All the customers might have a non-null internet sales amount for the date you have specified. Moreover the nonempty will just filter out those members where the condition is not met. In your case as all the customers has non-null Internet Sales value, none of the customers are filtered out. And the Internet sales amount in the axis 0 will not be limited to the date that is specified, instead it returns the sales amount for each customer across all the dates.
I want to create a barchart with a bar for each month and some measure.
But i also want to filter on a range of day which might not completly overlap some of the month.
When that happen I would like the aggregate count for those month to only aggregat over the days that fall in my date range not get the aggregate for the whole month.
Is that possible with MDX and if it is how should the request look like?
Create a second time dimension, using a virtual dimension of the original dimension. Use one dimension in the WHERE and another in the SELECT.
This often happens anyway if some people want 'Business Time' of quarters and periods, and others prefer months. Or if you have a financial year which runs April-April.
You can use subselect. You can find more information on this page and this page:
When a member is specified in the axis clause then that member with
its ascendants and descendants are included in the sub cube space for
the subselect; all non mentioned sibling members, in the axis or
slicer clause, and their descendants are filtered from the subspace.
This way, the space of the outer select has been limited to the
existing members in the axis clause or slicer clause, with their
ascendants and descendants as mentioned before.
Here is an example:
SELECT { [Customer].[Customer Geography].[Country].&[Australia]
, [Customer].[Customer Geography].[Country].&[United States]
} ON 1
, {[Measures].[Internet Sales Amount], [Measures].[Reseller Sales Amount]} ON 0
FROM ( SELECT {[Customer].[Customer Geography].[Country].&[Australia]
, [Customer].[State-Province].&[WA]&[US]} ON 0
FROM [Adventure Works]
)
The result will contain one row for Autralia and another one for the United States. With the subselect, I restricted the value of United Stated to the Washington state.
One way I found to do it with Mondrian is as follow
WITH MEMBER [Measures].[Units Shipped2] AS
SUM
(
{
exists([Store].[Store Country].currentmember.children,{[Store].[USA].[WA],[Store].[USA].[OR]})
},[Measures].[Units Shipped]
)
MEMBER [Measures].[Warehouse Sales2] AS
SUM
(
{
exists([Store].[Store Country].currentmember.children,{[Store].[USA].[WA],[Store].[USA].[OR]})
},[Measures].[Warehouse Sales]
)
SELECT
{[Measures].[Units Shipped2],[Measures].[Warehouse Sales2]} ON 0,
NON EMPTY [Store].[Store Country].Members on 1
FROM [Warehouse]
I am not sure if the filtering will be done in SQL like below and give good performance or be run locally
select Country, sum(unit_shipped)
where state in ('WA','OR' )
group by Country
I have a requirement to show a report which calculates the average selling unit price of each product and then multiplies this average by the number of units forecasted to sell in the next year (for each product).
At this point my main issue is getting this set up so the totals roll up correctly when viewing at product category level. Using AdventureWorks as an example (and sales orders instead of forecasts) I've got this far...
with
member [Measures].[Sales Order Value]
as sum(descendants([Product].[Product Categories].currentMember,
[Product].[Product Categories].[Product]),
[Measures].[Average Unit Price] * [Measures].[Order Count]),
format_string = "Currency"
select ([Date].[Calendar].[Calendar Year].&[2008],
{[Measures].[Sales Order Value] }) on columns,
[Product].[Product Categories].[Subcategory].members on rows
from [Adventure Works]
I think this is about right, I believe this is going down to product level to apply the calculation between product average unit price and any sales orders for the product.
My issue is that I think the average unit price is being calculated over all data, I need to alter this to pick up an average based on the last 12 months only.