Calculated member for epics the issues of some of which have a specific label

Questions & Answers
1

Hi,

I’m trying to create a calculated member in the “issue” dimension, which shows all the issues of 4 different epics. But in 2 of the 4 Epics I only want to have the issues, which have a specific label.
Probably I should do this in “measures”, but I’d prefer to do that in the issue dimenion, using the aggregate function.
Anyways, I’m kind of stuck here. Anyone who could point me in the right direction?
Thx

2

Hi @Theo,
​
The best solution depends on your actual needs.
What are your next steps after retrieving the set of issues?
​
If you only need to find a specific numeric measure for relevant issues and they only have non-overlapping labels, you might use tuples to find the numbers.
​The simplest option might be like this.
​

([Measures].[Specific measure]​,
 [Epic Link]​.[Epic1])
+​
([Measures].[Specific measure],
 [Epic Link].[Epic1])
+
​([Measures].[Specific measure],
 [Epic Link].[Epic3],
​ [Labels].[Specific label 1])
+
​([Measures].[Specific measure],
 [Epic Link].[Epic4],
​ [Labels].[Specific label 2])

​However, if you​ want to display these issues or have several overlapping conditions, you need to filter the issues after iterating through the Issue dimension.
​The expression to create the relevant set might then be as follows.

​Aggregate(
 Filter(
--generate set of all issues
  DescendantsSet(
   [Issue].CurrentHierarchyMember,
   [Issue].CurrentHierarchy.Levels("Issue")),
--conditions for filter
--first condition - belongs to first two epics
   Sum(
     {[Epic Link].[Epic 1],
      [Epic Link].[Epic 2]},
  [Measures].[Issues created]) >0
 OR
--alternative - other two epics with condition on labels  
  (Sum(
     {[Epic Link].[Epic 3],
      [Epic Link].[Epic 4]},
  [Measures].[Issues created]) >0
 AND
 (  ([Labels].[Label 1],
     [Measures].[Issues created])
  +
    ([Labels].[Label 2],
     [Measures].[Issues created])
  )>0
 )
))

​However, the best approach depends on the actual conditions and requirements.
​
​Regards,
​Oskars / support@eazyBI.com