ssas

问题 I have built a multidimensional SSAS cube with some calculations. I have a calculated member in which I use the the parallelperiod function to calculate the previous month value according to the following code : Sum ( (EXISTING [TimeDim Transactions].[Year - Quarter - Month - Date].[date].MEMBERS) ,( ParallelPeriod ( [TimeDim Transactions].[Year - Quarter - Month - Date].[month] ,1 ,[TimeDim Transactions].[Year - Quarter - Month - Date].CurrentMember ) ,[Measures].[Net Amount] ) ) When I run

问题 SSAS and MDX newbie here. If I have an SSAS cube with Geography, Product as the dimensions and total sales as a measure. The following elements are under geography: EAST WEST NORTH SOUTH MID-ATLANTIC MOUNTAIN NORTH-WEST Products are: 1 2 3 4 For Product ID = 1, is there a way I can group some members into a "rest of the country" bucket and aggregate the sales? Meaning, the expected output is: Product ID Geography Sales 1 East 100 1 West 200 1 North 300 1 South 400 1 RestOfNation 1200 2 East

问题 So we have an multiple SSIS packages which are run parallel to perform incremental loads to the multiple cubes. This package runs every night. Occasionally we run into network issues. Now if SSIS looses the connection for even a moment, it fails all the packages. It does not automatically try to re connect even if the connection become active and is available again. It continues to execute the packages using the same connection info that it acquired during the start of execution. Now due to

问题 This feels like a really basic question, because it's a really basic function I'm looking for. I know you can do this: ([Dimension].CurrentMember IS [Dimension].[AParticularMember]) and you get a logical 1 or 0 as the function value. But how do you do this kind of thing, without concatenating IS functions in a whole lot of ugly ORs: ([Dimension].CurrentMember ISAMEMBEROF {[Dimension].[AMember],[Dimension].[AnotherMember],[Dimension].[YetAnotherMember]} ) ? This is really basic set operations,

问题 How can I remove the null values in the bottomcount, ie. i only want to see the products with actually sold units, I have tried with nonempty and non empty without success. with member [Measures].[Amount Sold] as ([Measures].[Internet Sales Amount]), format_string = "currency" select {[Measures].[Amount Sold]} on columns, bottomcount( order( {[Product].[Product].Members}, ([Measures].[Amount Sold]), bdesc), 5 )on rows 回答1: You could filter [Product].Members using NOT ISEMPTY() to exclude all

问题 I searched the Internet and StackOverflow and was not able to find an answer to this issue. I created an OLAP cube via SSAS with a date dimension. One of the attributes of the dimension is Activity Calendar Month . The NameColumn value is formatted like "January 2015", "February 2015", etc. The KeyColumns is set as a DATE value type for the first of the month, for each month. Example, NameColumn value "January 2015" = KeyColumns value "1/1/2015", "February 2015" = "2/1/2015", etc. The

问题 I need a dax measure which shows me which customers bought products B and C in last 90 days. And another one which shows me those whose bought products B and C in last 90 days. (based in my filter date context) Below is like it should be: Can someone help me? Here is a sample data if needed: FactSales KeyDate KeyCustomer KeyProduct Total 1 1 1 12,9 1 2 2 13 1 3 1 156,4 1 4 1 564,8 2 1 1 894,8 2 2 1 56,5 3 1 2 564,85 3 2 3 564,8 4 1 1 1325,6 4 2 1 132,3 Customer KeyCustomer Name 1 Jean 2 Mari

问题 I need to create a Calculation in the SQL Server Data Tools. Imagine that in my OLAP structure I have a City and a Seller Dimensions, and a fact. And in the City Dimension I have a city, a state, and a Population, and this is a Measure in a Dimension. I need to sum the Population in the cities which have sales. But I can't sum only the cities with sales, I can only show the state's total population. One seller can sell in many cities. Example: I'll filter by seller John and the query returns

问题 I'm new to MDX. I understand that MDX is a query language, not a data transformation language. However, I'm also aware that this distinction is partially meaningless; there is no clear line between transformation and reporting, and every query language is capable of some transformation. Proficiency in a query language requires knowing what transformations are reasonable, and which require a redesign of the underlying schema. From what I've seen of MDX, it clearly has features designed for

问题 WITH MEMBER [Measures].[NetPromoterScore] AS (IIF(([Measures].[Net Promoter Score] = 1/0 OR [Measures].[Net Promoter Score] = -1/0 OR ISEMPTY([Measures].[Net Promoter Score]) OR [Measures].[Avg Revenue Per Unit] = 1/0 OR [Measures].[Avg Revenue Per Unit] = -1/0 OR ISEMPTY([Measures].[Avg Revenue Per Unit]) ), NULL, [Measures].[Net Promoter Score])) MEMBER [Measures].[AvgRevenuePerUnit] AS (IIF(([Measures].[Net Promoter Score] = 1/0 OR [Measures].[Net Promoter Score] = -1/0 OR ISEMPTY(