![]() Not sure how do we hide the measure though (because the measure is dummy, it is only used to establish M2M relationship between 2 dimensions). Bud said we can do it (see below), and from what I understand here, this is done by simply mapping a base measure to the fact table containing the M2M relationship, just like the way we create any base measure. Have searched the whole library and I couldn’t find M2M in 11g OLAP. This is critical for many business situations. ![]() If you are not familiar with M2M in SSAS, pls read Marco’s paper about M2M Revolution. The difference is that BISM is relational (tables), with “in-memory column” (VertiPaq), where as 11g’s OLAP is compressed MOLAP. BISM can expose itself as multidimensional interface (MDX) or SQL/relational interface (DAX). This “blend concept” is similar to SQL Server’s BISM concept in Denali (2011). Oracle OLAP has the ability to blend multidimensional and SQL models within the same query (see Bud’s comments below).Oracle mentions compression & cost-based aggregation (see page 7). SSAS is certainly compressed, but I’m not sure if SSAS aggregations are cost-based.This MOLAP storage solves the classic ROLAP problem of “MV aggregates can’t cover every permutation of every member of every attribute against other members of other attributes”. Oracle OLAP cubes are stored as true multidimensional store (I’m interpretting this as compressed MOLAP), not as relational tables with materialized view/MV.In the mean time we could use Tableau (see Jen’s session in SQLBits 8), Strategy Companion, CubePlayer or Excel 2010. In AS now we have Crescent to look forward to. Users are not expected to use SQL to query the cube. Answers), BO (since 9i AW/2003), Excel or other tool to browse Oracle OLAP. For my next DWBI project, I’m thinking about implementing Oracle 11g OLAP option instead of SSAS, so I’m comparing them.
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