数据仓库中的 2 个多维数据集可以直接相互比较吗？
有没有办法比较两个 OLAP 多维数据集之间的所有信息（聚合、详细级别）？例如，假设我想将为与 sql server 2000 一起创建的一个多维数据集与同一个多维数据集进行比较，但迁移到在 sql server 2005/2008 上运行 - 从技术上讲，它们应该为所有维度/度量组合返回相同的信息，但我需要一种验证方法。
我绝对不是开发人员，但我确实可以使用企业管理器，并且可能还可以使用 SAS 工具等，并且我了解一点 SQL，但其他方面就不多了。我知道您可以将二维（即表）数据集与 SQL 查询进行比较，也可以与 SAS 进行比较 - 但我从未听说过比较三维多维数据集的方法。
我在这件事上运气不好吗？我想做的最后一件事是查看两个立方体并通过 Excel 或其他东西并排比较所有可能的结果，我希望它可以以某种方式自动化。
Is there a way to compare all information (aggregates, down to the detail level) between two OLAP cubes? For example, say I wanted to compare one cube created to work with sql server 2000 to that same cube, but migrated to run on sql server 2005/2008 - technically they should both return the same information for all dimension / measure combinations but I need a way to verify.
I am definitely NOT a developer, but I do have access to enterprise manager, and potentially SAS tools etc. and I know a bit of SQL but not much else. I know that you can compare two dimensional (i.e. tables) data sets with sql queries, and also with SAS - but I have never heard of a way to compare three dimensional cubes.
Am I out of luck on this one? The last thing that I want to have to do is view both cubes and compare all possible results side by side via excel or something, I hope that it can be automated somehow.
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比较多维数据集意味着进行足够的“切片和切块”查询来证明您已经查询了所有事实。
您可以简单地获得各种事实表和维度表的总和和计数。如果它们相同，则两者之间的任何特定查询都很可能相同。
如果没有相关维度和事实的详细信息，就很难提出更具体的建议。
但是，请考虑您可以轻松计算多维数据集每个维度的一组小计。如果维度的行数相同，则结果将是相同的行数。如果总计相同，那么剩下的就是逐行比较小计。
如果您对每个维度都执行一次此操作，您应该有信心它们是相同的。或者，您会发现可以通过更详细的查询来探索的差异。
Comparing cubes means doing enough "slice-and-dice" queries to prove that you've queried all of the facts.
You can, simply, get a sum and count of the various fact and dimension tables. If those are the same, odds are good that any particular query will be the same between the two.
Without details on the dimensions and facts in question, it's hard to make a more specific recommendation.
However, consider that you can easily compute a set of subtotals for each dimension of the cube. If the dimensions are the same number of rows, the results will be the same number of rows. If the grand total is the same, then all that's left is row-by-row comparison of the subtotals.
If you do this once for each dimension, you should have some confidence that they're the same. Or, you'll find a difference that you can explore with more detailed queries.
最好的方法是通过交换行和列来比较多维数据集数据，并验证所有计数和总计是否正确匹配。
例如，如果您有特定位置的年度总计，那么交换位置和月份之间的值并验证它们是否正确匹配将是一个好方法。
The best approach is to compare the cube data by interchanging the rows and columns and verifying if all the counts and totals match properly.
For example, if you are having year-wise totals for a particular location, it would be a good approach to interchange the values between locations and the months and verifying whether they match properly.