|
Home > Archive > MS SQL Server OLAP > December 2005 > Rolling Trends
You are viewing an archived Text-only version of the thread.
To view this thread in it's original format and/or if you want to reply to
this thread please [click here]
|
|
| Scott Mescall 2005-12-22, 3:24 am |
| All,
I'm trying to write a calculated measure to find a rolling past 3 month
average and a past 12 month average. I'm great with month-to-month and
year-over-year. However, my MDX skills fall apart when I need anything past
the ".Previous" when dealing with measures. Can anyone get me started on the
right path with these types of calculations.
Thanks for the help!
-Scott Mescall
| |
| Scott Mescall 2005-12-22, 3:24 am |
| I'm using SQL Server 2000
"Scott Mescall" wrote:
> All,
>
> I'm trying to write a calculated measure to find a rolling past 3 month
> average and a past 12 month average. I'm great with month-to-month and
> year-over-year. However, my MDX skills fall apart when I need anything past
> the ".Previous" when dealing with measures. Can anyone get me started on the
> right path with these types of calculations.
>
> Thanks for the help!
>
> -Scott Mescall
| |
| Deepak Puri 2005-12-22, 3:24 am |
| Check out this article by William Pearson:
http://www.databasejournal.com/feat...cle.php/3397621[color=darkred]
...
Overview
In this article, we resume the focus of a group of articles that began
with Mastering Time: Change across Periods. In that article, as well as
its immediate successor, Mastering Time: Period - to - Date
Aggregations, we concentrated upon the Time dimension from the
perspective of our MDX queries. Our intent, in these and occasional
subsequent articles, is to explore ways to effectively report change
over time, as well as to accumulate those changes to present snapshots,
trends and other time-based metrics in a precise manner to meet typical
business requirements. As most of us realize, time is the most pervasive
dimension. A cube that has no time dimension is rare, indeed.
Consequently, this group of articles holds information that is of
interest to virtually anyone involved with MSAS cube design, development
and use.
In this article, we will examine "rolling average" aggregations, a
common business requirement. "Rolling," or "moving," averages, involve a
measure, the average under consideration, that is aggregated over a
progressively moving window of time periods. (While the window typically
involves time, the functions that define the "window" involved here can
certainly involve members of other dimensional levels).
...[color=darkred]
- Deepak
Deepak Puri
Microsoft MVP - SQL Server
*** Sent via Developersdex http://www.droptable.com ***
| |
| Scott Mescall 2005-12-22, 9:23 am |
| Thanks for the help!
"Deepak Puri" wrote:
> Check out this article by William Pearson:
>
> http://www.databasejournal.com/feat...cle.php/3397621
> ...
> Overview
>
> In this article, we resume the focus of a group of articles that began
> with Mastering Time: Change across Periods. In that article, as well as
> its immediate successor, Mastering Time: Period - to - Date
> Aggregations, we concentrated upon the Time dimension from the
> perspective of our MDX queries. Our intent, in these and occasional
> subsequent articles, is to explore ways to effectively report change
> over time, as well as to accumulate those changes to present snapshots,
> trends and other time-based metrics in a precise manner to meet typical
> business requirements. As most of us realize, time is the most pervasive
> dimension. A cube that has no time dimension is rare, indeed.
> Consequently, this group of articles holds information that is of
> interest to virtually anyone involved with MSAS cube design, development
> and use.
>
> In this article, we will examine "rolling average" aggregations, a
> common business requirement. "Rolling," or "moving," averages, involve a
> measure, the average under consideration, that is aggregated over a
> progressively moving window of time periods. (While the window typically
> involves time, the functions that define the "window" involved here can
> certainly involve members of other dimensional levels).
> ...
>
>
> - Deepak
>
> Deepak Puri
> Microsoft MVP - SQL Server
>
> *** Sent via Developersdex http://www.droptable.com ***
>
|
|
|
|
|