Using different levels of the same dimension

If I have 2 fact tables and a conforming time dimension. Can I make a join so that one table will ignore different years completely? (1 table has only 1 year of data, so it should display same values for different years for other table -yet, I still want to be able to drill-down from Year-to-Detail). I am somewhat successful at seeing it ok on a Annual level (when setting the desired metric to Grand Total Level - but aggregate navigation doesn't work correctly -I'm locked at annual level). Thanks

HI mma (and anyone else who was following).
Here's an update:
a) AGO function doesn't support anything non-integer - it's official and the product enhancement request has been filled. Go figure - they cancel time-series wizard - but at the same time - the AGO isn't fully scalable. Since we're developing a brand-new RPD - and we weren't using any YAGO, MAGO, etc. tables - this has bitten us later than sooner.
b) using CASE statement is no panacea (change for each year).
c) your method (time.key=time.key (of 2005) + MOD (time,key, 365) would probably work in my situation (I tried it - and it wasn't that difficult to implement), UNLESS I'd get the following RPD integrity error - "Error: Using a complex join in table that sources time dimension" (I tried aliases as well).
d) Right now, I'm just doing that metric on Annual level. Creating a view in Oracle that only contains needed data (for 2006) , using it as a physical table in physical layer, and connecting with all foreign keys BUT time.
I still think there must be a better way without complex ETL and without creating additional column. LOJ and ROJ and FOJ didn't work.
Thanks for looking at this again.

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This might be a stupid question, but I am wondering why my Aironet 1602i and 2602i access points connected to the same controller would use different channels for the same network. Would this be to avoid interference? If so, I am planning to put 9 access points throughout the office and they will be fairly close to each other. Would this increase the risk of interference?
Thanks,
Tibet

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Querying last element in a dimension based on another level in the same dimension using MDX

Summary: I need to write an MDX that returns the last date available in the level in the date dimension based for each month in the same dimension.
Details: i have the a dimension which has the following levels.
Dim_Date : Date (key) , Month (name of month), Quarter, Year.
The query i m trying to come up with would return the result something like
Month || Max_date || measure1
Jan 2014-01-25 100
Feb 2014-02-28 200
Please let me know if the above makes sense. Basically i m trying to get the max date for each month.
Thanks
Sri

Hi SrikanthGS,
Assuming that with the last date available for the measure you mean the last date with non-empty figures, consider the following against the AW:
SELECT [Measures].[Internet Sales Amount] ON 0,
Generate (
[Date].[Calendar].[Month].Members,
( Exists ( [Date].[Month of Year].[Month of Year], [Date].[Calendar].CurrentMember ),
Tail (
NonEmpty (
Descendants ( [Date].[Calendar].CurrentMember, [Date].[Calendar].[Date] ),
[Measures].[Internet Sales Amount]
1
) ON 1
FROM [Adventure Works]
WHERE [Product].[Product Categories].[Subcategory].&[1]
Philip,

Why is the Star Transformation using two indexes for the same dimension?

Hi,
Recently, I have made an investigation about the Star Transformation feature. I have found a strange test case, which plays an important role in my strategy for our overall DWH architecture. Here it is:
The Strategy:
I would like to have the classical Star Transformation approach (single column Bitmap Indexes for each dimension foreign key column in the fact table), together with additional Bitmap Join Indexes for some of the dimension attributes, which would benefit from the materialization of the join (bitmap merge operation will be skipped/optimized).
The query:
select dp.brand, ds. region_name, dc.region_name
     , count(*), sum(f.extended_price)
from fact_line_item f
     , dim_part       dp
     , dim_supplier   ds
     , dim_customer   dc
where dp.mfgr        = 10                 -- dimension selectivity = 1/10 --> acttual/fact selectivity = 6/10
   and f.part_dk      = dp.dk
   and ds.region_name = 'REGION #1' -- dimension selectivity = 1/9
   and f.supplier_dk = ds.dk
   and dc.region_name = 'REGION #1' -- dimension selectivity = 1/11
   and f.customer_dk = dc.dk
group by dp.brand, ds. region_name, dc.region_name
The actual plan:
| Id | Operation                              | Name                        | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers | Reads |
|   0 | SELECT STATEMENT                       |                             |      1 |        | 3247 (100)|      1 |00:01:42.05 |     264K|    220K|
|   1 | HASH GROUP BY                         |                             |      1 |      2 | 3247   (1)|      1 |00:01:42.05 |     264K|    220K|
|* 2 |   HASH JOIN                            |                             |      1 | 33242 | 3037   (1)|    217K|00:01:29.67 |     264K|    220K|
|* 3 |    TABLE ACCESS FULL                   | DIM_SUPPLIER                |      1 |   1112 |   102   (0)|   1112 |00:00:00.01 |     316 |      4 |
|* 4 |    HASH JOIN                           |                             |      1 | 33245 | 2934   (1)|    217K|00:01:29.10 |     264K|    220K|
|* 5 |     TABLE ACCESS FULL                  | DIM_CUSTOMER                |      1 |    910 |   102   (0)|    910 |00:00:00.08 |     316 |      8 |
|* 6 |     HASH JOIN                          |                             |      1 | 33248 | 2831   (1)|    217K|00:01:28.57 |     264K|    220K|
|* 7 |      TABLE ACCESS FULL                 | DIM_PART                    |      1 |     10 |     3   (0)|     10 |00:00:00.01 |       6 |      0 |
|   8 |      PARTITION RANGE ALL               |                             |      1 | 36211 | 2827   (1)|    217K|00:01:28.01 |     264K|    220K|
|   9 |       TABLE ACCESS BY LOCAL INDEX ROWID| FACT_LINE_ITEM              |      6 | 36211 | 2827   (1)|    217K|00:01:33.85 |     264K|    220K|
| 10 |        BITMAP CONVERSION TO ROWIDS     |                             |      6 |        |            |    217K|00:00:07.09 |   46980 |   3292 |
| 11 |         BITMAP AND                     |                             |      6 |        |            |     69 |00:00:08.33 |   46980 |   3292 |
| 12 |          BITMAP MERGE                  |                             |      6 |        |            |    193 |00:00:02.09 |    2408 |   1795 |
| 13 |           BITMAP KEY ITERATION         |                             |      6 |        |            |   4330 |00:00:04.66 |    2408 |   1795 |
| 14 |            BUFFER SORT                 |                             |      6 |        |            |     60 |00:00:00.01 |       6 |      0 |
|* 15 |             TABLE ACCESS FULL          | DIM_PART                    |      1 |     10 |     3   (0)|     10 |00:00:00.01 |       6 |      0 |
|* 16 |            BITMAP INDEX RANGE SCAN     | FACT_LI__P_PART_DIM_KEY_BIX |     60 |        |            |   4330 |00:00:02.11 |    2402 |   1795 |
|* 17 |          BITMAP INDEX SINGLE VALUE     | FACT_LI__P_PART_MFGR_BJX    |      6 |        |            |   1747 |00:00:06.65 |     890 |    888 |
| 18 |          BITMAP MERGE                  |                             |      6 |        |            |    169 |00:00:02.78 |   16695 |    237 |
| 19 |           BITMAP KEY ITERATION         |                             |      6 |        |            |   5460 |00:00:01.56 |   16695 |    237 |
| 20 |            BUFFER SORT                 |                             |      6 |        |            |   5460 |00:00:00.02 |     316 |      0 |
|* 21 |             TABLE ACCESS FULL          | DIM_CUSTOMER                |      1 |    910 |   102   (0)|    910 |00:00:00.01 |     316 |      0 |
|* 22 |            BITMAP INDEX RANGE SCAN     | FACT_LI__P_CUST_DIM_KEY_BIX |   5460 |        |            |   5460 |00:00:02.07 |   16379 |    237 |
| 23 |          BITMAP MERGE                  |                             |      6 |        |            |    170 |00:00:03.65 |   26987 |    372 |
| 24 |           BITMAP KEY ITERATION         |                             |      6 |        |            |   6672 |00:00:02.23 |   26987 |    372 |
| 25 |            BUFFER SORT                 |                             |      6 |        |            |   6672 |00:00:00.01 |     316 |      0 |
|* 26 |             TABLE ACCESS FULL          | DIM_SUPPLIER                |      1 |   1112 |   102   (0)|   1112 |00:00:00.01 |     316 |      0 |
|* 27 |            BITMAP INDEX RANGE SCAN     | FACT_LI__S_SUPP_DIM_KEY_BIX |   6672 |        |            |   6672 |00:00:02.74 |   26671 |    372 |
The Question:
Why is the Star Transformation using both indexes FACT_LI__P_PART_DIM_KEY_BIX and FACT_LI__P_PART_MFGR_BJX for the same dimension criteria (dp.mfgr = 10)?? The introduction of the additional Bitmap Join Index actually make Oracle to do the work twice !!!
Anybody, any idea ?!?

Dom, here it is the plan with the predicates:
| Id | Operation                              | Name                        | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers | Reads |
|   0 | SELECT STATEMENT                       |                             |      1 |        | 3638 (100)|      1 |00:06:41.17 |     445K|    236K|
|   1 | HASH GROUP BY                         |                             |      1 |      2 | 3638   (1)|      1 |00:06:41.17 |     445K|    236K|
|* 2 |   HASH JOIN                            |                             |      1 | 33242 | 3429   (1)|    217K|00:08:18.02 |     445K|    236K|
|* 3 |    TABLE ACCESS FULL                   | DIM_SUPPLIER                |      1 |   1112 |   102   (0)|   1112 |00:00:00.03 |     319 |    313 |
|* 4 |    HASH JOIN                           |                             |      1 | 33245 | 3326   (1)|    217K|00:08:17.47 |     445K|    236K|
|* 5 |     TABLE ACCESS FULL                  | DIM_CUSTOMER                |      1 |    910 |   102   (0)|    910 |00:00:00.01 |     319 |    313 |
|* 6 |     HASH JOIN                          |                             |      1 | 33248 | 3223   (1)|    217K|00:08:16.63 |     445K|    236K|
|* 7 |      TABLE ACCESS FULL                 | DIM_PART                    |      1 |     10 |     3   (0)|     10 |00:00:00.01 |       6 |      0 |
|   8 |      PARTITION RANGE ALL               |                             |      1 | 36211 | 3219   (1)|    217K|00:08:16.30 |     445K|    236K|
|   9 |       TABLE ACCESS BY LOCAL INDEX ROWID| FACT_LINE_ITEM              |      6 | 36211 | 3219   (1)|    217K|00:08:40.89 |     445K|    236K|
| 10 |        BITMAP CONVERSION TO ROWIDS     |                             |      6 |        |            |    217K|00:00:32.00 |   46919 | 19331 |
| 11 |         BITMAP AND                     |                             |      6 |        |            |     69 |00:00:34.50 |   46919 | 19331 |
| 12 |          BITMAP MERGE                  |                             |      6 |        |            |    193 |00:00:00.58 |    2353 |      1 |
| 13 |           BITMAP KEY ITERATION         |                             |      6 |        |            |   4330 |00:00:00.10 |    2353 |      1 |
| 14 |            BUFFER SORT                 |                             |      6 |        |            |     60 |00:00:00.01 |       6 |      0 |
|* 15 |             TABLE ACCESS FULL          | DIM_PART                    |      1 |     10 |     3   (0)|     10 |00:00:00.01 |       6 |      0 |
|* 16 |            BITMAP INDEX RANGE SCAN     | FACT_LI__P_PART_DIM_KEY_BIX |     60 |        |            |   4330 |00:00:00.07 |    2347 |      1 |
|* 17 |          BITMAP INDEX SINGLE VALUE     | FACT_LI__P_PART_MFGR_BJX    |      6 |        |            |   1747 |00:01:23.64 |     882 |    565 |
| 18 |          BITMAP MERGE                  |                             |      6 |        |            |    169 |00:00:09.14 |   16697 |   7628 |
| 19 |           BITMAP KEY ITERATION         |                             |      6 |        |            |   5460 |00:00:02.19 |   16697 |   7628 |
| 20 |            BUFFER SORT                 |                             |      6 |        |            |   5460 |00:00:00.01 |     316 |      0 |
|* 21 |             TABLE ACCESS FULL          | DIM_CUSTOMER                |      1 |    910 |   102   (0)|    910 |00:00:00.01 |     316 |      0 |
|* 22 |            BITMAP INDEX RANGE SCAN     | FACT_LI__P_CUST_DIM_KEY_BIX |   5460 |        |            |   5460 |00:00:08.78 |   16381 |   7628 |
| 23 |          BITMAP MERGE                  |                             |      6 |        |            |    170 |00:00:21.46 |   26987 | 11137 |
| 24 |           BITMAP KEY ITERATION         |                             |      6 |        |            |   6672 |00:00:10.29 |   26987 | 11137 |
| 25 |            BUFFER SORT                 |                             |      6 |        |            |   6672 |00:00:00.01 |     316 |      0 |
|* 26 |             TABLE ACCESS FULL          | DIM_SUPPLIER                |      1 |   1112 |   102   (0)|   1112 |00:00:00.01 |     316 |      0 |
|* 27 |            BITMAP INDEX RANGE SCAN     | FACT_LI__S_SUPP_DIM_KEY_BIX |   6672 |        |            |   6672 |00:00:20.94 |   26671 | 11137 |
Predicate Information (identified by operation id):
   2 - access("F"."SUPPLIER_DK"="DS"."DK")
   3 - filter("DS"."REGION_NAME"='REGION #1')
   4 - access("F"."CUSTOMER_DK"="DC"."DK")
   5 - filter("DC"."REGION_NAME"='REGION #1')
   6 - access("F"."PART_DK"="DP"."DK")
   7 - filter("DP"."MFGR"=10)
15 - filter("DP"."MFGR"=10)
16 - access("F"."PART_DK"="DP"."DK")
17 - access("F"."SYS_NC00017$"=10)
21 - filter("DC"."REGION_NAME"='REGION #1')
22 - access("F"."CUSTOMER_DK"="DC"."DK")
26 - filter("DS"."REGION_NAME"='REGION #1')
27 - access("F"."SUPPLIER_DK"="DS"."DK")
Note
   - star transformation used for this statement

Displaying different levels of the same hierarchy in the different columns

I have a parent child hierarchy and wish to display different levels of the hierarchy in different columns of the resultset eg
level 1 level 2
Europe UK
Europe France
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N America Canada
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{} on 0,
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Hi,
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crossjoin({[Sales Territory].[Sales Territory Group].[Sales Territory Group].members},{[Sales Territory].[Sales Territory Country].[Sales Territory Country].members}) on 1
from [Adventure Works]
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Reseller Sales Amount
Europe
France
$4,607,537.94
Europe
Germany
$1,983,988.04
Europe
United Kingdom
$4,279,008.83
NA
NA
(null)
North America
Canada
$14,377,925.60
North America
United States
$53,607,801.21
Pacific
Australia
$1,594,335.38
Please remember to mark the replies as answers if they help and unmark them if they provide no help.

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cordialy
Attachments:
projet.zip ‏13 KB

CrisSTine01 wrote:
hello, I want to use two indicators for the same variable in two different VIs running at the same time
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Using different levels of the same dimension

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