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• Calculate Matrix Rank using scipy

  - by Hooked

  I'd like to calculate the mathematical rank of a matrix using scipy. The most obvious function numpy.rank calculates the dimension of an array (ie. scalars have dimension 0, vectors 1, matrices 2, etc...). I am aware that the numpy.linalg.lstsq module has this capability, but I was wondering if such a fundamental operation is built into the matrix class somewhere. Here is an explicit example: from numpy import matrix, rank A = matrix([[1,3,7],[2,8,3],[7,8,1]]) print rank(A) This gives 2 the dimension, where I'm looking for an answer of 3.

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• visualize irregular data in vtk

  - by aaron berry

  I have an irregular data, x dimension - 384, y dimension - 256 and z dimension 64. Now these coordinates are stored in 3 separate binary files and i have a data file having a data value for these points. I want to know, how can i represent such data to be easily visualized in vtk. Till now we were using AVS which has fld files, which can read such data easily. I dont know how to do it in vtk. Would appreciate any pointers in this direction.

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• Fact table with multiple facts

  - by Jeff Meatball Yang

  I have a dimension (SiteItem) has two important facts: perUserClicks perBrowserClicks however, within this dimension, I have groups of dimensions based on an attribute column (let's call the groups AboveFoldItems, LeftNavItems, OnTheFlyItems, etc.) each have more facts that are specific to that group: AboveFoldItems: eyeTime, loadTime LeftNavItems: mouseOverTime OnTheFlyItems: doesn't have any extra, but may in the future Is the following fact table schema ok? DateKey SessionKey SiteItemKey perUserClicks perBrowserClicks eyeTime loadTime mouseOverTime It seems a little wasteful since only some columns pertain to some dimension keys (the irrelevant facts are left NULL). But... this seems like it would be a common problem, so there should be a common solution for this, right?

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• How to create a conditional measure in SQL Server 2008 Analysis services

  - by Jonathan

  Hi there I am not sure if the title has the correct terms, I have a developer and am very new to cubes. I have a cube which has data associated to materials that are broken down into chemical compounds. For example a rock material has 10% of this chemical and 10% of that chemical, etc. Samples are taken daily and sample is a dimension with date, etc. So, the measure needs to average by the sample dimension but needs to sum across the chemical compound dimension (To add up to 100% for example). Is this at all possible?

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• Add RESTful Action

  - by Drew Rush

  The source of my information is section 2.9 here: [http://guides.rubyonrails.org/routing.html#connecting-urls-to-code][1] What I'm trying to do is add a custom action "search" and corresponding view. So, as it says to do in the documentation, I've added this code in my config/routes.rb file: resources :dimensions do collection do get "search" end end I've also defined in the dimensions_controller file: def search @dimensions = Dimension.all respond_to do |format| format.html # search.html.erb format.json { render json: @dimensions } end end I then stopped and restarted the rails server, but when I navigate to /dimensions/home, I'm still getting this error message: Couldn't find Dimension with id=search Also showing that my parameter is: {"id"=>"search"} So am I just missing another bit of code that gives the instruction to interpret /dimension/search as a collection action as opposed to the show action? Thanks for your time.

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• 3d transformation of game world keeping gameplay 2d - COCOS2D 2.0

  - by samfisher

  Using: COCOS2D + iOS. I want to rotate the game world, may be loading another .tmx file for another dimensions when user want to switch dimension. the effect what I am looking for is something like this:CLICK HERE What I have thought of till now: rotating CCCamera will be mandatory. Question: How will I have the other part of the level in place while the camera rotates/rotating? I can load a CCSprite and rotate it accordingly to the 3rd dimension. phew..!! Question: When the camera and world is rotated, will the player controls work properly.. I think not...? I think a better option would be to checkout with COCOS3D... there I could implement 3d world... right?? Question: Not sure how well 2d dynamics will work there as I want to user Box2d as physics engine.. could anyone provide suggestions? Regards, Sam

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• SSAS: Utility to check you have the correct data types and sizes in your cube definition

  - by DrJohn

  This blog describes a tool I developed which allows you to compare the data types and data sizes found in the cube’s data source view with the data types/sizes of the corresponding dimensional attribute.  Why is this important?  Well when creating named queries in a cube’s data source view, it is often necessary to use the SQL CAST or CONVERT operation to change the data type to something more appropriate for SSAS.  This is particularly important when your cube is based on an Oracle data source or using custom SQL queries rather than views in the relational database.   The problem with BIDS is that if you change the underlying SQL query, then the size of the data type in the dimension does not update automatically.  This then causes problems during deployment whereby processing the dimension fails because the data in the relational database is wider than that allowed by the dimensional attribute. In particular, if you use some string manipulation functions provided by SQL Server or Oracle in your queries, you may find that the 10 character string you expect suddenly turns into an 8,000 character monster.  For example, the SQL Server function REPLACE returns column with a width of 8,000 characters.  So if you use this function in the named query in your DSV, you will get a column width of 8,000 characters.  Although the Oracle REPLACE function is far more intelligent, the generated column size could still be way bigger than the maximum length of the data actually in the field. Now this may not be a problem when prototyping, but in your production cubes you really should clean up this kind of thing as these massive strings will add to processing times and storage space. Similarly, you do not want to forget to change the size of the dimension attribute if your database columns increase in size. Introducing CheckCubeDataTypes Utiltity The CheckCubeDataTypes application extracts all the data types and data sizes for all attributes in the cube and compares them to the data types and data sizes in the cube’s data source view.  It then generates an Excel CSV file which contains all this metadata along with a flag indicating if there is a mismatch between the DSV and the dimensional attribute.  Note that the app not only checks all the attribute keys but also the name and value columns for each attribute. Another benefit of having the metadata held in a CSV text file format is that you can place the file under source code control.  This allows you to compare the metadata of the previous cube release with your new release to highlight problems introduced by new development. You can download the C# source code from here: CheckCubeDataTypes.zip A typical example of the output Excel CSV file is shown below - note that the last column shows a data size mismatch by TRUE appearing in the column

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• Distinct Count of Customers in a SCD Type 2 in #DAX

  - by Marco Russo (SQLBI)

  If you have a Slowly Changing Dimension (SCD) Type 2 for your customer and you want to calculate the number of distinct customers that bought a product, you cannot use the simple formula: Customers := DISTINCTCOUNT( FactTable[Customer Id] ) ) because it would return the number of distinct versions of customers. What you really want to do is to calculate the number of distinct application keys of the customers, that could be a lower number than the number you’ve got with the previous formula. Assuming that a Customer Code column in the Customers dimension contains the application key, you should use the following DAX formula: Customers := COUNTROWS( SUMMARIZE( FactTable, Customers[Customer Code] ) ) Be careful: only the version above is really fast, because it is solved by xVelocity (formerly known as VertiPaq) engine. Other formulas involving nested calculations might be more complex and move computation to the formula engine, resulting in slower query. This is absolutely an interesting pattern and I have to say it’s a killer feature. Try to do the same in Multidimensional…

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• Function Folding in #PowerQuery

  - by Darren Gosbell

  Originally posted on: http://geekswithblogs.net/darrengosbell/archive/2014/05/16/function-folding-in-powerquery.aspxLooking at a typical Power Query query you will noticed that it's made up of a number of small steps. As an example take a look at the query I did in my previous post about joining a fact table to a slowly changing dimension. It was roughly built up of the following steps: Get all records from the fact table Get all records from the dimension table do an outer join between these two tables on the business key (resulting in an increase in the row count as there are multiple records in the dimension table for each business key) Filter out the excess rows introduced in step 3 remove extra columns that are not required in the final result set. If Power Query was to execute a query like this literally, following the same steps in the same order it would not be overly efficient. Particularly if your two source tables were quite large. However Power Query has a feature called function folding where it can take a number of these small steps and push them down to the data source. The degree of function folding that can be performed depends on the data source, As you might expect, relational data sources like SQL Server, Oracle and Teradata support folding, but so do some of the other sources like OData, Exchange and Active Directory. To explore how this works I took the data from my previous post and loaded it into a SQL database. Then I converted my Power Query expression to source it's data from that database. Below is the resulting Power Query which I edited by hand so that the whole thing can be shown in a single expression: let     SqlSource = Sql.Database("localhost", "PowerQueryTest"),     BU = SqlSource{[Schema="dbo",Item="BU"]}[Data],     Fact = SqlSource{[Schema="dbo",Item="fact"]}[Data],     Source = Table.NestedJoin(Fact,{"BU_Code"},BU,{"BU_Code"},"NewColumn"),     LeftJoin = Table.ExpandTableColumn(Source, "NewColumn"                                   , {"BU_Key", "StartDate", "EndDate"}                                   , {"BU_Key", "StartDate", "EndDate"}),     BetweenFilter = Table.SelectRows(LeftJoin, each (([Date] >= [StartDate]) and ([Date] <= [EndDate])) ),     RemovedColumns = Table.RemoveColumns(BetweenFilter,{"StartDate", "EndDate"}) in     RemovedColumns If the above query was run step by step in a literal fashion you would expect it to run two queries against the SQL database doing "SELECT * …" from both tables. However a profiler trace shows just the following single SQL query: select [_].[BU_Code],     [_].[Date],     [_].[Amount],     [_].[BU_Key] from (     select [$Outer].[BU_Code],         [$Outer].[Date],         [$Outer].[Amount],         [$Inner].[BU_Key],         [$Inner].[StartDate],         [$Inner].[EndDate]     from [dbo].[fact] as [$Outer]     left outer join     (         select [_].[BU_Key] as [BU_Key],             [_].[BU_Code] as [BU_Code2],             [_].[BU_Name] as [BU_Name],             [_].[StartDate] as [StartDate],             [_].[EndDate] as [EndDate]         from [dbo].[BU] as [_]     ) as [$Inner] on ([$Outer].[BU_Code] = [$Inner].[BU_Code2] or [$Outer].[BU_Code] is null and [$Inner].[BU_Code2] is null) ) as [_] where [_].[Date] >= [_].[StartDate] and [_].[Date] <= [_].[EndDate] The resulting query is a little strange, you can probably tell that it was generated programmatically. But if you look closely you'll notice that every single part of the Power Query formula has been pushed down to SQL Server. Power Query itself ends up just constructing the query and passing the results back to Excel, it does not do any of the data transformation steps itself. So now you can feel a bit more comfortable showing Power Query to your less technical Colleagues knowing that the tool will do it's best fold all the  small steps in Power Query down the most efficient query that it can against the source systems.

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• Working with Analytic Workflow Manager (AWM) - Part 8 Cube Metadata Analysis

  - by Mohan Ramanuja

  CUBE SIZEselect dbal.owner||'.'||substr(dbal.table_name,4) awname, sum(dbas.bytes)/1024/1024 as mb, dbas.tablespace_name from dba_lobs dbal, dba_segments dbas where dbal.column_name = 'AWLOB' and dbal.segment_name = dbas.segment_name group by dbal.owner, dbal.table_name, dbas.tablespace_name order by dbal.owner, dbal.table_name SESSION RESOURCES select vses.username||':'||vsst.sid username, vstt.name, max(vsst.value) valuefrom v$sesstat vsst, v$statname vstt, v$session vseswhere vstt.statistic# = vsst.statistic# and vsst.sid = vses.sid andVSES.USERNAME LIKE ('ATTRIBDW_OWN') ANDvstt.name in ('session pga memory', 'session pga memory max', 'session uga memory','session uga memory max', 'session cursor cache count', 'session cursor cache hits', 'session stored procedure space', 'opened cursors current', 'opened cursors cumulative') andvses.username is not null group by vsst.sid, vses.username, vstt.name order by vsst.sid, vses.username, vstt.name OLAP PGA USE select 'OLAP Pages Occupying: '|| round((((select sum(nvl(pool_size,1)) from v$aw_calc)) / (select value from v$pgastat where name = 'total PGA inuse')),2)*100||'%' info from dual union select 'Total PGA Inuse Size: '||value/1024||' KB' info from v$pgastat where name = 'total PGA inuse' union select 'Total OLAP Page Size: '|| round(sum(nvl(pool_size,1))/1024,0)||' KB' info from v$aw_calc order by info desc OLAP PGA USAGE PER USER select vs.username, vs.sid, round(pga_used_mem/1024/1024,2)||' MB' pga_used, round(pga_max_mem/1024/1024,2)||' MB' pga_max, round(pool_size/1024/1024,2)||' MB' olap_pp, round(100*(pool_hits-pool_misses)/pool_hits,2) || '%' olap_ratio from v$process vp, v$session vs, v$aw_calc va where session_id=vs.sid and addr = paddr CUBE LOADING SCRIPT REM The 'set define off' statement is needed only if running this script through SQLPlus.REM If you are using another tool to run this script, the line below may be commented out.set define offBEGIN  DBMS_CUBE.BUILD(    'VALIDATE  ATTRIBDW_OWN.CURRENCY USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNT USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.DATEDIM USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.CUSIP USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNTRETURN',    'CCCCC', -- refresh methodfalse, -- refresh after errors    0, -- parallelismtrue, -- atomic refreshtrue, -- automatic orderfalse); -- add dimensionsEND;/BEGIN  DBMS_CUBE.BUILD(    '  ATTRIBDW_OWN.CURRENCY USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNT USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.DATEDIM USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.CUSIP USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNTRETURN',    'CCCCC', -- refresh methodfalse, -- refresh after errors    0, -- parallelismtrue, -- atomic refreshtrue, -- automatic orderfalse); -- add dimensionsEND;/ VISUALIZATION OBJECT - AW$ATTRIBDW_OWN  CREATE TABLE "ATTRIBDW_OWN"."AW$ATTRIBDW_OWN"        (            "PS#"    NUMBER(10,0),            "GEN#"   NUMBER(10,0),            "EXTNUM" NUMBER(8,0),            "AWLOB" BLOB,            "OBJNAME"  VARCHAR2(256 BYTE),            "PARTNAME" VARCHAR2(256 BYTE)        )        PCTFREE 10 PCTUSED 40 INITRANS 4 MAXTRANS 255 STORAGE        (            BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "AWLOB"        )        STORE AS SECUREFILE        (            TABLESPACE "ATTRIBDW_DATA" DISABLE STORAGE IN ROW CHUNK 8192 RETENTION MIN 1 CACHE NOCOMPRESS KEEP_DUPLICATES STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        )        PARTITION BY RANGE        (            "GEN#"        )        SUBPARTITION BY HASH        (            "PS#",            "EXTNUM"        )        SUBPARTITIONS 8        (            PARTITION "PTN1" VALUES LESS THAN (1) PCTFREE 10 PCTUSED 40 INITRANS 4 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" LOB ("AWLOB") STORE AS SECUREFILE ( TABLESPACE "ATTRIBDW_DATA" DISABLE STORAGE IN ROW CHUNK 8192 RETENTION MIN 1 CACHE READS LOGGING NOCOMPRESS KEEP_DUPLICATES STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)) ( SUBPARTITION "SYS_SUBP661" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP662" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP663" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP664" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP665" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION            "SYS_SUBP666" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP667" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP668" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" ) ,            PARTITION "PTNN" VALUES LESS THAN (MAXVALUE) PCTFREE 10 PCTUSED 40 INITRANS 4 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" LOB ("AWLOB") STORE AS SECUREFILE ( TABLESPACE "ATTRIBDW_DATA" DISABLE STORAGE IN ROW CHUNK 8192 RETENTION MIN 1 CACHE NOCOMPRESS KEEP_DUPLICATES STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)) ( SUBPARTITION "SYS_SUBP669" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP670" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP671" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP672" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP673" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION            "SYS_SUBP674" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP675" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP676" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" )        ) ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."ATTRIBDW_OWN_I$" ON "ATTRIBDW_OWN"."AW$ATTRIBDW_OWN"    (        "PS#", "GEN#", "EXTNUM"    )    PCTFREE 10 INITRANS 4 MAXTRANS 255 COMPUTE STATISTICS STORAGE    (        INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT    )    TABLESPACE "ATTRIBDW_DATA" ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000406980C00004$$" ON "ATTRIBDW_OWN"."AW$ATTRIBDW_OWN"    (        PCTFREE 10 INITRANS 1 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" LOCAL (PARTITION "SYS_IL_P711" PCTFREE 10 INITRANS 1 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) ( SUBPARTITION "SYS_IL_SUBP695" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP696" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP697" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP698" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP699" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP700" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP701" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP702" TABLESPACE "ATTRIBDW_DATA" ) , PARTITION "SYS_IL_P712" PCTFREE 10 INITRANS 1 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) ( SUBPARTITION "SYS_IL_SUBP703" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP704" TABLESPACE        "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP705" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP706" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP707" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP708" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP709" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP710" TABLESPACE "ATTRIBDW_DATA" ) ) PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE BUILD LOG  CREATE TABLE "ATTRIBDW_OWN"."CUBE_BUILD_LOG"        (            "BUILD_ID"          NUMBER,            "SLAVE_NUMBER"      NUMBER,            "STATUS"            VARCHAR2(10 BYTE),            "COMMAND"           VARCHAR2(25 BYTE),            "BUILD_OBJECT"      VARCHAR2(30 BYTE),            "BUILD_OBJECT_TYPE" VARCHAR2(10 BYTE),            "OUTPUT" CLOB,            "AW"            VARCHAR2(30 BYTE),            "OWNER"         VARCHAR2(30 BYTE),            "PARTITION"     VARCHAR2(50 BYTE),            "SCHEDULER_JOB" VARCHAR2(100 BYTE),            "TIME" TIMESTAMP (6)WITH TIME ZONE,        "BUILD_SCRIPT" CLOB,        "BUILD_TYPE"            VARCHAR2(22 BYTE),        "COMMAND_DEPTH"         NUMBER(2,0),        "BUILD_SUB_OBJECT"      VARCHAR2(30 BYTE),        "REFRESH_METHOD"        VARCHAR2(1 BYTE),        "SEQ_NUMBER"            NUMBER,        "COMMAND_NUMBER"        NUMBER,        "IN_BRANCH"             NUMBER(1,0),        "COMMAND_STATUS_NUMBER" NUMBER,        "BUILD_NAME"            VARCHAR2(100 BYTE)        )        SEGMENT CREATION IMMEDIATE PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING STORAGE        (            INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "OUTPUT"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        )        LOB        (            "BUILD_SCRIPT"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        ) ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407294C00013$$" ON "ATTRIBDW_OWN"."CUBE_BUILD_LOG"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407294C00007$$" ON "ATTRIBDW_OWN"."CUBE_BUILD_LOG" ( PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE DIMENSION COMPILE  CREATE TABLE "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"        (            "ID"               NUMBER,            "SEQ_NUMBER"       NUMBER,            "ERROR#"           NUMBER(8,0) NOT NULL ENABLE,            "ERROR_MESSAGE"    VARCHAR2(2000 BYTE),            "DIMENSION"        VARCHAR2(100 BYTE),            "DIMENSION_MEMBER" VARCHAR2(100 BYTE),            "MEMBER_ANCESTOR"  VARCHAR2(100 BYTE),            "HIERARCHY1"       VARCHAR2(100 BYTE),            "HIERARCHY2"       VARCHAR2(100 BYTE),            "ERROR_CONTEXT" CLOB        )        SEGMENT CREATION DEFERRED PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING TABLESPACE "ATTRIBDW_DATA" LOB        (            "ERROR_CONTEXT"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING        ) ;COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ID"IS    'Current operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."SEQ_NUMBER"IS    'Cube build log sequence number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ERROR#"IS    'Error number being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ERROR_MESSAGE"IS    'Error text being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."DIMENSION"IS    'Name of dimension being compiled';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."DIMENSION_MEMBER"IS    'Problem dimension member';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."MEMBER_ANCESTOR"IS    'Problem dimension member''s parent';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."HIERARCHY1"IS    'First hierarchy involved in error';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."HIERARCHY2"IS    'Second hierarchy involved in error';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ERROR_CONTEXT"IS    'Extra information for error';    COMMENT ON TABLE "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"IS    'Cube dimension compile log';CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407307C00010$$" ON "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE( INITIAL 1048576 NEXT 1048576 MAXEXTENTS 2147483645) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE OPERATING LOG  CREATE TABLE "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"        (            "INST_ID"    NUMBER NOT NULL ENABLE,            "SID"        NUMBER NOT NULL ENABLE,            "SERIAL#"    NUMBER NOT NULL ENABLE,            "USER#"      NUMBER NOT NULL ENABLE,            "SQL_ID"     VARCHAR2(13 BYTE),            "JOB"        NUMBER,            "ID"         NUMBER,            "PARENT_ID"  NUMBER,            "SEQ_NUMBER" NUMBER,            "TIME" TIMESTAMP (6)WITH TIME ZONE NOT NULL ENABLE,        "LOG_LEVEL"    NUMBER(4,0) NOT NULL ENABLE,        "DEPTH"        NUMBER(4,0),        "OPERATION"    VARCHAR2(15 BYTE) NOT NULL ENABLE,        "SUBOPERATION" VARCHAR2(20 BYTE),        "STATUS"       VARCHAR2(10 BYTE) NOT NULL ENABLE,        "NAME"         VARCHAR2(20 BYTE) NOT NULL ENABLE,        "VALUE"        VARCHAR2(4000 BYTE),        "DETAILS" CLOB        )        SEGMENT CREATION IMMEDIATE PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING STORAGE        (            INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "DETAILS"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        ) ;COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."INST_ID"IS    'Instance ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SID"IS    'Session ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SERIAL#"IS    'Session serial#';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."USER#"IS    'User ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SQL_ID"IS    'Executing SQL statement ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."JOB"IS    'Identifier of job';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."ID"IS    'Current operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."PARENT_ID"IS    'Parent operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SEQ_NUMBER"IS    'Cube build log sequence number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."TIME"IS    'Time of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."LOG_LEVEL"IS    'Verbosity level of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."DEPTH"IS    'Nesting depth of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."OPERATION"IS    'Current operation';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SUBOPERATION"IS    'Current suboperation';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."STATUS"IS    'Status of current operation';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."NAME"IS    'Name of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."VALUE"IS    'Value of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."DETAILS"IS    'Extra information for record';    COMMENT ON TABLE "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"IS    'Cube operations log';CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407301C00018$$" ON "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE REJECTED RECORDS CREATE TABLE "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"        (            "ID"            NUMBER,            "SEQ_NUMBER"    NUMBER,            "ERROR#"        NUMBER(8,0) NOT NULL ENABLE,            "ERROR_MESSAGE" VARCHAR2(2000 BYTE),            "RECORD#"       NUMBER(38,0),            "SOURCE_ROW" ROWID,            "REJECTED_RECORD" CLOB        )        SEGMENT CREATION IMMEDIATE PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING STORAGE        (            INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "REJECTED_RECORD"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        ) ;COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."ID"IS    'Current operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."SEQ_NUMBER"IS    'Cube build log sequence number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."ERROR#"IS    'Error number being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."ERROR_MESSAGE"IS    'Error text being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."RECORD#"IS    'Rejected record number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."SOURCE_ROW"IS    'Rejected record''s ROWID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."REJECTED_RECORD"IS    'Rejected record copy';    COMMENT ON TABLE "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"IS    'Cube rejected records log';CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407304C00007$$" ON "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ;

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• Create a fast algorithm for a "weighted" median

  - by Hameer Abbasi

  Suppose we have a set S with k elements of 2-dimensional vectors, (x, n). What would be the most efficient algorithm to calculate the median of the weighted set? By "weighted set", I mean that the number x has a weight n. Here is an example (inefficient due to sorting) algorithm, where Sx is the x-part, and Sn is the n-part. Assume that all co-ordinate pairs are already arranged in Sx, with the respective changes also being done in Sn, and the sum of n is sumN: sum <= 0; i<= 0 while(sum < sumN) sum <= sum + Sn(i) ++i if(sum > sumN/2) return Sx(i) else return (Sx(i)*Sn(i) + Sx(i+1)*Sn(i+1))/(Sn(i) + Sn(i+1)) EDIT: Would this hold in two or more dimensions, if we were to calculate the median first in one dimension, then in another, with n being the sum along that dimension in the second pass?

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• Show events AND pageviews in Google Analytics

  - by supertrue

  Each page on my site contains a file, and I have Google Analytics set up to track file download events. I would like to see what fraction of users who visit Page X download Page X's file. I can view number of events by page by clicking on Content » Events » Pages. But I can't figure out how to see both events and pageviews (or visits) at the same time. Visits and pageviews are not available in the Secondary dimension dropdown from the Events list, and Events are not available as a Secondary dimension in the regular traffic listing (Content » Site Content » All Pages). I want something like this: Page Pageviews Events 1. /section/mypage 1,000 123 2. /category/anotherpage 867 41 3. /about/download 88 7 Is there a way to get this in Google Analytics?—to view events and pageviews, by page, at the same time?

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• Performance using T-SQL PIVOT vs SSIS PIVOT Transformation Component.

  - by Nev_Rahd

  Hi I am in process of building Dimension from EDW (source), wherein I need to pivot columns of source to load Dimension. Currently most of the pivoting stuff am doing is by using T-SQL PIVOT which further get used in my SSIS package to merge with Dim table This pivoting can also be achieved by SSIS PIVOT Transformation component. In regards to Performance which approach would be the best? Thanks

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• Java JFrame method pack() problem

  - by Oliver

  Hi, I have a frame with 4 JPanels and 1 JScrollPane, the 4 panels are in border layout north, east, south, west and the scrollpane in the center. I have been trying to get the pack method for a frame fuctioning but when run you just get the title bar of the window. Any Ideas? Thank you in advance. JFrame conFrame; JPanel panel1; JPanel panel2; JPanel panel3; JPanel panel4; JScrollPane listPane; JList list; Object namesAr[]; ... ... ... namesAr= namesA.toArray(); list = new JList(namesAr); list.setSelectionMode(ListSelectionModel.SINGLE_SELECTION); list.setLayoutOrientation(JList.HORIZONTAL_WRAP); list.setVisibleRowCount(-3); list.addListSelectionListener(this); listPane = new JScrollPane(list); panel1 = new JPanel(); panel2 = new JPanel(); panel3 = new JPanel(); panel4 = new JPanel(); conFrame.setLayout(new BorderLayout()); panel1.setPreferredSize(new Dimension(100, 100)); panel2.setPreferredSize(new Dimension(100, 100)); panel3.setPreferredSize(new Dimension(100, 100)); panel4.setPreferredSize(new Dimension(100, 100)); panel1.setBackground(Color.red); panel2.setBackground(Color.red); panel3.setBackground(Color.red); panel4.setBackground(Color.red); conFrame.pack(); conFrame.add(panel1, BorderLayout.NORTH); conFrame.add(panel2, BorderLayout.EAST); conFrame.add(panel3, BorderLayout.SOUTH); conFrame.add(panel4, BorderLayout.WEST); conFrame.add(listPane, BorderLayout.CENTER); conFrame.setVisible(true);

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• Cube project doesn't work because of permissions

  - by sms

  I'm doing "Multidimensional Project" with MS SQL Server 2012 (Server Data Tools - Visual Studio 2010 Shell). I can't run (debug) it. If the data source's impersonation information is set to "use the service account", this error occures: Error 2 Internal error: The operation terminated unsuccessfully. 0 0 Error 3 OLE DB error: OLE DB or ODBC error: Login failed for user 'NT Service\MSSQLServerOLAPService'.; 28000. 0 0 Error 4 Errors in the high-level relational engine. A connection could not be made to the data source with the DataSourceID of 'Data Warehouse', Name of 'Data Warehouse'. 0 0 Error 5 Errors in the OLAP storage engine: An error occurred while the dimension, with the ID of 'Items', Name of 'Items' was being processed. 0 0 Error 6 Errors in the OLAP storage engine: An error occurred while the 'Id' attribute of the 'Items' dimension from the 'Warehouse_MultidimensionalProject_Cube' database was being processed. 0 0 Error 7 Server: The current operation was cancelled because another operation in the transaction failed. 0 0 I guessed that this account has no premissions but (1) I coudn't even add this account (it seems that it doesn't exist) and (2) how is that even possible for it to not have built-it poremissions? When I'm setting impersonation to "use the credentials of current user" (which is the owner of the data source, btw.), another error occures: Error 2 Internal error: The operation terminated unsuccessfully. 0 0 Error 3 The datasource, 'Data Warehouse', contains an ImpersonationMode that is not supported for processing operations. 0 0 Error 4 Errors in the high-level relational engine. A connection could not be made to the data source with the DataSourceID of 'Data Warehouse', Name of 'Data Warehouse'. 0 0 Error 5 Errors in the OLAP storage engine: An error occurred while the dimension, with the ID of 'Items', Name of 'Items' was being processed. 0 0 Error 6 Errors in the OLAP storage engine: An error occurred while the 'Id' attribute of the 'Items' dimension from the 'Warehouse_MultidimensionalProject_Cube' database was being processed. 0 0 Error 7 Server: The current operation was cancelled because another operation in the transaction failed. 0 0 Any help?

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• How can I find the maximum or minimum of a multi-dimensional matrix in Matlab?

  - by Nathan Fellman

  I have a 4D array of measurements in Matlab. Each dimension represents a different parameter for the measurement. I want to find the maximum and minimum value and the index (i.e. which parameter) of each. What's the best way to do it? I figure I can take the max of the max of the max in each dimension, but that seems like a kludge.

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• How do i pack multiple rectangles in a 2d box tetris style

  - by mglmnc

  I have a number of rectangles of various widths and heights. I have a larger rectangular platform to put them on. I want to pack them on one side of the platform so they spread in the lengthwise (X) dimension but keep the widthwise (Y) dimension to a minimal. That is to place them like a tetris game. There can be no overlaps but there can be gaps. Is there an algorithm out there to do this?

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• UIImage resize and crop to fit

  - by Amit Hagin

  I read a lot, also here, but couldn't find a simple way to do it: In objective c - I have a big UIImage and a small UIImageView. I want to programmatically shrink the content of a UIImage just enough to fit the smaller dimension within the UIImageView. The larger dimension will be cropped, and the result will be the maximum I can get from an image without changing the proportion. can you please help me?

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• How can I find the absolute maximum or minimum of a multi-dimensional matrix in Matlab?

  - by Nathan Fellman

  I have a 4D array of measurements in Matlab. Each dimension represents a different parameter for the measurement. I want to find the maximum and minimum value and the index (i.e. which parameter) of each. What's the best way to do it? I figure I can take the max of the max of the max in each dimension, but that seems like a kludge.

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• R: optimal way of computing the "product" of two vectors

  - by Musa

  Hi, Let's assume that I have a vector r <- rnorm(4) and a matrix W of dimension 20000*200 for example: W <- matrix(rnorm(20000*200),20000,200) I want to compute a new matrix M of dimension 5000*200 such that m11 <- r%*%W[1:4,1], m21 <- r%*%W[5:8,1], m12 <- r%*%W[1:4,2] etc. (i.e. grouping rows 4-by-4 and computing the product). What's the optimal (speed,memory) way of doing this? Thanks in advance.

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• Currency Conversion in Oracle BI applications

  - by Saurabh Verma

  Authored by Vijay Aggarwal and Hichem Sellami A typical data warehouse contains Star and/or Snowflake schema, made up of Dimensions and Facts. The facts store various numerical information including amounts. Example; Order Amount, Invoice Amount etc. With the true global nature of business now-a-days, the end-users want to view the reports in their own currency or in global/common currency as defined by their business. This presents a unique opportunity in BI to provide the amounts in converted rates either by pre-storing or by doing on-the-fly conversions while displaying the reports to the users. Source Systems OBIA caters to various source systems like EBS, PSFT, Sebl, JDE, Fusion etc. Each source has its own unique and intricate ways of defining and storing currency data, doing currency conversions and presenting to the OLTP users. For example; EBS stores conversion rates between currencies which can be classified by conversion rates, like Corporate rate, Spot rate, Period rate etc. Siebel stores exchange rates by conversion rates like Daily. EBS/Fusion stores the conversion rates for each day, where as PSFT/Siebel store for a range of days. PSFT has Rate Multiplication Factor and Rate Division Factor and we need to calculate the Rate based on them, where as other Source systems store the Currency Exchange Rate directly. OBIA Design The data consolidation from various disparate source systems, poses the challenge to conform various currencies, rate types, exchange rates etc., and designing the best way to present the amounts to the users without affecting the performance. When consolidating the data for reporting in OBIA, we have designed the mechanisms in the Common Dimension, to allow users to report based on their required currencies. OBIA Facts store amounts in various currencies: Document Currency: This is the currency of the actual transaction. For a multinational company, this can be in various currencies. Local Currency: This is the base currency in which the accounting entries are recorded by the business. This is generally defined in the Ledger of the company. Global Currencies: OBIA provides five Global Currencies. Three are used across all modules. The last two are for CRM only. A Global currency is very useful when creating reports where the data is viewed enterprise-wide. Example; a US based multinational would want to see the reports in USD. The company will choose USD as one of the global currencies. OBIA allows users to define up-to five global currencies during the initial implementation. The term Currency Preference is used to designate the set of values: Document Currency, Local Currency, Global Currency 1, Global Currency 2, Global Currency 3; which are shared among all modules. There are four more currency preferences, specific to certain modules: Global Currency 4 (aka CRM Currency) and Global Currency 5 which are used in CRM; and Project Currency and Contract Currency, used in Project Analytics. When choosing Local Currency for Currency preference, the data will show in the currency of the Ledger (or Business Unit) in the prompt. So it is important to select one Ledger or Business Unit when viewing data in Local Currency. More on this can be found in the section: Toggling Currency Preferences in the Dashboard. Design Logic When extracting the fact data, the OOTB mappings extract and load the document amount, and the local amount in target tables. It also loads the exchange rates required to convert the document amount into the corresponding global amounts. If the source system only provides the document amount in the transaction, the extract mapping does a lookup to get the Local currency code, and the Local exchange rate. The Load mapping then uses the local currency code and rate to derive the local amount. The load mapping also fetches the Global Currencies and looks up the corresponding exchange rates. The lookup of exchange rates is done via the Exchange Rate Dimension provided as a Common/Conforming Dimension in OBIA. The Exchange Rate Dimension stores the exchange rates between various currencies for a date range and Rate Type. Two physical tables W_EXCH_RATE_G and W_GLOBAL_EXCH_RATE_G are used to provide the lookups and conversions between currencies. The data is loaded from the source system’s Ledger tables. W_EXCH_RATE_G stores the exchange rates between currencies with a date range. On the other hand, W_GLOBAL_EXCH_RATE_G stores the currency conversions between the document currency and the pre-defined five Global Currencies for each day. Based on the requirements, the fact mappings can decide and use one or both tables to do the conversion. Currency design in OBIA also taps into the MLS and Domain architecture, thus allowing the users to map the currencies to a universal Domain during the implementation time. This is especially important for companies deploying and using OBIA with multiple source adapters. Some Gotchas to Look for It is necessary to think through the currencies during the initial implementation. 1) Identify various types of currencies that are used by your business. Understand what will be your Local (or Base) and Documentation currency. Identify various global currencies that your users will want to look at the reports. This will be based on the global nature of your business. Changes to these currencies later in the project, while permitted, but may cause Full data loads and hence lost time. 2) If the user has a multi source system make sure that the Global Currencies and Global Rate Types chosen in Configuration Manager do have the corresponding source specific counterparts. In other words, make sure for every DW specific value chosen for Currency Code or Rate Type, there is a source Domain mapping already done. Technical Section This section will briefly mention the technical scenarios employed in the OBIA adaptors to extract data from each source system. In OBIA, we have two main tables which store the Currency Rate information as explained in previous sections. W_EXCH_RATE_G and W_GLOBAL_EXCH_RATE_G are the two tables. W_EXCH_RATE_G stores all the Currency Conversions present in the source system. It captures data for a Date Range. W_GLOBAL_EXCH_RATE_G has Global Currency Conversions stored at a Daily level. However the challenge here is to store all the 5 Global Currency Exchange Rates in a single record for each From Currency. Let’s voyage further into the Source System Extraction logic for each of these tables and understand the flow briefly. EBS: In EBS, we have Currency Data stored in GL_DAILY_RATES table. As the name indicates GL_DAILY_RATES EBS table has data at a daily level. However in our warehouse we store the data with a Date Range and insert a new range record only when the Exchange Rate changes for a particular From Currency, To Currency and Rate Type. Below are the main logical steps that we employ in this process. (Incremental Flow only) – Cleanup the data in W_EXCH_RATE_G. Delete the records which have Start Date > minimum conversion date Update the End Date of the existing records. Compress the daily data from GL_DAILY_RATES table into Range Records. Incremental map uses $$XRATE_UPD_NUM_DAY as an extra parameter. Generate Previous Rate, Previous Date and Next Date for each of the Daily record from the OLTP. Filter out the records which have Conversion Rate same as Previous Rates or if the Conversion Date lies within a single day range. Mark the records as ‘Keep’ and ‘Filter’ and also get the final End Date for the single Range record (Unique Combination of From Date, To Date, Rate and Conversion Date). Filter the records marked as ‘Filter’ in the INFA map. The above steps will load W_EXCH_RATE_GS. Step 0 updates/deletes W_EXCH_RATE_G directly. SIL map will then insert/update the GS data into W_EXCH_RATE_G. These steps convert the daily records in GL_DAILY_RATES to Range records in W_EXCH_RATE_G. We do not need such special logic for loading W_GLOBAL_EXCH_RATE_G. This is a table where we store data at a Daily Granular Level. However we need to pivot the data because the data present in multiple rows in source tables needs to be stored in different columns of the same row in DW. We use GROUP BY and CASE logic to achieve this. Fusion: Fusion has extraction logic very similar to EBS. The only difference is that the Cleanup logic that was mentioned in step 0 above does not use $$XRATE_UPD_NUM_DAY parameter. In Fusion we bring all the Exchange Rates in Incremental as well and do the cleanup. The SIL then takes care of Insert/Updates accordingly. PeopleSoft:PeopleSoft does not have From Date and To Date explicitly in the Source tables. Let’s look at an example. Please note that this is achieved from PS1 onwards only. 1 Jan 2010 – USD to INR – 45 31 Jan 2010 – USD to INR – 46 PSFT stores records in above fashion. This means that Exchange Rate of 45 for USD to INR is applicable for 1 Jan 2010 to 30 Jan 2010. We need to store data in this fashion in DW. Also PSFT has Exchange Rate stored as RATE_MULT and RATE_DIV. We need to do a RATE_MULT/RATE_DIV to get the correct Exchange Rate. We generate From Date and To Date while extracting data from source and this has certain assumptions: If a record gets updated/inserted in the source, it will be extracted in incremental. Also if this updated/inserted record is between other dates, then we also extract the preceding and succeeding records (based on dates) of this record. This is required because we need to generate a range record and we have 3 records whose ranges have changed. Taking the same example as above, if there is a new record which gets inserted on 15 Jan 2010; the new ranges are 1 Jan to 14 Jan, 15 Jan to 30 Jan and 31 Jan to Next available date. Even though 1 Jan record and 31 Jan have not changed, we will still extract them because the range is affected. Similar logic is used for Global Exchange Rate Extraction. We create the Range records and get it into a Temporary table. Then we join to Day Dimension, create individual records and pivot the data to get the 5 Global Exchange Rates for each From Currency, Date and Rate Type. Siebel: Siebel Facts are dependent on Global Exchange Rates heavily and almost none of them really use individual Exchange Rates. In other words, W_GLOBAL_EXCH_RATE_G is the main table used in Siebel from PS1 release onwards. As of January 2002, the Euro Triangulation method for converting between currencies belonging to EMU members is not needed for present and future currency exchanges. However, the method is still available in Siebel applications, as are the old currencies, so that historical data can be maintained accurately. The following description applies only to historical data needing conversion prior to the 2002 switch to the Euro for the EMU member countries. If a country is a member of the European Monetary Union (EMU), you should convert its currency to other currencies through the Euro. This is called triangulation, and it is used whenever either currency being converted has EMU Triangulation checked. Due to this, there are multiple extraction flows in SEBL ie. EUR to EMU, EUR to NonEMU, EUR to DMC and so on. We load W_EXCH_RATE_G through multiple flows with these data. This has been kept same as previous versions of OBIA. W_GLOBAL_EXCH_RATE_G being a new table does not have such needs. However SEBL does not have From Date and To Date columns in the Source tables similar to PSFT. We use similar extraction logic as explained in PSFT section for SEBL as well. What if all 5 Global Currencies configured are same? As mentioned in previous sections, from PS1 onwards we store Global Exchange Rates in W_GLOBAL_EXCH_RATE_G table. The extraction logic for this table involves Pivoting data from multiple rows into a single row with 5 Global Exchange Rates in 5 columns. As mentioned in previous sections, we use CASE and GROUP BY functions to achieve this. This approach poses a unique problem when all the 5 Global Currencies Chosen are same. For example – If the user configures all 5 Global Currencies as ‘USD’ then the extract logic will not be able to generate a record for From Currency=USD. This is because, not all Source Systems will have a USD->USD conversion record. We have _Generated mappings to take care of this case. We generate a record with Conversion Rate=1 for such cases. Reusable Lookups Before PS1, we had a Mapplet for Currency Conversions. In PS1, we only have reusable Lookups- LKP_W_EXCH_RATE_G and LKP_W_GLOBAL_EXCH_RATE_G. These lookups have another layer of logic so that all the lookup conditions are met when they are used in various Fact Mappings. Any user who would want to do a LKP on W_EXCH_RATE_G or W_GLOBAL_EXCH_RATE_G should and must use these Lookups. A direct join or Lookup on the tables might lead to wrong data being returned. Changing Currency preferences in the Dashboard: In the 796x series, all amount metrics in OBIA were showing the Global1 amount. The customer needed to change the metric definitions to show them in another Currency preference. Project Analytics started supporting currency preferences since 7.9.6 release though, and it published a Tech note for other module customers to add toggling between currency preferences to the solution. List of Currency Preferences Starting from 11.1.1.x release, the BI Platform added a new feature to support multiple currencies. The new session variable (PREFERRED_CURRENCY) is populated through a newly introduced currency prompt. This prompt can take its values from the xml file: userpref_currencies_OBIA.xml, which is hosted in the BI Server installation folder, under :< home>\instances\instance1\config\OracleBIPresentationServicesComponent\coreapplication_obips1\userpref_currencies.xml This file contains the list of currency preferences, like“Local Currency”, “Global Currency 1”,…which customers can also rename to give them more meaningful business names. There are two options for showing the list of currency preferences to the user in the dashboard: Static and Dynamic. In Static mode, all users will see the full list as in the user preference currencies file. In the Dynamic mode, the list shown in the currency prompt drop down is a result of a dynamic query specified in the same file. Customers can build some security into the rpd, so the list of currency preferences will be based on the user roles…BI Applications built a subject area: “Dynamic Currency Preference” to run this query, and give every user only the list of currency preferences required by his application roles. Adding Currency to an Amount Field When the user selects one of the items from the currency prompt, all the amounts in that page will show in the Currency corresponding to that preference. For example, if the user selects “Global Currency1” from the prompt, all data will be showing in Global Currency 1 as specified in the Configuration Manager. If the user select “Local Currency”, all amount fields will show in the Currency of the Business Unit selected in the BU filter of the same page. If there is no particular Business Unit selected in that filter, and the data selected by the query contains amounts in more than one currency (for example one BU has USD as a functional currency, the other has EUR as functional currency), then subtotals will not be available (cannot add USD and EUR amounts in one field), and depending on the set up (see next paragraph), the user may receive an error. There are two ways to add the Currency field to an amount metric: In the form of currency code, like USD, EUR…For this the user needs to add the field “Apps Common Currency Code” to the report. This field is in every subject area, usually under the table “Currency Tag” or “Currency Code”… In the form of currency symbol ($ for USD, € for EUR,…) For this, the user needs to format the amount metrics in the report as a currency column, by specifying the currency tag column in the Column Properties option in Column Actions drop down list. Typically this column should be the “BI Common Currency Code” available in every subject area. Select Column Properties option in the Edit list of a metric. In the Data Format tab, select Custom as Treat Number As. Enter the following syntax under Custom Number Format: [$:currencyTagColumn=Subjectarea.table.column] Where Column is the “BI Common Currency Code” defined to take the currency code value based on the currency preference chosen by the user in the Currency preference prompt.

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• SPARC T4-2 Produces World Record Oracle Essbase Aggregate Storage Benchmark Result

  - by Brian

  Significance of Results Oracle's SPARC T4-2 server configured with a Sun Storage F5100 Flash Array and running Oracle Solaris 10 with Oracle Database 11g has achieved exceptional performance for the Oracle Essbase Aggregate Storage Option benchmark. The benchmark has upwards of 1 billion records, 15 dimensions and millions of members. Oracle Essbase is a multi-dimensional online analytical processing (OLAP) server and is well-suited to work well with SPARC T4 servers. The SPARC T4-2 server (2 cpus) running Oracle Essbase 11.1.2.2.100 outperformed the previous published results on Oracle's SPARC Enterprise M5000 server (4 cpus) with Oracle Essbase 11.1.1.3 on Oracle Solaris 10 by 80%, 32% and 2x performance improvement on Data Loading, Default Aggregation and Usage Based Aggregation, respectively. The SPARC T4-2 server with Sun Storage F5100 Flash Array and Oracle Essbase running on Oracle Solaris 10 achieves sub-second query response times for 20,000 users in a 15 dimension database. The SPARC T4-2 server configured with Oracle Essbase was able to aggregate and store values in the database for a 15 dimension cube in 398 minutes with 16 threads and in 484 minutes with 8 threads. The Sun Storage F5100 Flash Array provides more than a 20% improvement out-of-the-box compared to a mid-size fiber channel disk array for default aggregation and user-based aggregation. The Sun Storage F5100 Flash Array with Oracle Essbase provides the best combination for large Oracle Essbase databases leveraging Oracle Solaris ZFS and taking advantage of high bandwidth for faster load and aggregation. Oracle Fusion Middleware provides a family of complete, integrated, hot pluggable and best-of-breed products known for enabling enterprise customers to create and run agile and intelligent business applications. Oracle Essbase's performance demonstrates why so many customers rely on Oracle Fusion Middleware as their foundation for innovation. Performance Landscape System Data Size(millions of items) Database Load(minutes) Default Aggregation(minutes) Usage Based Aggregation(minutes) SPARC T4-2, 2 x SPARC T4 2.85 GHz 1000 149 398* 55 Sun M5000, 4 x SPARC64 VII 2.53 GHz 1000 269 526 115 Sun M5000, 4 x SPARC64 VII 2.4 GHz 400 120 448 18 * – 398 mins with CALCPARALLEL set to 16; 484 mins with CALCPARALLEL threads set to 8 Configuration Summary Hardware Configuration: 1 x SPARC T4-2 2 x 2.85 GHz SPARC T4 processors 128 GB memory 2 x 300 GB 10000 RPM SAS internal disks Storage Configuration: 1 x Sun Storage F5100 Flash Array 40 x 24 GB flash modules SAS HBA with 2 SAS channels Data Storage Scheme Striped - RAID 0 Oracle Solaris ZFS Software Configuration: Oracle Solaris 10 8/11 Installer V 11.1.2.2.100 Oracle Essbase Client v 11.1.2.2.100 Oracle Essbase v 11.1.2.2.100 Oracle Essbase Administration services 64-bit Oracle Database 11g Release 2 (11.2.0.3) HP's Mercury Interactive QuickTest Professional 9.5.0 Benchmark Description The objective of the Oracle Essbase Aggregate Storage Option benchmark is to showcase the ability of Oracle Essbase to scale in terms of user population and data volume for large enterprise deployments. Typical administrative and end-user operations for OLAP applications were simulated to produce benchmark results. The benchmark test results include: Database Load: Time elapsed to build a database including outline and data load. Default Aggregation: Time elapsed to build aggregation. User Based Aggregation: Time elapsed of the aggregate views proposed as a result of tracked retrieval queries. Summary of the data used for this benchmark: 40 flat files, each of size 1.2 GB, 49.4 GB in total 10 million rows per file, 1 billion rows total 28 columns of data per row Database outline has 15 dimensions (five of them are attribute dimensions) Customer dimension has 13.3 million members 3 rule files Key Points and Best Practices The Sun Storage F5100 Flash Array has been used to accelerate the application performance. Setting data load threads (DLTHREADSPREPARE) to 64 and Load Buffer to 6 improved dataloading by about 9%. Factors influencing aggregation materialization performance are "Aggregate Storage Cache" and "Number of Threads" (CALCPARALLEL) for parallel view materialization. The optimal values for this workload on the SPARC T4-2 server were: Aggregate Storage Cache: 32 GB CALCPARALLEL: 16   See Also Oracle Essbase Aggregate Storage Option Benchmark on Oracle's SPARC T4-2 Server oracle.com Oracle Essbase oracle.com OTN SPARC T4-2 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 28 August 2012.

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• concurrency::extent<N> from amp.h

  - by Daniel Moth

  Overview We saw in a previous post how index<N> represents a point in N-dimensional space and in this post we'll see how to define the N-dimensional space itself. With C++ AMP, an N-dimensional space can be specified with the template class extent<N> where you define the size of each dimension. From a look and feel perspective, you'd expect the programmatic interface of a point type and size type to be similar (even though the concepts are different). Indeed, exactly like index<N>, extent<N> is essentially a coordinate vector of N integers ordered from most- to least- significant, BUT each integer represents the size for that dimension (and hence cannot be negative). So, if you read the description of index, you won't be surprised with the below description of extent<N> There is the rank field returning the value of N you passed as the template parameter. You can construct one extent from another (via the copy constructor or the assignment operator), you can construct it by passing an integer array, or via convenience constructor overloads for 1- 2- and 3- dimension extents. Note that the parameterless constructor creates an extent of the specified rank with all bounds initialized to 0. You can access the components of the extent through the subscript operator (passing it an integer). You can perform some arithmetic operations between extent objects through operator overloading, i.e. ==, !=, +=, -=, +, -. There are operator overloads so that you can perform operations between an extent and an integer: -- (pre- and post- decrement), ++ (pre- and post- increment), %=, *=, /=, +=, –= and, finally, there are additional overloads for plus and minus (+,-) between extent<N> and index<N> objects, returning a new extent object as the result. In addition to the usual suspects, extent offers a contains function that tests if an index is within the bounds of the extent (assuming an origin of zero). It also has a size function that returns the total linear size of this extent<N> in units of elements. Example code extent<2> e(3, 4); _ASSERT(e.rank == 2); _ASSERT(e.size() == 3 * 4); e += 3; e[1] += 6; e = e + index<2>(3,-4); _ASSERT(e == extent<2>(9, 9)); _ASSERT( e.contains(index<2>(8, 8))); _ASSERT(!e.contains(index<2>(8, 9))); grid<N> Our upcoming pre-release bits also have a similar type to extent, grid<N>. The way you create a grid is by passing it an extent, e.g. extent<3> e(4,2,6); grid<3> g(e); I am not going to dive deeper into grid, suffice for now to think of grid<N> simply as an alias for the extent<N> object, that you create when you encounter a function that expects a grid object instead of an extent object. Usage The extent class on its own simply defines the size of the N-dimensional space. We'll see in future posts that when you create containers (arrays) and wrappers (array_views) for your data, it is an extent<N> object that you'll need to use to create those (and use an index<N> object to index into them). We'll also see that it is a grid<N> object that you pass to the new parallel_for_each function that I'll cover in the next post. Comments about this post by Daniel Moth welcome at the original blog.

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• Setting minimum size limit for a window in java swing

  - by shadyabhi

  I have a JFrame which has 3 JPanels in GridBagLayout.. Now, when I minimize a windows, after a certain limit, the third JPanel tends to disappear. I tried setting minimizing size of JFrame using setMinimumSize(new Dimension(int,int)) but no success. The windows can still be minimized. So, I actually want to make a threshhold, that my window cannot be minimized after a certain limit. How can I do so? Code:- import java.awt.Dimension; import javax.swing.JFrame; public class JFrameExample { public static void main(String[] args) { JFrame frame = new JFrame("Hello World"); frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); frame.setMinimumSize(new Dimension(400, 400)); frame.setVisible(true); } } Also: shadyabhi@shadyabhi-desktop:~/java$ java --showversion java version "1.5.0" gij (GNU libgcj) version 4.4.1 Copyright (C) 2007 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. Usage: gij [OPTION] ... CLASS [ARGS] ... to invoke CLASS.main, or gij -jar [OPTION] ... JARFILE [ARGS] ... to execute a jar file Try `gij --help' for more information. shadyabhi@shadyabhi-desktop:~/java$ Gives me output like

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• How do I construct a more complex single LINQ to XML query?

  - by Cyberherbalist

  I'm a LINQ newbie, so the following might turn out to be very simple and obvious once it's answered, but I have to admit that the question is kicking my arse. Given this XML: <measuresystems> <measuresystem name="SI" attitude="proud"> <dimension name="mass" dim="M" degree="1"> <unit name="kilogram" symbol="kg"> <factor name="hundredweight" foreignsystem="US" value="45.359237" /> <factor name="hundredweight" foreignsystem="Imperial" value="50.80234544" /> </unit> </dimension> </measuresystem> </measuresystems> I can query for the value of the conversion factor between kilogram and US hundredweight using the following LINQ to XML, but surely there is a way to condense the four successive queries into a single complex query? XElement mss = XElement.Load(fileName); IEnumerable<XElement> ms = from el in mss.Elements("measuresystem") where (string)el.Attribute("name") == "SI" select el; IEnumerable<XElement> dim = from e2 in ms.Elements("dimension") where (string)e2.Attribute("name") == "mass" select e2; IEnumerable<XElement> unit = from e3 in dim.Elements("unit") where (string)e3.Attribute("name") == "kilogram" select e3; IEnumerable<XElement> factor = from e4 in unit.Elements("factor") where (string)e4.Attribute("name") == "pound" && (string)e4.Attribute("foreignsystem") == "US" select e4; foreach (XElement ex in factor) { Console.WriteLine ((string)ex.Attribute("value")); }

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