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Last modified by Helena on 2025/09/10 11:19
From version 6.5
edited by Helena
on 2025/05/16 12:34
Change comment: There is no comment for this version
To version 6.7
edited by Helena
on 2025/05/16 12:35
Change comment: There is no comment for this version

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... ... @@ -419,7 +419,6 @@
419 419  
420 420  ‘DF1(1.0.0)/POPULATION.CANADA’ :=
421 421  DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
422 -
423 423  … … …
424 424  
425 425  In fact the VTL operator “sub” has exactly the same behaviour. Therefore, mapping different parts of a SDMX Dataflow to different VTL Data Sets in the direction from SDMX to VTL through the ordered concatenation notation is equivalent to a proper use of the operator “**sub**” on such a Dataflow.{{footnote}}In case the ordered concatenation notation is used, the VTL Transformation described above, e.g. ‘DF1(1.0)/POPULATION.USA’ := DF1(1.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA”], is implicitly executed. In order to test the overall compliance of the VTL program to the VTL consistency rules, it has to be considered as part of the VTL program even if it is not explicitly coded.{{/footnote}}
... ... @@ -472,37 +472,23 @@
472 472  VTL dataset INDICATOR value COUNTRY value
473 473  
474 474  ‘DF2(1.0.0)/GDPPERCAPITA.USA’ GDPPERCAPITA USA
475 -
476 476  ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ GDPPERCAPITA CANADA … … …
477 -
478 478  ‘DF2(1.0.0)/POPGROWTH.USA’ POPGROWTH USA
479 -
480 480  ‘DF2(1.0.0)/POPGROWTH.CANADA’ POPGROWTH CANADA
481 -
482 482  … … …
483 483  
484 484  It should be noted that the application of this many-to-one mapping from VTL to SDMX is equivalent to an appropriate sequence of VTL Transformations. These use the VTL operator “calc” to add the proper VTL identifiers (in the example, INDICATOR and COUNTRY) and to assign to them the proper values and the operator “union” in order to obtain the final VTL dataset (in the example DF2(1.0.0)), that can be mapped oneto-one to the homonymous SDMX Dataflow. Following the same example, these VTL Transformations would be:
485 485  
486 486  DF2bis_GDPPERCAPITA_USA := ‘DF2(1.0.0)/GDPPERCAPITA.USA’ [calc identifier INDICATOR := ”GDPPERCAPITA”, identifier COUNTRY := ”USA”];
487 -
488 488  DF2bis_GDPPERCAPITA_CANADA := ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ [calc identifier INDICATOR:=”GDPPERCAPITA”, identifier COUNTRY:=”CANADA”]; … … …
489 -
490 490  DF2bis_POPGROWTH_USA := ‘DF2(1.0.0)/POPGROWTH.USA’
491 -
492 492  [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”USA”];
493 -
494 494  DF2bis_POPGROWTH_CANADA’ := ‘DF2(1.0.0)/POPGROWTH.CANADA’ [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”CANADA”]; … … …
495 -
496 496  DF2(1.0) <- UNION (DF2bis_GDPPERCAPITA_USA’,
497 -
498 498  DF2bis_GDPPERCAPITA_CANADA’,
499 -
500 500  … ,
501 -
502 502  DF2bis_POPGROWTH_USA’,
503 -
504 504  DF2bis_POPGROWTH_CANADA’
505 -
506 506  …);
507 507  
508 508  In other words, starting from the datasets explicitly calculated through VTL (in the example ‘DF2(1.0)/GDPPERCAPITA.USA’ and so on), the first step consists in calculating other (non-persistent) VTL datasets (in the example