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

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... ... @@ -419,6 +419,7 @@
419 419  
420 420  ‘DF1(1.0.0)/POPULATION.CANADA’ :=
421 421  DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
422 +
422 422  … … …
423 423  
424 424  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}}
... ... @@ -471,23 +471,37 @@
471 471  VTL dataset INDICATOR value COUNTRY value
472 472  
473 473  ‘DF2(1.0.0)/GDPPERCAPITA.USA’ GDPPERCAPITA USA
475 +
474 474  ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ GDPPERCAPITA CANADA … … …
477 +
475 475  ‘DF2(1.0.0)/POPGROWTH.USA’ POPGROWTH USA
479 +
476 476  ‘DF2(1.0.0)/POPGROWTH.CANADA’ POPGROWTH CANADA
481 +
477 477  … … …
478 478  
479 479  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:
480 480  
481 481  DF2bis_GDPPERCAPITA_USA := ‘DF2(1.0.0)/GDPPERCAPITA.USA’ [calc identifier INDICATOR := ”GDPPERCAPITA”, identifier COUNTRY := ”USA”];
487 +
482 482  DF2bis_GDPPERCAPITA_CANADA := ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ [calc identifier INDICATOR:=”GDPPERCAPITA”, identifier COUNTRY:=”CANADA”]; … … …
489 +
483 483  DF2bis_POPGROWTH_USA := ‘DF2(1.0.0)/POPGROWTH.USA’
491 +
484 484  [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”USA”];
493 +
485 485  DF2bis_POPGROWTH_CANADA’ := ‘DF2(1.0.0)/POPGROWTH.CANADA’ [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”CANADA”]; … … …
495 +
486 486  DF2(1.0) <- UNION (DF2bis_GDPPERCAPITA_USA’,
497 +
487 487  DF2bis_GDPPERCAPITA_CANADA’,
499 +
488 488  … ,
501 +
489 489  DF2bis_POPGROWTH_USA’,
503 +
490 490  DF2bis_POPGROWTH_CANADA’
505 +
491 491  …);
492 492  
493 493  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