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Last modified by Helena on 2025/09/10 11:19
From version 6.6
edited by Helena
on 2025/05/16 12:34
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To version 6.12
edited by Helena
on 2025/05/16 12:41
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... ... @@ -414,13 +414,8 @@
414 414  
415 415  It should be noted that the desired VTL Data Sets (i.e. of the kind ‘DF1(1.0.0)/// INDICATORvalue//.//COUNTRYvalue//’) can be obtained also by applying the VTL operator “**sub**” (subspace) to the Dataflow DF1(1.0.0), like in the following VTL expression:
416 416  
417 -‘DF1(1.0.0)/POPULATION.USA’ :=
418 -DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA” ];
417 +[[image:1747388275998-621.png]]
419 419  
420 -‘DF1(1.0.0)/POPULATION.CANADA’ :=
421 -DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
422 -… … …
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}}
425 425  
426 426  In the direction from SDMX to VTL it is allowed to omit the value of one or more
... ... @@ -431,8 +431,7 @@
431 431  
432 432  This is equivalent to the application of the VTL “sub” operator only to the identifier //INDICATOR//:
433 433  
434 -‘DF1(1.0.0)/POPULATION.’ :=
435 -DF1(1.0.0) [ sub INDICATOR=“POPULATION” ];
429 +[[image:1747388244829-693.png]]
436 436  
437 437  Therefore the VTL Data Set ‘DF1(1.0.0)/POPULATION.’ would have the identifiers COUNTRY and TIME_PERIOD.
438 438  
... ... @@ -459,47 +459,18 @@
459 459  
460 460  Some examples follow, for some specific values of INDICATOR and COUNTRY:
461 461  
462 -‘DF2(1.0.0)/GDPPERCAPITA.USA’ <- expression11; ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ <- expression12;
456 +[[image:1747388222879-916.png]]
463 463  
464 -… … …
465 -‘DF2(1.0.0)/POPGROWTH.USA’ <- expression21;
466 -‘DF2(1.0.0)/POPGROWTH.CANADA’ <- expression22;
467 -… … …
458 +[[image:1747388206717-256.png]]
468 468  
469 469  As said, it is assumed that these VTL derived Data Sets have the TIME_PERIOD as the only identifier. In the mapping from VTL to SMDX, the Dimensions INDICATOR and COUNTRY are added to the VTL data structure on order to obtain the SDMX one, with the following values respectively:
470 470  
471 -VTL dataset INDICATOR value COUNTRY value
462 +[[image:1747388148322-387.png]]
472 472  
473 -‘DF2(1.0.0)/GDPPERCAPITA.USA’ GDPPERCAPITA USA
474 -‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ GDPPERCAPITA CANADA … … …
475 -‘DF2(1.0.0)/POPGROWTH.USA’ POPGROWTH USA
476 -‘DF2(1.0.0)/POPGROWTH.CANADA’ POPGROWTH CANADA
477 -… … …
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 -DF2bis_GDPPERCAPITA_USA := ‘DF2(1.0.0)/GDPPERCAPITA.USA’ [calc identifier INDICATOR := ”GDPPERCAPITA”, identifier COUNTRY := ”USA”];
466 +[[image:1747388179021-814.png]]
482 482  
483 -DF2bis_GDPPERCAPITA_CANADA := ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ [calc identifier INDICATOR:=”GDPPERCAPITA”, identifier COUNTRY:=”CANADA”]; … … …
484 -
485 -DF2bis_POPGROWTH_USA := ‘DF2(1.0.0)/POPGROWTH.USA’
486 -
487 -[calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”USA”];
488 -
489 -DF2bis_POPGROWTH_CANADA’ := ‘DF2(1.0.0)/POPGROWTH.CANADA’ [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”CANADA”]; … … …
490 -
491 -DF2(1.0) <- UNION (DF2bis_GDPPERCAPITA_USA’,
492 -
493 -DF2bis_GDPPERCAPITA_CANADA’,
494 -
495 -… ,
496 -
497 -DF2bis_POPGROWTH_USA’,
498 -
499 -DF2bis_POPGROWTH_CANADA’
500 -
501 -…);
502 -
503 503  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
504 504  
505 505  DF2bis_GDPPERCAPITA_USA and so on) by adding the identifiers INDICATOR and COUNTRY with the desired values (//INDICATORvalue// and //COUNTRYvalue)//. Finally, all these non-persistent Data Sets are united and give the final result DF2(1.0){{footnote}}The result is persistent in this example but it can be also non persistent if needed.{{/footnote}}, which can be mapped one-to-one to the homonymous SDMX Dataflow having the dimension components TIME_PERIOD, INDICATOR and COUNTRY.
... ... @@ -512,38 +512,30 @@
512 512  
513 513  With reference to the VTL “model for Variables and Value domains”, the following additional mappings have to be considered:
514 514  
515 -(% style="width:1170.29px" %)
516 -|**VTL**|(% style="width:754px" %)**SDMX**
517 -|**Data Set Component**|(% style="width:754px" %)Although this abstraction exists in SDMX, it does not have an explicit definition and correspond to a Component (either a DimensionComponent or a Measure or a DataAttribute) belonging to one specific Dataflow{{footnote}}Through SDMX Constraints, it is possible to specify the values that a Component of a Dataflow can assume.{{/footnote}}
518 -|**Represented Variable**|(% style="width:754px" %)(((
480 +(% style="width:895.294px" %)
481 +|(% style="width:278px" %)**VTL**|(% style="width:613px" %)**SDMX**
482 +|(% style="width:278px" %)**Data Set Component**|(% style="width:613px" %)Although this abstraction exists in SDMX, it does not have an explicit definition and correspond to a Component (either a DimensionComponent or a Measure or a DataAttribute) belonging to one specific Dataflow{{footnote}}Through SDMX Constraints, it is possible to specify the values that a Component of a Dataflow can assume.{{/footnote}}
483 +|(% style="width:278px" %)**Represented Variable**|(% style="width:613px" %)(((
519 519  **Concept** with a definite
520 520  
521 521  Representation
522 522  )))
523 -|**Value Domain**|(% style="width:754px" %)(((
524 -**Representation** (see the Structure
525 -
526 -Pattern in the Base Package)
488 +|(% style="width:278px" %)**Value Domain**|(% style="width:613px" %)(((
489 +**Representation** (see the Structure Pattern in the Base Package)
527 527  )))
528 -|**Enumerated Value Domain / Code List**|(% style="width:754px" %)**Codelist**
529 -|**Code**|(% style="width:754px" %)(((
530 -**Code** (for enumerated
531 -
532 -DimensionComponent, Measure, DataAttribute)
491 +|(% style="width:278px" %)**Enumerated Value Domain /
492 +Code List**|(% style="width:613px" %)**Codelist**
493 +|(% style="width:278px" %)**Code**|(% style="width:613px" %)(((
494 +**Code** (for enumerated DimensionComponent, Measure, DataAttribute)
533 533  )))
534 -|**Described Value Domain**|(% style="width:754px" %)(((
535 -non-enumerated** Representation**
536 -
537 -(having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
496 +|(% style="width:278px" %)**Described Value Domain**|(% style="width:613px" %)(((
497 +non-enumerated** Representation **(having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
538 538  )))
539 -|**Value**|(% style="width:754px" %)Although this abstraction exists in SDMX, it does not have an explicit definition and correspond to a **Code** of a Codelist (for enumerated Representations) or
540 -| |(% style="width:754px" %)(((
541 -to a valid **value **(for non-enumerated** **Representations)
542 -)))
543 -|**Value Domain Subset / Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
544 -|**Enumerated Value Domain Subset / Enumerated Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
545 -|**Described Value Domain Subset / Described Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
546 -|**Set list**|(% style="width:754px" %)This abstraction does not exist in SDMX
499 +|(% style="width:278px" %)**Value**|(% style="width:613px" %)Although this abstraction exists in SDMX, it does not have an explicit definition and correspond to a **Code** of a Codelist (for enumerated Representations) or to a valid **value **(for non-enumerated** **Representations)
500 +|(% style="width:278px" %)**Value Domain Subset / Set**|(% style="width:613px" %)This abstraction does not exist in SDMX
501 +|(% style="width:278px" %)**Enumerated Value Domain Subset / Enumerated Set**|(% style="width:613px" %)This abstraction does not exist in SDMX
502 +|(% style="width:278px" %)**Described Value Domain Subset / Described Set**|(% style="width:613px" %)This abstraction does not exist in SDMX
503 +|(% style="width:278px" %)**Set list**|(% style="width:613px" %)This abstraction does not exist in SDMX
547 547  
548 548  The main difference between VTL and SDMX relies on the fact that the VTL artefacts for defining subsets of Value Domains do not exist in SDMX, therefore the VTL features for referring to predefined subsets are not available in SDMX. These artefacts are the Value Domain Subset (or Set), either enumerated or described, the Set List (list of values belonging to enumerated subsets) and the Data Set Component (aimed at defining the set of values that the Component of a Data Set can take, possibly a subset of the codes of Value Domain).
549 549  
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569 569  
570 570  The VTL data types are sub-divided in scalar types (like integers, strings, etc.), which are the types of the scalar values, and compound types (like Data Sets, Components, Rulesets, etc.), which are the types of the compound structures. See below the diagram of the VTL data types, taken from the VTL User Manual:
571 571  
572 -[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_e3df33ae.png||height="543" width="483"]]
529 +[[image:1747388434672-948.png]]
573 573  
574 574  (% class="wikigeneratedid" id="HFigure222013VTLDataTypes" %)
575 575  **Figure 22 – VTL Data Types**
... ... @@ -578,13 +578,10 @@
578 578  
579 579  The VTL basic scalar types are listed below and follow a hierarchical structure in terms of supersets/subsets (e.g. "scalar" is the superset of all the basic scalar types):
580 580  
538 +[[image:1747388465321-274.png]]
581 581  
582 582  **Figure 23 – VTL Basic Scalar Types**
583 583  
584 -(((
585 -
586 -)))
587 -
588 588  === 12.4.2 VTL basic scalar types and SDMX data types ===
589 589  
590 590  The VTL assumes that a basic scalar type has a unique internal representation and can have more external representations.
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