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Last modified by Artur on 2025/09/10 11:19
From version 1.22
edited by Helena
on 2025/06/16 13:27
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To version 1.25
edited by Helena
on 2025/06/16 13:38
Change comment: There is no comment for this version

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... ... @@ -280,11 +280,12 @@
280 280  
281 281  Mapping table:
282 282  
283 -|**VTL**|**SDMX**
284 -|(Simple) Identifier|Dimension
285 -|(Time) Identifier|TimeDimension
286 -|Measure|Measure
287 -|Attribute|DataAttribute
283 +(% style="width:470.294px" %)
284 +|(% style="width:262px" %)**VTL**|(% style="width:205px" %)**SDMX**
285 +|(% style="width:262px" %)(Simple) Identifier|(% style="width:205px" %)Dimension
286 +|(% style="width:262px" %)(Time) Identifier|(% style="width:205px" %)TimeDimension
287 +|(% style="width:262px" %)Measure|(% style="width:205px" %)Measure
288 +|(% style="width:262px" %)Attribute|(% style="width:205px" %)DataAttribute
288 288  
289 289  If the distinction between simple identifier and time identifier is not maintained in the VTL environment, the classification between Dimension and TimeDimension exists only in SDMX, as declared in the relevant DataStructureDefinition.
290 290  
... ... @@ -312,11 +312,12 @@
312 312  
313 313  The summary mapping table of the **unpivot** mapping method is the following:
314 314  
315 -|**VTL**|**SDMX**
316 -|(Simple) Identifier|Dimension
317 -|(Time) Identifier|TimeDimension
318 -|All Measure Components|MeasureDimension (having one Code for each VTL measure component) & one Measure
319 -|Attribute|DataAttribute depending on all SDMX Dimensions including the TimeDimension and except the MeasureDimension
316 +(% style="width:638.294px" %)
317 +|(% style="width:200px" %)**VTL**|(% style="width:435px" %)**SDMX**
318 +|(% style="width:200px" %)(Simple) Identifier|(% style="width:435px" %)Dimension
319 +|(% style="width:200px" %)(Time) Identifier|(% style="width:435px" %)TimeDimension
320 +|(% style="width:200px" %)All Measure Components|(% style="width:435px" %)MeasureDimension (having one Code for each VTL measure component) & one Measure
321 +|(% style="width:200px" %)Attribute|(% style="width:435px" %)DataAttribute depending on all SDMX Dimensions including the TimeDimension and except the MeasureDimension
320 320  
321 321  At observation / data point level:
322 322  
... ... @@ -338,12 +338,13 @@
338 338  
339 339  The mapping table is the following:
340 340  
341 -|VTL|SDMX
342 -|(Simple) Identifier|Dimension
343 -|(Time) Identifier|TimeDimension
344 -|Some Measures|Measure
345 -|Other Measures|DataAttribute
346 -|Attribute|DataAttribute
343 +(% style="width:467.294px" %)
344 +|(% style="width:214px" %)VTL|(% style="width:250px" %)SDMX
345 +|(% style="width:214px" %)(Simple) Identifier|(% style="width:250px" %)Dimension
346 +|(% style="width:214px" %)(Time) Identifier|(% style="width:250px" %)TimeDimension
347 +|(% style="width:214px" %)Some Measures|(% style="width:250px" %)Measure
348 +|(% style="width:214px" %)Other Measures|(% style="width:250px" %)DataAttribute
349 +|(% style="width:214px" %)Attribute|(% style="width:250px" %)DataAttribute
347 347  
348 348  Even in this case, the resulting SDMX definitions must be compliant with the SDMX consistency rules. For example, the SDMX DSD must have the attributeRelationship for the DataAttributes, which does not exist in VTL.
349 349  
... ... @@ -381,11 +381,11 @@
381 381  
382 382  Therefore, the generic name of this kind of VTL datasets would be:
383 383  
384 -'DF(1.0.0)/INDICATORvalue.COUNTRYvalue'
387 +> 'DF(1.0.0)/INDICATORvalue.COUNTRYvalue'
385 385  
386 386  Where DF(1.0.0) is the Dataflow and //INDICATORvalue// and //COUNTRYvalue //are placeholders for one value of the INDICATOR and COUNTRY dimensions. Instead the specific name of one of these VTL datasets would be:
387 387  
388 -‘DF(1.0.0)/POPULATION.USA’
391 +> ‘DF(1.0.0)/POPULATION.USA’
389 389  
390 390  In particular, this is the VTL dataset that contains all the observations of the Dataflow DF(1.0.0) for which //INDICATOR// = POPULATION and //COUNTRY// = USA.
391 391  
... ... @@ -399,26 +399,22 @@
399 399  
400 400  SDMX Dataflow having INDICATOR=//INDICATORvalue //and COUNTRY=// COUNTRYvalue//. For example, the VTL dataset ‘DF1(1.0.0)/POPULATION.USA’ would contain all the observations of DF1(1.0.0) having INDICATOR = POPULATION and COUNTRY = USA.
401 401  
402 -In order to obtain the data structure of these VTL Data Sets from the SDMX one, it is assumed that the SDMX DimensionComponents on which the mapping is based are dropped, i.e. not maintained in the VTL data structure; this is possible because their values are fixed for each one of the invoked VTL Data Sets{{footnote}}If these DimensionComponents would not be dropped, the various VTL Data Sets resulting from this kind of mapping would have non-matching values for the Identifiers corresponding to the mapping Dimensions (e.g. POPULATION and COUNTRY). As a consequence, taking into account that the typical binary VTL operations at dataset level (+, -, *, / and so on) are executed on the observations having matching values for the identifiers, it would not be possible to compose the resulting VTL datasets one another (e.g. it would not be possible to calculate the population ratio between USA and CANADA).{{/footnote}}. After that, the mapping method from SDMX to VTL specified for the Dataflow DF1(1.0.0) is applied (i.e.
405 +In order to obtain the data structure of these VTL Data Sets from the SDMX one, it is assumed that the SDMX DimensionComponents on which the mapping is based are dropped, i.e. not maintained in the VTL data structure; this is possible because their values are fixed for each one of the invoked VTL Data Sets{{footnote}}If these DimensionComponents would not be dropped, the various VTL Data Sets resulting from this kind of mapping would have non-matching values for the Identifiers corresponding to the mapping Dimensions (e.g. POPULATION and COUNTRY). As a consequence, taking into account that the typical binary VTL operations at dataset level (+, -, *, / and so on) are executed on the observations having matching values for the identifiers, it would not be possible to compose the resulting VTL datasets one another (e.g. it would not be possible to calculate the population ratio between USA and CANADA).{{/footnote}}. After that, the mapping method from SDMX to VTL specified for the Dataflow DF1(1.0.0) is applied (i.e. basic, pivot …).
403 403  
404 -basic, pivot …).
405 -
406 406  In the example above, for all the datasets of the kind
407 407  
408 -‘DF1(1.0.0)///INDICATORvalue//.//COUNTRYvalue//’, the dimensions INDICATOR and COUNTRY would be dropped so that the data structure of all the resulting VTL Data Sets would have the identifier TIME_PERIOD only.
409 +> ‘DF1(1.0.0)///INDICATORvalue//.//COUNTRYvalue//’, the dimensions INDICATOR and COUNTRY would be dropped so that the data structure of all the resulting VTL Data Sets would have the identifier TIME_PERIOD only.
409 409  
410 410  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:
411 411  
412 -‘DF1(1.0.0)/POPULATION.USA’ :=
413 +> ‘DF1(1.0.0)/POPULATION.USA’ :=
414 +> DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA” ];
415 +>
416 +> ‘DF1(1.0.0)/POPULATION.CANADA’ :=
417 +> DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
418 +>
419 +> … … …
413 413  
414 -DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA” ];
415 -
416 -‘DF1(1.0.0)/POPULATION.CANADA’ :=
417 -
418 -DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
419 -
420 -… … …
421 -
422 422  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}}
423 423  
424 424  In the direction from SDMX to VTL it is allowed to omit the value of one or more DimensionComponents on which the mapping is based, but maintaining all the separating dots (therefore it may happen to find two or more consecutive dots and dots in the beginning or in the end). The absence of value means that for the corresponding Dimension all the values are kept and the Dimension is not dropped.
... ... @@ -427,10 +427,9 @@
427 427  
428 428  This is equivalent to the application of the VTL “sub” operator only to the identifier //INDICATOR//:
429 429  
430 -‘DF1(1.0.0)/POPULATION.’ :=
429 +> ‘DF1(1.0.0)/POPULATION.’ :=
430 +> DF1(1.0.0) [ sub INDICATOR=“POPULATION” ];
431 431  
432 -DF1(1.0.0) [ sub INDICATOR=“POPULATION” ];
433 -
434 434  Therefore the VTL Data Set ‘DF1(1.0.0)/POPULATION.’ would have the identifiers COUNTRY and TIME_PERIOD.
435 435  
436 436  Heterogeneous invocations of the same Dataflow are allowed, i.e. omitting different Dimensions in different invocations.
... ... @@ -448,41 +448,38 @@
448 448  
449 449  The corresponding VTL Transformations, assuming that the result needs to be persistent, would be of this kind:{{footnote}}the symbol of the VTL persistent assignment is used (<-){{/footnote}}
450 450  
451 -‘DF2(1.0.0)/INDICATORvalue.COUNTRYvalue’ <- expression
449 +> ‘DF2(1.0.0)/INDICATORvalue.COUNTRYvalue’ <- expression
452 452  
453 453  Some examples follow, for some specific values of INDICATOR and COUNTRY:
454 454  
455 -‘DF2(1.0.0)/GDPPERCAPITA.USA’ <- expression11; ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ <- expression12;
456 -… … …
453 +> ‘DF2(1.0.0)/GDPPERCAPITA.USA’ <- expression11; ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ <- expression12;
454 +> … … …
455 +> ‘DF2(1.0.0)/POPGROWTH.USA’ <- expression21;
456 +> ‘DF2(1.0.0)/POPGROWTH.CANADA’ <- expression22;
457 +> … … …
457 457  
458 -‘DF2(1.0.0)/POPGROWTH.USA’ <- expression21;
459 -‘DF2(1.0.0)/POPGROWTH.CANADA’ <- expression22;
460 -… … …
461 -
462 462  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:
463 463  
464 464  VTL dataset   INDICATOR value COUNTRY value
465 465  
466 -‘DF2(1.0.0)/GDPPERCAPITA.USA’ GDPPERCAPITA USA
467 -‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ GDPPERCAPITA CANADA … … …
468 -‘DF2(1.0.0)/POPGROWTH.USA’  POPGROWTH USA
469 -‘DF2(1.0.0)/POPGROWTH.CANADA’ POPGROWTH CANADA
463 +> ‘DF2(1.0.0)/GDPPERCAPITA.USA’ GDPPERCAPITA USA
464 +> ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ GDPPERCAPITA CANADA … … …
465 +> ‘DF2(1.0.0)/POPGROWTH.USA’  POPGROWTH USA
466 +> ‘DF2(1.0.0)/POPGROWTH.CANADA’ POPGROWTH CANADA
467 +> … … …
470 470  
471 -… … …
472 -
473 473  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:
474 474  
475 -DF2bis_GDPPERCAPITA_USA := ‘DF2(1.0.0)/GDPPERCAPITA.USA’ [calc identifier INDICATOR := ”GDPPERCAPITA”, identifier COUNTRY := ”USA”];
476 -DF2bis_GDPPERCAPITA_CANADA := ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ [calc identifier INDICATOR:=”GDPPERCAPITA”, identifier COUNTRY:=”CANADA”]; … … …
477 -DF2bis_POPGROWTH_USA := ‘DF2(1.0.0)/POPGROWTH.USA’
478 -[calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”USA”];
479 -DF2bis_POPGROWTH_CANADA’ := ‘DF2(1.0.0)/POPGROWTH.CANADA’ [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”CANADA”]; … … …
480 -DF2(1.0) <- UNION  (DF2bis_GDPPERCAPITA_USA’,
481 -DF2bis_GDPPERCAPITA_CANADA’,
482 -… ,
483 -DF2bis_POPGROWTH_USA’,
484 -DF2bis_POPGROWTH_CANADA’
485 -…);
471 +> DF2bis_GDPPERCAPITA_USA := ‘DF2(1.0.0)/GDPPERCAPITA.USA’ [calc identifier INDICATOR := ”GDPPERCAPITA”, identifier COUNTRY := ”USA”];
472 +> DF2bis_GDPPERCAPITA_CANADA := ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ [calc identifier INDICATOR:=”GDPPERCAPITA”, identifier COUNTRY:=”CANADA”]; … … …
473 +> DF2bis_POPGROWTH_USA := ‘DF2(1.0.0)/POPGROWTH.USA’  [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”USA”];
474 +> DF2bis_POPGROWTH_CANADA’ := ‘DF2(1.0.0)/POPGROWTH.CANADA’ [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”CANADA”]; … … …
475 +> DF2(1.0) <- UNION  (DF2bis_GDPPERCAPITA_USA’,
476 +> DF2bis_GDPPERCAPITA_CANADA’,
477 +> … ,
478 +> DF2bis_POPGROWTH_USA’,
479 +> DF2bis_POPGROWTH_CANADA’
480 +> …);
486 486  
487 487  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 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.
488 488  
... ... @@ -494,25 +494,26 @@
494 494  
495 495  With reference to the VTL “model for Variables and Value domains”, the following additional mappings have to be considered:
496 496  
497 -|VTL|SDMX
498 -|**Data Set Component**|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^^43^^
499 -|**Represented Variable**|**Concept** with a definite Representation
500 -|**Value Domain**|(((
492 +(% style="width:706.294px" %)
493 +|(% style="width:257px" %)VTL|(% style="width:446px" %)SDMX
494 +|(% style="width:257px" %)**Data Set Component**|(% style="width:446px" %)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^^43^^
495 +|(% style="width:257px" %)**Represented Variable**|(% style="width:446px" %)**Concept** with a definite Representation
496 +|(% style="width:257px" %)**Value Domain**|(% style="width:446px" %)(((
501 501  **Representation** (see the Structure
502 502  Pattern in the Base Package)
503 503  )))
504 -|**Enumerated Value Domain / Code List**|**Codelist**
505 -|**Code**|**Code** (for enumerated DimensionComponent, Measure, DataAttribute)
506 -|**Described Value Domain**|(((
500 +|(% style="width:257px" %)**Enumerated Value Domain / Code List**|(% style="width:446px" %)**Codelist**
501 +|(% style="width:257px" %)**Code**|(% style="width:446px" %)**Code** (for enumerated DimensionComponent, Measure, DataAttribute)
502 +|(% style="width:257px" %)**Described Value Domain**|(% style="width:446px" %)(((
507 507  non-enumerated** Representation**
508 508  (having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
509 509  )))
510 -|**Value**|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
511 -| |to a valid **value **(for non-enumerated** **Representations)
512 -|**Value Domain Subset / Set**|This abstraction does not exist in SDMX
513 -|**Enumerated Value Domain Subset / Enumerated Set**|This abstraction does not exist in SDMX
514 -|**Described Value Domain Subset / Described Set**|This abstraction does not exist in SDMX
515 -|**Set list**|This abstraction does not exist in SDMX
506 +|(% style="width:257px" %)**Value**|(% style="width:446px" %)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
507 +|(% style="width:257px" %) |(% style="width:446px" %)to a valid **value **(for non-enumerated** **Representations)
508 +|(% style="width:257px" %)**Value Domain Subset / Set**|(% style="width:446px" %)This abstraction does not exist in SDMX
509 +|(% style="width:257px" %)**Enumerated Value Domain Subset / Enumerated Set**|(% style="width:446px" %)This abstraction does not exist in SDMX
510 +|(% style="width:257px" %)**Described Value Domain Subset / Described Set**|(% style="width:446px" %)This abstraction does not exist in SDMX
511 +|(% style="width:257px" %)**Set list**|(% style="width:446px" %)This abstraction does not exist in SDMX
516 516  
517 517  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).
518 518  
... ... @@ -520,8 +520,10 @@
520 520  
521 521  Therefore, it is important to be aware that some VTL operations (for example the binary operations at data set level) are consistent only if the components having the same names in the operated VTL Data Sets have also the same representation (i.e. the same Value Domain as for VTL). For example, it is possible to obtain correct results from the VTL expression
522 522  
523 -DS_c := DS_a + DS_b (where DS_a, DS_b, DS_c are VTL Data Sets) if the matching components in DS_a and DS_b (e.g. ref_date, geo_area, sector …) refer to the same general representation. In simpler words, DS_a and DS_b must use the same values/codes (for ref_date, geo_area, sector … ), otherwise the relevant values would not match and the result of the operation would be wrong.
519 +> DS_c := DS_a + DS_b (where DS_a, DS_b, DS_c are VTL Data Sets)
524 524  
521 +if the matching components in DS_a and DS_b (e.g. ref_date, geo_area, sector …) refer to the same general representation. In simpler words, DS_a and DS_b must use the same values/codes (for ref_date, geo_area, sector … ), otherwise the relevant values would not match and the result of the operation would be wrong.
522 +
525 525  As mentioned, the property above is not enforced by construction in SDMX, and different representations of the same Concept can be not compatible one another (for example, it may happen that geo_area is represented by ISO-alpha-3 codes in DS_a and by ISO alpha-2 codes in DS_b). Therefore, it will be up to the definer of VTL
526 526  
527 527  Transformations to ensure that the VTL expressions are consistent with the actual representations of the correspondent SDMX Concepts.
... ... @@ -536,8 +536,9 @@
536 536  
537 537  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:
538 538  
539 -[[image:1750067055028-964.png]]
540 540  
538 +[[image:1750070288958-132.png]]
539 +
541 541  **Figure 22 – VTL Data Types**
542 542  
543 543  The VTL scalar types are in turn subdivided in basic scalar types, which are elementary (not defined in term of other data types) and Value Domain and Set scalar types, which are defined in terms of the basic scalar types.
1750070288958-132.png
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