Changes for page 12 Validation and Transformation Language (VTL)

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...	...	@@ -235,16 +235,18 @@
235	235	* The SDMX Measure is not mapped to VTL as well (it disappears in the VTL Data Structure);
236	236	* An SDMX DataAttribute is mapped in different ways according to its AttributeRelationship:
237	237	** If, according to the SDMX AttributeRelationship, the values of the DataAttribute do not depend on the values of the MeasureDimension, the SDMX DataAttribute becomes a VTL Attribute having the same name. This happens if the AttributeRelationship is not specified (i.e. the DataAttribute does not depend on any DimensionComponent and therefore is at data set level), or if it refers to a set (or a group) of dimensions which does not include the MeasureDimension;
238		-** Otherwise, if, according to the SDMX AttributeRelationship, the values of the DataAttribute depend on the MeasureDimension, the SDMX DataAttribute is mapped to one VTL Attribute for each possible Code of the SDMX MeasureDimension. By default, the names of the VTL Attributes are obtained by concatenating the name of the SDMX DataAttribute and the names of the correspondent Code of the MeasureDimension separated by underscore. For example, if the SDMX DataAttribute is named DA and the possible Codes of the SDMX MeasureDimension are named C1, C2, …, Cn, then the corresponding VTL Attributes will be named DA_C1, DA_C2, …, DA_Cn (if different names are desired, they can be achieved afterwards by renaming the Attributes through VTL operators). o Like in the Basic mapping, the resulting VTL Attributes are considered as dependent on all the VTL identifiers (i.e. "at data point / observation level"), because VTL does not have the SDMX notion of Attribute Relationship.
	238	+** Otherwise, if, according to the SDMX AttributeRelationship, the values of the DataAttribute depend on the MeasureDimension, the SDMX DataAttribute is mapped to one VTL Attribute for each possible Code of the SDMX MeasureDimension. By default, the names of the VTL Attributes are obtained by concatenating the name of the SDMX DataAttribute and the names of the correspondent Code of the MeasureDimension separated by underscore. For example, if the SDMX DataAttribute is named DA and the possible Codes of the SDMX MeasureDimension are named C1, C2, …, Cn, then the corresponding VTL Attributes will be named DA_C1, DA_C2, …, DA_Cn (if different names are desired, they can be achieved afterwards by renaming the Attributes through VTL operators).
	239	+** Like in the Basic mapping, the resulting VTL Attributes are considered as dependent on all the VTL identifiers (i.e. "at data point / observation level"), because VTL does not have the SDMX notion of Attribute Relationship.
239	239
240	240	The summary mapping table of the "pivot" mapping from SDMX to VTL for the SDMX data structures that contain a MeasureDimension is the following:
241	241
242		-|**SDMX**|**VTL**
243		-|Dimension|(Simple) Identifier
244		-|TimeDimension|(Time) Identifier
245		-|MeasureDimension & one Measure|One Measure for each Code of the SDMX MeasureDimension
246		-|DataAttribute not depending on the MeasureDimension|Attribute
247		-|DataAttribute depending on the MeasureDimension|(((
	243	+(% style="width:739.294px" %)
	244	+|(% style="width:335px" %)**SDMX**|(% style="width:400px" %)**VTL**
	245	+|(% style="width:335px" %)Dimension|(% style="width:400px" %)(Simple) Identifier
	246	+|(% style="width:335px" %)TimeDimension|(% style="width:400px" %)(Time) Identifier
	247	+|(% style="width:335px" %)MeasureDimension & one Measure|(% style="width:400px" %)One Measure for each Code of the SDMX MeasureDimension
	248	+|(% style="width:335px" %)DataAttribute not depending on the MeasureDimension|(% style="width:400px" %)Attribute
	249	+|(% style="width:335px" %)DataAttribute depending on the MeasureDimension|(% style="width:400px" %)(((
248	248	One Attribute for each Code of the
249	249	SDMX MeasureDimension
250	250	)))
...	...	@@ -254,19 +254,14 @@
254	254	At observation / data point level, calling Cj (j=1, … n) the j^^th^^ Code of the MeasureDimension:
255	255
256	256	* The set of SDMX observations having the same values for all the Dimensions except than the MeasureDimension become one multi-measure VTL Data Point, having one Measure for each Code Cj of the SDMX MeasureDimension;
257		-* The values of the SDMX simple Dimensions, TimeDimension and DataAttributes not depending on the MeasureDimension (these components by definition have always the same values for all the observations of the set above) become the values of the corresponding VTL (simple)
258		-
259		-Identifiers, (time) Identifier and Attributes.
260		-
	259	+* The values of the SDMX simple Dimensions, TimeDimension and DataAttributes not depending on the MeasureDimension (these components by definition have always the same values for all the observations of the set above) become the values of the corresponding VTL (simple) Identifiers, (time) Identifier and Attributes.
261	261	* The value of the Measure of the SDMX observation belonging to the set above and having MeasureDimension=Cj becomes the value of the VTL Measure Cj
262	262	* For the SDMX DataAttributes depending on the MeasureDimension, the value of the DataAttribute DA of the SDMX observation belonging to the set above and having MeasureDimension=Cj becomes the value of the VTL Attribute DA_Cj
263	263
264	264	==== 12.3.3.3 From SDMX DataAttributes to VTL Measures ====
265	265
266		-* In some cases, it may happen that the DataAttributes of the SDMX DataStructure need to be managed as Measures in VTL. Therefore, a variant of both the methods above consists in transforming all the SDMX DataAttributes in VTL Measures. When DataAttributes are converted to Measures, the two methods above are called Basic_A2M and Pivot_A2M (the suffix "A2M" stands for Attributes to Measures). Obviously, the resulting VTL data structure is, in general, multi-measure and does not contain
	265	+* In some cases, it may happen that the DataAttributes of the SDMX DataStructure need to be managed as Measures in VTL. Therefore, a variant of both the methods above consists in transforming all the SDMX DataAttributes in VTL Measures. When DataAttributes are converted to Measures, the two methods above are called Basic_A2M and Pivot_A2M (the suffix "A2M" stands for Attributes to Measures). Obviously, the resulting VTL data structure is, in general, multi-measure and does not contain Attributes.
267	267
268		-Attributes.
269		-
270	270	The Basic_A2M and Pivot_A2M behaves respectively like the Basic and Pivot methods, except that the final VTL components, which according to the Basic and Pivot methods would have had the role of Attribute, assume instead the role of Measure.
271	271
272	272	Proper VTL features allow changing the role of specific attributes even after the SDMX to VTL mapping: they can be useful when only some of the DataAttributes need to be managed as VTL Measures.
...	...	@@ -283,11 +283,12 @@
283	283
284	284	Mapping table:
285	285
286		-|**VTL**|**SDMX**
287		-|(Simple) Identifier|Dimension
288		-|(Time) Identifier|TimeDimension
289		-|Measure|Measure
290		-|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
291	291
292	292	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.
293	293
...	...	@@ -315,11 +315,12 @@
315	315
316	316	The summary mapping table of the **unpivot** mapping method is the following:
317	317
318		-|**VTL**|**SDMX**
319		-|(Simple) Identifier|Dimension
320		-|(Time) Identifier|TimeDimension
321		-|All Measure Components|MeasureDimension (having one Code for each VTL measure component) & one Measure
322		-|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
323	323
324	324	At observation / data point level:
325	325
...	...	@@ -341,12 +341,13 @@
341	341
342	342	The mapping table is the following:
343	343
344		-|VTL|SDMX
345		-|(Simple) Identifier|Dimension
346		-|(Time) Identifier|TimeDimension
347		-|Some Measures|Measure
348		-|Other Measures|DataAttribute
349		-|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
350	350
351	351	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.
352	352
...	...	@@ -384,11 +384,11 @@
384	384
385	385	Therefore, the generic name of this kind of VTL datasets would be:
386	386
387		-'DF(1.0.0)/INDICATORvalue.COUNTRYvalue'
	387	+> 'DF(1.0.0)/INDICATORvalue.COUNTRYvalue'
388	388
389	389	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:
390	390
391		-‘DF(1.0.0)/POPULATION.USA’
	391	+> ‘DF(1.0.0)/POPULATION.USA’
392	392
393	393	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.
394	394
...	...	@@ -402,26 +402,22 @@
402	402
403	403	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.
404	404
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.
	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 …).
406	406
407		-basic, pivot …).
408		-
409	409	In the example above, for all the datasets of the kind
410	410
411		-‘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.
412	412
413	413	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:
414	414
415		-‘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	+> … … …
416	416
417		-DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA” ];
418		-
419		-‘DF1(1.0.0)/POPULATION.CANADA’ :=
420		-
421		-DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
422		-
423		-… … …
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}}
426	426
427	427	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.
...	...	@@ -430,10 +430,9 @@
430	430
431	431	This is equivalent to the application of the VTL “sub” operator only to the identifier //INDICATOR//:
432	432
433		-‘DF1(1.0.0)/POPULATION.’ :=
	429	+> ‘DF1(1.0.0)/POPULATION.’ :=
	430	+> DF1(1.0.0) [ sub INDICATOR=“POPULATION” ];
434	434
435		-DF1(1.0.0) [ sub INDICATOR=“POPULATION” ];
436		-
437	437	Therefore the VTL Data Set ‘DF1(1.0.0)/POPULATION.’ would have the identifiers COUNTRY and TIME_PERIOD.
438	438
439	439	Heterogeneous invocations of the same Dataflow are allowed, i.e. omitting different Dimensions in different invocations.
...	...	@@ -451,41 +451,38 @@
451	451
452	452	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}}
453	453
454		-‘DF2(1.0.0)/INDICATORvalue.COUNTRYvalue’ <- expression
	449	+> ‘DF2(1.0.0)/INDICATORvalue.COUNTRYvalue’ <- expression
455	455
456	456	Some examples follow, for some specific values of INDICATOR and COUNTRY:
457	457
458		-‘DF2(1.0.0)/GDPPERCAPITA.USA’ <- expression11; ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ <- expression12;
459		-… … …
	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	+> … … …
460	460
461		-‘DF2(1.0.0)/POPGROWTH.USA’ <- expression21;
462		-‘DF2(1.0.0)/POPGROWTH.CANADA’ <- expression22;
463		-… … …
464		-
465	465	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:
466	466
467	467	VTL dataset   INDICATOR value COUNTRY value
468	468
469		-‘DF2(1.0.0)/GDPPERCAPITA.USA’ GDPPERCAPITA USA
470		-‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ GDPPERCAPITA CANADA … … …
471		-‘DF2(1.0.0)/POPGROWTH.USA’  POPGROWTH USA
472		-‘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	+> … … …
473	473
474		-… … …
475		-
476	476	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:
477	477
478		-DF2bis_GDPPERCAPITA_USA := ‘DF2(1.0.0)/GDPPERCAPITA.USA’ [calc identifier INDICATOR := ”GDPPERCAPITA”, identifier COUNTRY := ”USA”];
479		-DF2bis_GDPPERCAPITA_CANADA := ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ [calc identifier INDICATOR:=”GDPPERCAPITA”, identifier COUNTRY:=”CANADA”]; … … …
480		-DF2bis_POPGROWTH_USA := ‘DF2(1.0.0)/POPGROWTH.USA’
481		-[calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”USA”];
482		-DF2bis_POPGROWTH_CANADA’ := ‘DF2(1.0.0)/POPGROWTH.CANADA’ [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”CANADA”]; … … …
483		-DF2(1.0) <- UNION  (DF2bis_GDPPERCAPITA_USA’,
484		-DF2bis_GDPPERCAPITA_CANADA’,
485		-… ,
486		-DF2bis_POPGROWTH_USA’,
487		-DF2bis_POPGROWTH_CANADA’
488		-…);
	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	+> …);
489	489
490	490	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.
491	491
...	...	@@ -497,25 +497,26 @@
497	497
498	498	With reference to the VTL “model for Variables and Value domains”, the following additional mappings have to be considered:
499	499
500		-|VTL|SDMX
501		-|**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^^
502		-|**Represented Variable**|**Concept** with a definite Representation
503		-|**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" %)(((
504	504	**Representation** (see the Structure
505	505	Pattern in the Base Package)
506	506	)))
507		-|**Enumerated Value Domain / Code List**|**Codelist**
508		-|**Code**|**Code** (for enumerated DimensionComponent, Measure, DataAttribute)
509		-|**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" %)(((
510	510	non-enumerated** Representation**
511	511	(having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
512	512	)))
513		-|**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
514		-| |to a valid **value **(for non-enumerated** **Representations)
515		-|**Value Domain Subset / Set**|This abstraction does not exist in SDMX
516		-|**Enumerated Value Domain Subset / Enumerated Set**|This abstraction does not exist in SDMX
517		-|**Described Value Domain Subset / Described Set**|This abstraction does not exist in SDMX
518		-|**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
519	519
520	520	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).
521	521
...	...	@@ -523,8 +523,10 @@
523	523
524	524	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
525	525
526		-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)
527	527
	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	+
528	528	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
529	529
530	530	Transformations to ensure that the VTL expressions are consistent with the actual representations of the correspondent SDMX Concepts.
...	...	@@ -539,8 +539,9 @@
539	539
540	540	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:
541	541
542		-[[image:1750067055028-964.png]]
543	543
	538	+[[image:1750070288958-132.png]]
	539	+
544	544	**Figure 22 – VTL Data Types**
545	545
546	546	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.

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