Changes for page 12 Validation and Transformation Language (VTL)

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...	...	@@ -198,7 +198,7 @@
198	198
199	199	The possible mapping options are described in more detail in the following sections.
200	200
201		-=== 12.3.2 Mapping from SDMX to VTL data structures ===
	201	+=== 12.3.3 Mapping from SDMX to VTL data structures ===
202	202
203	203	==== 12.3.3.1 Basic Mapping ====
204	204
...	...	@@ -206,11 +206,12 @@
206	206
207	207	When transforming **from SDMX to VTL**, this method consists in leaving the components unchanged and maintaining their names and roles, according to the following table:
208	208
209		-|**SDMX**|**VTL**
210		-|Dimension|(Simple) Identifier
211		-|TimeDimension|(Time) Identifier
212		-|Measure|Measure
213		-|DataAttribute|Attribute
	209	+(% style="width:468.294px" %)
	210	+|(% style="width:196px" %)**SDMX**|(% style="width:269px" %)**VTL**
	211	+|(% style="width:196px" %)Dimension|(% style="width:269px" %)(Simple) Identifier
	212	+|(% style="width:196px" %)TimeDimension|(% style="width:269px" %)(Time) Identifier
	213	+|(% style="width:196px" %)Measure|(% style="width:269px" %)Measure
	214	+|(% style="width:196px" %)DataAttribute|(% style="width:269px" %)Attribute
214	214
215	215	The SDMX DataAttributes, in VTL they are all considered "at data point / observation level" (i.e. dependent on all the VTL Identifiers), because VTL does not have the SDMX AttributeRelationships, which defines the construct to which the DataAttribute is related (e.g. observation, dimension or set or group of dimensions, whole data set).
216	216
...	...	@@ -220,10 +220,8 @@
220	220
221	221	An alternative mapping method from SDMX to VTL is the **Pivot **mapping, which makes sense and is different from the Basic method only for the SDMX data structures that contain a Dimension that plays the role of measure dimension (like in SDMX 2.1) and just one Measure. Through this method, these structures can be mapped to multimeasure VTL data structures. Besides that, a user may choose to use any Dimension acting as a list of Measures (e.g., a Dimension with indicators), either by considering the “Measure” role of a Dimension, or at will using any coded Dimension. Of course, in SDMX 3.0, this can only work when only one Measure is defined in the DSD.
222	222
223		-In SDMX 2.1 the MeasureDimension was a subclass of DimensionComponent like Dimension and TimeDimension. In the current SDMX version, this subclass does not exist anymore, however a Dimension can have the role of measure dimension (i.e. a Dimension that contributes to the identification of the measures). In SDMX 2.1 a DataStructure could have zero or one MeasureDimensions, in the current version of the standard, from zero to many Dimension may have the role of measure dimension. Hereinafter a Dimension that plays the role of measure dimension is referenced for simplicity as “MeasureDimension“, i.e. maintaining the capital letters and the courier font even if the MeasureDimension is not anymore a class in the SDMX Information Model of the current SDMX version. For the sake of simplicity, the description below considers just one Dimension having the role of MeasureDimension (i.e., the more simple and common case). Nevertheless, it maintains its validity also if in the DataStructure there are more dimension with the role of MeasureDimensions: in this case what is said about the MeasureDimension must be applied to the combination of all the
	224	+In SDMX 2.1 the MeasureDimension was a subclass of DimensionComponent like Dimension and TimeDimension. In the current SDMX version, this subclass does not exist anymore, however a Dimension can have the role of measure dimension (i.e. a Dimension that contributes to the identification of the measures). In SDMX 2.1 a DataStructure could have zero or one MeasureDimensions, in the current version of the standard, from zero to many Dimension may have the role of measure dimension. Hereinafter a Dimension that plays the role of measure dimension is referenced for simplicity as “MeasureDimension“, i.e. maintaining the capital letters and the courier font even if the MeasureDimension is not anymore a class in the SDMX Information Model of the current SDMX version. For the sake of simplicity, the description below considers just one Dimension having the role of MeasureDimension (i.e., the more simple and common case). Nevertheless, it maintains its validity also if in the DataStructure there are more dimension with the role of MeasureDimensions: in this case what is said about the MeasureDimension must be applied to the combination of all the MeasureDimensions considered as a joint variable{{footnote}}E.g., if in the data structure there exist 3 Dimensions C,D,E having the role of MeasureDimension, they should be considered as a joint MeasureDimension Z=(C,D,E); therefore when the description says “each possible value Cj of the MeasureDimension …” it means “each possible combination of values (Cj, Dk, Ew) of the joint MeasureDimension Z=(C,D,E)”.{{/footnote}}.
224	224
225		-MeasureDimensions considered as a joint variable{{footnote}}E.g., if in the data structure there exist 3 Dimensions C,D,E having the role of MeasureDimension, they should be considered as a joint MeasureDimension Z=(C,D,E); therefore when the description says “each possible value Cj of the MeasureDimension …” it means “each possible combination of values (Cj, Dk, Ew) of the joint MeasureDimension Z=(C,D,E)”.{{/footnote}}.
226		-
227	227	Among other things, the Pivot method provides also backward compatibility with the SDMX 2.1 data structures that contained a MeasureDimension.
228	228
229	229	If applied to SDMX structures that do not contain any MeasureDimension, this method behaves like the Basic mapping (see the previous paragraph).
...	...	@@ -236,16 +236,18 @@
236	236	* The SDMX Measure is not mapped to VTL as well (it disappears in the VTL Data Structure);
237	237	* An SDMX DataAttribute is mapped in different ways according to its AttributeRelationship:
238	238	** 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;
239		-** 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.
240	240
241	241	The summary mapping table of the "pivot" mapping from SDMX to VTL for the SDMX data structures that contain a MeasureDimension is the following:
242	242
243		-|**SDMX**|**VTL**
244		-|Dimension|(Simple) Identifier
245		-|TimeDimension|(Time) Identifier
246		-|MeasureDimension & one Measure|One Measure for each Code of the SDMX MeasureDimension
247		-|DataAttribute not depending on the MeasureDimension|Attribute
248		-|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" %)(((
249	249	One Attribute for each Code of the
250	250	SDMX MeasureDimension
251	251	)))
...	...	@@ -255,19 +255,14 @@
255	255	At observation / data point level, calling Cj (j=1, … n) the j^^th^^ Code of the MeasureDimension:
256	256
257	257	* 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;
258		-* 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)
259		-
260		-Identifiers, (time) Identifier and Attributes.
261		-
	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.
262	262	* 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
263	263	* 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
264	264
265	265	==== 12.3.3.3 From SDMX DataAttributes to VTL Measures ====
266	266
267		-* 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.
268	268
269		-Attributes.
270		-
271	271	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.
272	272
273	273	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.
...	...	@@ -284,11 +284,12 @@
284	284
285	285	Mapping table:
286	286
287		-|**VTL**|**SDMX**
288		-|(Simple) Identifier|Dimension
289		-|(Time) Identifier|TimeDimension
290		-|Measure|Measure
291		-|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
292	292
293	293	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.
294	294
...	...	@@ -316,11 +316,12 @@
316	316
317	317	The summary mapping table of the **unpivot** mapping method is the following:
318	318
319		-|**VTL**|**SDMX**
320		-|(Simple) Identifier|Dimension
321		-|(Time) Identifier|TimeDimension
322		-|All Measure Components|MeasureDimension (having one Code for each VTL measure component) & one Measure
323		-|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
324	324
325	325	At observation / data point level:
326	326
...	...	@@ -342,12 +342,13 @@
342	342
343	343	The mapping table is the following:
344	344
345		-|VTL|SDMX
346		-|(Simple) Identifier|Dimension
347		-|(Time) Identifier|TimeDimension
348		-|Some Measures|Measure
349		-|Other Measures|DataAttribute
350		-|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
351	351
352	352	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.
353	353
...	...	@@ -385,11 +385,11 @@
385	385
386	386	Therefore, the generic name of this kind of VTL datasets would be:
387	387
388		-'DF(1.0.0)/INDICATORvalue.COUNTRYvalue'
	387	+> 'DF(1.0.0)/INDICATORvalue.COUNTRYvalue'
389	389
390	390	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:
391	391
392		-‘DF(1.0.0)/POPULATION.USA’
	391	+> ‘DF(1.0.0)/POPULATION.USA’
393	393
394	394	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.
395	395
...	...	@@ -403,26 +403,22 @@
403	403
404	404	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.
405	405
406		-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 …).
407	407
408		-basic, pivot …).
409		-
410	410	In the example above, for all the datasets of the kind
411	411
412		-‘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.
413	413
414	414	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:
415	415
416		-‘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	+> … … …
417	417
418		-DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA” ];
419		-
420		-‘DF1(1.0.0)/POPULATION.CANADA’ :=
421		-
422		-DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
423		-
424		-… … …
425		-
426	426	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}}
427	427
428	428	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.
...	...	@@ -431,10 +431,9 @@
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.’ :=
	429	+> ‘DF1(1.0.0)/POPULATION.’ :=
	430	+> DF1(1.0.0) [ sub INDICATOR=“POPULATION” ];
435	435
436		-DF1(1.0.0) [ sub INDICATOR=“POPULATION” ];
437		-
438	438	Therefore the VTL Data Set ‘DF1(1.0.0)/POPULATION.’ would have the identifiers COUNTRY and TIME_PERIOD.
439	439
440	440	Heterogeneous invocations of the same Dataflow are allowed, i.e. omitting different Dimensions in different invocations.
...	...	@@ -452,41 +452,38 @@
452	452
453	453	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}}
454	454
455		-‘DF2(1.0.0)/INDICATORvalue.COUNTRYvalue’ <- expression
	449	+> ‘DF2(1.0.0)/INDICATORvalue.COUNTRYvalue’ <- expression
456	456
457	457	Some examples follow, for some specific values of INDICATOR and COUNTRY:
458	458
459		-‘DF2(1.0.0)/GDPPERCAPITA.USA’ <- expression11; ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ <- expression12;
460		-… … …
	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	+> … … …
461	461
462		-‘DF2(1.0.0)/POPGROWTH.USA’ <- expression21;
463		-‘DF2(1.0.0)/POPGROWTH.CANADA’ <- expression22;
464		-… … …
465		-
466	466	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:
467	467
468	468	VTL dataset   INDICATOR value COUNTRY value
469	469
470		-‘DF2(1.0.0)/GDPPERCAPITA.USA’ GDPPERCAPITA USA
471		-‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ GDPPERCAPITA CANADA … … …
472		-‘DF2(1.0.0)/POPGROWTH.USA’  POPGROWTH USA
473		-‘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	+> … … …
474	474
475		-… … …
476		-
477	477	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:
478	478
479		-DF2bis_GDPPERCAPITA_USA := ‘DF2(1.0.0)/GDPPERCAPITA.USA’ [calc identifier INDICATOR := ”GDPPERCAPITA”, identifier COUNTRY := ”USA”];
480		-DF2bis_GDPPERCAPITA_CANADA := ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ [calc identifier INDICATOR:=”GDPPERCAPITA”, identifier COUNTRY:=”CANADA”]; … … …
481		-DF2bis_POPGROWTH_USA := ‘DF2(1.0.0)/POPGROWTH.USA’
482		-[calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”USA”];
483		-DF2bis_POPGROWTH_CANADA’ := ‘DF2(1.0.0)/POPGROWTH.CANADA’ [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”CANADA”]; … … …
484		-DF2(1.0) <- UNION  (DF2bis_GDPPERCAPITA_USA’,
485		-DF2bis_GDPPERCAPITA_CANADA’,
486		-… ,
487		-DF2bis_POPGROWTH_USA’,
488		-DF2bis_POPGROWTH_CANADA’
489		-…);
	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	+> …);
490	490
491	491	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.
492	492
...	...	@@ -498,25 +498,26 @@
498	498
499	499	With reference to the VTL “model for Variables and Value domains”, the following additional mappings have to be considered:
500	500
501		-|VTL|SDMX
502		-|**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^^
503		-|**Represented Variable**|**Concept** with a definite Representation
504		-|**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" %)(((
505	505	**Representation** (see the Structure
506	506	Pattern in the Base Package)
507	507	)))
508		-|**Enumerated Value Domain / Code List**|**Codelist**
509		-|**Code**|**Code** (for enumerated DimensionComponent, Measure, DataAttribute)
510		-|**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" %)(((
511	511	non-enumerated** Representation**
512	512	(having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
513	513	)))
514		-|**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
515		-| |to a valid **value **(for non-enumerated** **Representations)
516		-|**Value Domain Subset / Set**|This abstraction does not exist in SDMX
517		-|**Enumerated Value Domain Subset / Enumerated Set**|This abstraction does not exist in SDMX
518		-|**Described Value Domain Subset / Described Set**|This abstraction does not exist in SDMX
519		-|**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
520	520
521	521	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).
522	522
...	...	@@ -524,8 +524,10 @@
524	524
525	525	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
526	526
527		-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)
528	528
	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	+
529	529	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
530	530
531	531	Transformations to ensure that the VTL expressions are consistent with the actual representations of the correspondent SDMX Concepts.
...	...	@@ -540,8 +540,9 @@
540	540
541	541	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:
542	542
543		-[[image:1750067055028-964.png]]
544	544
	538	+[[image:1750070288958-132.png]]
	539	+
545	545	**Figure 22 – VTL Data Types**
546	546
547	547	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.
...	...	@@ -548,6 +548,8 @@
548	548
549	549	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):
550	550
	546	+[[image:1750070310572-584.png]]
	547	+
551	551	**Figure 23 – VTL Basic Scalar Types**
552	552
553	553	=== 12.4.2 VTL basic scalar types and SDMX data types ===
...	...	@@ -572,158 +572,157 @@
572	572
573	573	The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types.
574	574
575		-|SDMX data type (BasicComponentDataType)|Default VTL basic scalar type
576		-|(((
	572	+(% style="width:583.294px" %)
	573	+|(% style="width:360px" %)SDMX data type (BasicComponentDataType)|(% style="width:221px" %)Default VTL basic scalar type
	574	+|(% style="width:360px" %)(((
577	577	String
578	578	(string allowing any character)
579		-)))|string
580		-|(((
581		-Alpha
582		-
	577	+)))|(% style="width:221px" %)string
	578	+|(% style="width:360px" %)(((
	579	+Alpha
583	583	(string which only allows A-z)
584		-)))|string
585		-|(((
	581	+)))|(% style="width:221px" %)string
	582	+|(% style="width:360px" %)(((
586	586	AlphaNumeric
587	587	(string which only allows A-z and 0-9)
588		-)))|string
589		-|(((
	585	+)))|(% style="width:221px" %)string
	586	+|(% style="width:360px" %)(((
590	590	Numeric
591		-
592	592	(string which only allows 0-9, but is not numeric so that is can having leading zeros)
593		-)))|string
594		-|(((
	589	+)))|(% style="width:221px" %)string
	590	+|(% style="width:360px" %)(((
595	595	BigInteger
596	596	(corresponds to XML Schema xs:integer datatype; infinite set of integer values)
597		-)))|integer
598		-|(((
	593	+)))|(% style="width:221px" %)integer
	594	+|(% style="width:360px" %)(((
599	599	Integer
600	600	(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647
601	601	(inclusive))
602		-)))|integer
603		-|(((
	598	+)))|(% style="width:221px" %)integer
	599	+|(% style="width:360px" %)(((
604	604	Long
605	605	(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and
606	606	+9223372036854775807 (inclusive))
607		-)))|integer
608		-|(((
	603	+)))|(% style="width:221px" %)integer
	604	+|(% style="width:360px" %)(((
609	609	Short
610	610	(corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive))
611		-)))|integer
612		-|Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|number
613		-|(((
	607	+)))|(% style="width:221px" %)integer
	608	+|(% style="width:360px" %)Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|(% style="width:221px" %)number
	609	+|(% style="width:360px" %)(((
614	614	Float
615	615	(corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type)
616		-)))|number
617		-|(((
	612	+)))|(% style="width:221px" %)number
	613	+|(% style="width:360px" %)(((
618	618	Double
619	619	(corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type)
620		-)))|number
621		-|(((
	616	+)))|(% style="width:221px" %)number
	617	+|(% style="width:360px" %)(((
622	622	Boolean
623	623	(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of
624	624	binary-valued logic: {true, false})
625		-)))|boolean
626		-|(((
	621	+)))|(% style="width:221px" %)boolean
	622	+|(% style="width:360px" %)(((
627	627	URI
628	628	(corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference)
629		-)))|string
630		-|(((
	625	+)))|(% style="width:221px" %)string
	626	+|(% style="width:360px" %)(((
631	631	Count
632	632	(an integer following a sequential pattern, increasing by 1 for each occurrence)
633		-)))|integer
634		-|(((
	629	+)))|(% style="width:221px" %)integer
	630	+|(% style="width:360px" %)(((
635	635	InclusiveValueRange
636	636	(decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
637		-)))|number
638		-|(((
	633	+)))|(% style="width:221px" %)number
	634	+|(% style="width:360px" %)(((
639	639	ExclusiveValueRange
640	640	(decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
641		-)))|number
642		-|(((
	637	+)))|(% style="width:221px" %)number
	638	+|(% style="width:360px" %)(((
643	643	Incremental
644	644	(decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation)
645		-)))|number
646		-|(((
	641	+)))|(% style="width:221px" %)number
	642	+|(% style="width:360px" %)(((
647	647	ObservationalTimePeriod
648	648	(superset of StandardTimePeriod and TimeRange)
649		-)))|time
650		-|(((
	645	+)))|(% style="width:221px" %)time
	646	+|(% style="width:360px" %)(((
651	651	StandardTimePeriod
652	652	(superset of BasicTimePeriod and ReportingTimePeriod)
653		-)))|time
654		-|(((
	649	+)))|(% style="width:221px" %)time
	650	+|(% style="width:360px" %)(((
655	655	BasicTimePeriod
656	656	(superset of GregorianTimePeriod and DateTime)
657		-)))|date
658		-|(((
	653	+)))|(% style="width:221px" %)date
	654	+|(% style="width:360px" %)(((
659	659	GregorianTimePeriod
660	660	(superset of GregorianYear, GregorianYearMonth, and GregorianDay)
661		-)))|date
662		-|GregorianYear (YYYY)|date
663		-|GregorianYearMonth / GregorianMonth (YYYY-MM)|date
664		-|GregorianDay (YYYY-MM-DD)|date
665		-|(((
	657	+)))|(% style="width:221px" %)date
	658	+|(% style="width:360px" %)GregorianYear (YYYY)|(% style="width:221px" %)date
	659	+|(% style="width:360px" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% style="width:221px" %)date
	660	+|(% style="width:360px" %)GregorianDay (YYYY-MM-DD)|(% style="width:221px" %)date
	661	+|(% style="width:360px" %)(((
666	666	ReportingTimePeriod
667	667	(superset of RepostingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, ReportingDay)
668		-)))|time_period
669		-|(((
	664	+)))|(% style="width:221px" %)time_period
	665	+|(% style="width:360px" %)(((
670	670	ReportingYear
671	671	(YYYY-A1 – 1 year period)
672		-)))|time_period
673		-|(((
	668	+)))|(% style="width:221px" %)time_period
	669	+|(% style="width:360px" %)(((
674	674	ReportingSemester
675	675	(YYYY-Ss – 6 month period)
676		-)))|time_period
677		-|(((
	672	+)))|(% style="width:221px" %)time_period
	673	+|(% style="width:360px" %)(((
678	678	ReportingTrimester
679	679	(YYYY-Tt – 4 month period)
680		-)))|time_period
681		-|(((
	676	+)))|(% style="width:221px" %)time_period
	677	+|(% style="width:360px" %)(((
682	682	ReportingQuarter
683	683	(YYYY-Qq – 3 month period)
684		-)))|time_period
685		-|(((
	680	+)))|(% style="width:221px" %)time_period
	681	+|(% style="width:360px" %)(((
686	686	ReportingMonth
687	687	(YYYY-Mmm – 1 month period)
688		-)))|time_period
689		-|ReportingWeek|time_period
690		-| (YYYY-Www – 7 day period; following ISO 8601 definition of a week in a year)|
691		-|(((
	684	+)))|(% style="width:221px" %)time_period
	685	+|(% style="width:360px" %)ReportingWeek|(% style="width:221px" %)time_period
	686	+|(% style="width:360px" %) (YYYY-Www – 7 day period; following ISO 8601 definition of a week in a year)|(% style="width:221px" %)
	687	+|(% style="width:360px" %)(((
692	692	ReportingDay
693	693	(YYYY-Dddd – 1 day period)
694		-)))|time_period
695		-|(((
	690	+)))|(% style="width:221px" %)time_period
	691	+|(% style="width:360px" %)(((
696	696	DateTime
697	697	(YYYY-MM-DDThh:mm:ss)
698		-)))|date
699		-|(((
	694	+)))|(% style="width:221px" %)date
	695	+|(% style="width:360px" %)(((
700	700	TimeRange
701	701	(YYYY-MM-DD(Thh:mm:ss)?/<duration>)
702		-)))|time
703		-|(((
	698	+)))|(% style="width:221px" %)time
	699	+|(% style="width:360px" %)(((
704	704	Month
705	705	(~-~-MM; speicifies a month independent of a year; e.g. February is black history month in the United States)
706		-)))|string
707		-|(((
	702	+)))|(% style="width:221px" %)string
	703	+|(% style="width:360px" %)(((
708	708	MonthDay
709	709	(~-~-MM-DD; specifies a day within a month independent of a year; e.g. Christmas is December 25^^th^^; used to specify reporting year start day)
710		-)))|string
711		-|(((
	706	+)))|(% style="width:221px" %)string
	707	+|(% style="width:360px" %)(((
712	712	Day
713	713	(~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday)
714		-)))|string
715		-|(((
	710	+)))|(% style="width:221px" %)string
	711	+|(% style="width:360px" %)(((
716	716	Time
717	717	(hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM)
718		-)))|string
719		-|(((
	714	+)))|(% style="width:221px" %)string
	715	+|(% style="width:360px" %)(((
720	720	Duration
721	721	(corresponds to XML Schema xs:duration datatype)
722		-)))|duration
723		-|XHTML|Metadata type – not applicable
724		-|KeyValues|Metadata type – not applicable
725		-|IdentifiableReference|Metadata type – not applicable
726		-|DataSetReference|Metadata type – not applicable
	718	+)))|(% style="width:221px" %)duration
	719	+|(% style="width:360px" %)XHTML|(% style="width:221px" %)Metadata type – not applicable
	720	+|(% style="width:360px" %)KeyValues|(% style="width:221px" %)Metadata type – not applicable
	721	+|(% style="width:360px" %)IdentifiableReference|(% style="width:221px" %)Metadata type – not applicable
	722	+|(% style="width:360px" %)DataSetReference|(% style="width:221px" %)Metadata type – not applicable
727	727
728	728	**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
729	729
...	...	@@ -733,84 +733,82 @@
733	733
734	734	The following table describes the default conversion from the VTL basic scalar types to the SDMX data types .
735	735
736		-|(((
737		-VTL basic
738		-scalar type
739		-)))|(((
	732	+(% style="width:748.294px" %)
	733	+|(% style="width:164px" %)(((
	734	+VTL basic scalar type
	735	+)))|(% style="width:304px" %)(((
740	740	Default SDMX data type
741		-(BasicComponentDataType
742		-)
743		-)))|Default output format
744		-|String|String|Like XML (xs:string)
745		-|Number|Float|Like XML (xs:float)
746		-|Integer|Integer|Like XML (xs:int)
747		-|Date|DateTime|YYYY-MM-DDT00:00:00Z
748		-|Time|StandardTimePeriod|<date>/<date> (as defined above)
749		-|time_period|(((
	737	+(BasicComponentDataType)
	738	+)))|(% style="width:277px" %)Default output format
	739	+|(% style="width:164px" %)String|(% style="width:304px" %)String|(% style="width:277px" %)Like XML (xs:string)
	740	+|(% style="width:164px" %)Number|(% style="width:304px" %)Float|(% style="width:277px" %)Like XML (xs:float)
	741	+|(% style="width:164px" %)Integer|(% style="width:304px" %)Integer|(% style="width:277px" %)Like XML (xs:int)
	742	+|(% style="width:164px" %)Date|(% style="width:304px" %)DateTime|(% style="width:277px" %)YYYY-MM-DDT00:00:00Z
	743	+|(% style="width:164px" %)Time|(% style="width:304px" %)StandardTimePeriod|(% style="width:277px" %)<date>/<date> (as defined above)
	744	+|(% style="width:164px" %)time_period|(% style="width:304px" %)(((
750	750	ReportingTimePeriod
751	751	(StandardReportingPeriod)
752		-)))|(((
	747	+)))|(% style="width:277px" %)(((
753	753	YYYY-Pppp
754	754	(according to SDMX )
755	755	)))
756		-|Duration|Duration|Like XML (xs:duration) PnYnMnDTnHnMnS
757		-|Boolean|Boolean|Like XML (xs:boolean) with the values "true" or "false"
	751	+|(% style="width:164px" %)Duration|(% style="width:304px" %)Duration|(% style="width:277px" %)Like XML (xs:duration) PnYnMnDTnHnMnS
	752	+|(% style="width:164px" %)Boolean|(% style="width:304px" %)Boolean|(% style="width:277px" %)Like XML (xs:boolean) with the values "true" or "false"
758	758
759	759	**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
760	760
761		-In case a different default conversion is desired, it can be achieved through the CustomTypeScheme and CustomType artefacts (see also the section
	756	+In case a different default conversion is desired, it can be achieved through the CustomTypeScheme and CustomType artefacts (see also the section Transformations and Expressions of the SDMX information model).
762	762
763		-Transformations and Expressions of the SDMX information model).
764		-
765	765	The custom output formats can be specified by means of the VTL formatting mask described in the section "Type Conversion and Formatting Mask" of the VTL Reference Manual. Such a section describes the masks for the VTL basic scalar types "number", "integer", "date", "time", "time_period" and "duration" and gives examples. As for the types "string" and "boolean" the VTL conventions are extended with some other special characters as described in the following table.
766	766
767		-|(% colspan="2" %)VTL special characters for the formatting masks
768		-|(% colspan="2" %)
769		-|(% colspan="2" %)Number
770		-|D|one numeric digit (if the scientific notation is adopted, D is only for the mantissa)
771		-|E|one numeric digit (for the exponent of the scientific notation)
772		-|. (dot)|possible separator between the integer and the decimal parts.
773		-|, (comma)|possible separator between the integer and the decimal parts.
774		-| |
775		-|(% colspan="2" %)Time and duration
776		-|C|century
777		-|Y|year
778		-|S|semester
779		-|Q|quarter
780		-|M|month
781		-|W|week
782		-|D|day
783		-|h|hour digit (by default on 24 hours)
784		-|M|minute
785		-|S|second
786		-|D|decimal of second
787		-|P|period indicator (representation in one digit for the duration)
788		-|P|number of the periods specified in the period indicator
789		-|AM/PM|indicator of AM / PM (e.g. am/pm for "am" or "pm")
790		-|MONTH|uppercase textual representation of the month (e.g., JANUARY for January)
791		-|DAY|uppercase textual representation of the day (e.g., MONDAY for Monday)
792		-|Month|lowercase textual representation of the month (e.g., january)
793		-|Day|lowercase textual representation of the month (e.g., monday)
794		-|Month|First character uppercase, then lowercase textual representation of the month (e.g., January)
795		-|Day|First character uppercase, then lowercase textual representation of the day using (e.g. Monday)
796		-| |
797		-|(% colspan="2" %)String
798		-|X|any string character
799		-|Z|any string character from "A" to "z"
800		-|9|any string character from "0" to "9"
801		-| |
802		-|(% colspan="2" %)Boolean
803		-|B|Boolean using "true" for True and "false" for False
804		-|1|Boolean using "1" for True and "0" for False
805		-|0|Boolean using "0" for True and "1" for False
806		-| |
807		-|(% colspan="2" %)Other qualifiers
808		-|*|an arbitrary number of digits (of the preceding type)
809		-|+|at least one digit (of the preceding type)
810		-|( )|optional digits (specified within the brackets)
811		-|\|prefix for the special characters that must appear in the mask
812		-|N|fixed number of digits used in the preceding textual representation of the month or the day
813		-| |
	760	+(% style="width:717.294px" %)
	761	+|(% colspan="2" style="width:714px" %)VTL special characters for the formatting masks
	762	+|(% colspan="2" style="width:714px" %)
	763	+|(% colspan="2" style="width:714px" %)Number
	764	+|(% style="width:122px" %)D|(% style="width:591px" %)one numeric digit (if the scientific notation is adopted, D is only for the mantissa)
	765	+|(% style="width:122px" %)E|(% style="width:591px" %)one numeric digit (for the exponent of the scientific notation)
	766	+|(% style="width:122px" %). (dot)|(% style="width:591px" %)possible separator between the integer and the decimal parts.
	767	+|(% style="width:122px" %), (comma)|(% style="width:591px" %)possible separator between the integer and the decimal parts.
	768	+|(% style="width:122px" %) |(% style="width:591px" %)
	769	+|(% colspan="2" style="width:714px" %)Time and duration
	770	+|(% style="width:122px" %)C|(% style="width:591px" %)century
	771	+|(% style="width:122px" %)Y|(% style="width:591px" %)year
	772	+|(% style="width:122px" %)S|(% style="width:591px" %)semester
	773	+|(% style="width:122px" %)Q|(% style="width:591px" %)quarter
	774	+|(% style="width:122px" %)M|(% style="width:591px" %)month
	775	+|(% style="width:122px" %)W|(% style="width:591px" %)week
	776	+|(% style="width:122px" %)D|(% style="width:591px" %)day
	777	+|(% style="width:122px" %)h|(% style="width:591px" %)hour digit (by default on 24 hours)
	778	+|(% style="width:122px" %)M|(% style="width:591px" %)minute
	779	+|(% style="width:122px" %)S|(% style="width:591px" %)second
	780	+|(% style="width:122px" %)D|(% style="width:591px" %)decimal of second
	781	+|(% style="width:122px" %)P|(% style="width:591px" %)period indicator (representation in one digit for the duration)
	782	+|(% style="width:122px" %)P|(% style="width:591px" %)number of the periods specified in the period indicator
	783	+|(% style="width:122px" %)AM/PM|(% style="width:591px" %)indicator of AM / PM (e.g. am/pm for "am" or "pm")
	784	+|(% style="width:122px" %)MONTH|(% style="width:591px" %)uppercase textual representation of the month (e.g., JANUARY for January)
	785	+|(% style="width:122px" %)DAY|(% style="width:591px" %)uppercase textual representation of the day (e.g., MONDAY for Monday)
	786	+|(% style="width:122px" %)Month|(% style="width:591px" %)lowercase textual representation of the month (e.g., january)
	787	+|(% style="width:122px" %)Day|(% style="width:591px" %)lowercase textual representation of the month (e.g., monday)
	788	+|(% style="width:122px" %)Month|(% style="width:591px" %)First character uppercase, then lowercase textual representation of the month (e.g., January)
	789	+|(% style="width:122px" %)Day|(% style="width:591px" %)First character uppercase, then lowercase textual representation of the day using (e.g. Monday)
	790	+|(% style="width:122px" %) |(% style="width:591px" %)
	791	+|(% colspan="2" style="width:714px" %)String
	792	+|(% style="width:122px" %)X|(% style="width:591px" %)any string character
	793	+|(% style="width:122px" %)Z|(% style="width:591px" %)any string character from "A" to "z"
	794	+|(% style="width:122px" %)9|(% style="width:591px" %)any string character from "0" to "9"
	795	+|(% style="width:122px" %) |(% style="width:591px" %)
	796	+|(% colspan="2" style="width:714px" %)Boolean
	797	+|(% style="width:122px" %)B|(% style="width:591px" %)Boolean using "true" for True and "false" for False
	798	+|(% style="width:122px" %)1|(% style="width:591px" %)Boolean using "1" for True and "0" for False
	799	+|(% style="width:122px" %)0|(% style="width:591px" %)Boolean using "0" for True and "1" for False
	800	+|(% style="width:122px" %) |(% style="width:591px" %)
	801	+|(% colspan="2" style="width:714px" %)Other qualifiers
	802	+|(% style="width:122px" %)*|(% style="width:591px" %)an arbitrary number of digits (of the preceding type)
	803	+|(% style="width:122px" %)+|(% style="width:591px" %)at least one digit (of the preceding type)
	804	+|(% style="width:122px" %)( )|(% style="width:591px" %)optional digits (specified within the brackets)
	805	+|(% style="width:122px" %)\|(% style="width:591px" %)prefix for the special characters that must appear in the mask
	806	+|(% style="width:122px" %)N|(% style="width:591px" %)fixed number of digits used in the preceding textual representation of the month or the day
	807	+|(% style="width:122px" %) |(% style="width:591px" %)
814	814
815	815	The default conversion, either standard or customized, can be used to deduce automatically the representation of the components of the result of a VTL Transformation. In alternative, the representation of the resulting SDMX Dataflow can be given explicitly by providing its DataStructureDefinition. In other words, the representation specified in the DSD, if available, overrides any default conversion{{footnote}}The representation given in the DSD should obviously be compatible with the VTL data type.{{/footnote}}.
816	816

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