Changes for page 12 Validation and Transformation Language (VTL)

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14	14
15	15	The VTL language can be applied to SDMX artefacts by mapping the SDMX IM model artefacts to the model artefacts that VTL can manipulate{{footnote}}In this chapter, in order to distinguish VTL and SDMX model artefacts, the VTL ones are written in the Arial font while the SDMX ones in Courier New{{/footnote}}. Thus, the SDMX artefacts can be used in VTL as inputs and/or outputs of Transformations. It is important to be aware that the artefacts do not always have the same names in the SDMX and VTL IMs, nor do they always have the same meaning. The more evident example is given by the SDMX Dataset and the VTL "Data Set", which do not correspond one another: as a matter of fact, the VTL "Data Set" maps to the SDMX "Dataflow", while the SDMX "Dataset" has no explicit mapping to VTL (such an abstraction is not needed in the definition of VTL Transformations). A SDMX "Dataset", however, is an instance of a SDMX "Dataflow" and can be the artefact on which the VTL transformations are executed (i.e., the Transformations are defined on Dataflows and are applied to Dataflow instances that can be Datasets).
16	16
17		-The VTL programs (Transformation Schemes) are represented in SDMX through the TransformationScheme maintainable class which is composed of Transformation (nameable artefact). Each Transformation assigns the outcome of the evaluation of a VTL expression to a result.
	17	+The VTL programs (Transformation Schemes) are represented in SDMX through the TransformationScheme maintainable class which is composed of
18	18
	19	+Transformation (nameable artefact). Each Transformation assigns the outcome of the evaluation of a VTL expression to a result.
	20	+
19	19	This section does not explain the VTL language or any of the content published in the VTL guides. Rather, this is a description of how the VTL can be used in the SDMX context and applied to SDMX artefacts.
20	20
21	21	== 12.2 References to SDMX artefacts from VTL statements ==
...	...	@@ -26,8 +26,10 @@
26	26
27	27	The alias of an SDMX artefact can be its URN (Universal Resource Name), an abbreviation of its URN or another user-defined name.
28	28
29		-In any case, the aliases used in the VTL Transformations have to be mapped to the SDMX artefacts through the VtlMappingScheme and VtlMapping classes (see the section of the SDMX IM relevant to the VTL). A VtlMapping allows specifying the aliases to be used in the VTL Transformations, Rulesets{{footnote}}See also the section "VTL-DL Rulesets" in the VTL Reference Manual.{{/footnote}} or User Defined Operators{{footnote}}The VTLMappings are used also for User Defined Operators (UDO). Although UDOs are envisaged to be defined on generic operands, so that the specific artefacts to be manipulated are passed as parameters at their invocation, it is also possible that an UDO invokes directly some specific SDMX artefacts. These SDMX artefacts have to be mapped to the corresponding aliases used in the definition of the UDO through the VtlMappingScheme and VtlMapping classes as well.{{/footnote}} to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping.
	31	+In any case, the aliases used in the VTL Transformations have to be mapped to the
30	30
	33	+SDMX artefacts through the VtlMappingScheme and VtlMapping classes (see the section of the SDMX IM relevant to the VTL). A VtlMapping allows specifying the aliases to be used in the VTL Transformations, Rulesets{{footnote}}See also the section "VTL-DL Rulesets" in the VTL Reference Manual.{{/footnote}} or User Defined Operators{{footnote}}The VTLMappings are used also for User Defined Operators (UDO). Although UDOs are envisaged to be defined on generic operands, so that the specific artefacts to be manipulated are passed as parameters at their invocation, it is also possible that an UDO invokes directly some specific SDMX artefacts. These SDMX artefacts have to be mapped to the corresponding aliases used in the definition of the UDO through the VtlMappingScheme and VtlMapping classes as well.{{/footnote}} to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping.
	34	+
31	31	The correspondence between an alias and a SDMX artefact must be one-to-one, meaning that a generic alias identifies one and just one SDMX artefact while a SDMX artefact is identified by one and just one alias. In other words, within a VtlMappingScheme an artefact can have just one alias and different artefacts cannot have the same alias.
32	32
33	33	The references through the URN and the abbreviated URN are described in the following paragraphs.
...	...	@@ -198,7 +198,7 @@
198	198
199	199	=== 12.3.3 Mapping from SDMX to VTL data structures ===
200	200
201		-==== 12.3.3.1 Basic Mapping ====
	205	+**12.3.3.1 Basic Mapping**
202	202
203	203	The main mapping method from SDMX to VTL is called **Basic **mapping. This is considered as the default mapping method and is applied unless a different method is specified through the VtlMappingScheme and VtlDataflowMapping classes. 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:
204	204
...	...	@@ -211,43 +211,55 @@
211	211
212	212	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).
213	213
214		-With the Basic mapping, one SDMX observation{{footnote}}Here an SDMX observation is meant to correspond to one combination of values of the DimensionComponents.{{/footnote}} generates one VTL data point.
	218	+With the Basic mapping, one SDMX observation^^27^^ generates one VTL data point.
215	215
216		-==== 12.3.3.2 Pivot Mapping ====
	220	+**12.3.3.2 Pivot Mapping**
217	217
218	218	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.
219	219
220		-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	+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
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	226	+MeasureDimensions considered as a joint variable^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^24^^>>path:#sdfootnote24sym||name="sdfootnote24anc"]](%%)^^.
	227	+
222	222	Among other things, the Pivot method provides also backward compatibility with the SDMX 2.1 data structures that contained a MeasureDimension.
223	223
224	224	If applied to SDMX structures that do not contain any MeasureDimension, this method behaves like the Basic mapping (see the previous paragraph).
225	225
226		-Here an SDMX observation is meant to correspond to one combination of values of the DimensionComponents.
	232	+^^27^^ Here an SDMX observation is meant to correspond to one combination of values of the DimensionComponents.
227	227
228	228	The SDMX structures that contain a MeasureDimension are mapped as described below (this mapping is equivalent to a pivoting operation):
229	229
230	230	* A SDMX simple dimension becomes a VTL (simple) identifier and a SDMX TimeDimension becomes a VTL (time) identifier;
231		-* Each possible Code Cj of the SDMX MeasureDimension is mapped to a VTL Measure, having the same name as the SDMX Code (i.e. Cj); the VTL Measure Cj is a new VTL component even if the SDMX data structure has not such a Component;
	237	+* Each possible Code Cj of the SDMX MeasureDimension is mapped to a VTL Measure, having the same name as the SDMX Code (i.e. Cj); the VTL Measure Cj is a new VTL component even if the SDMX data structure has not such a
	238	+
	239	+Component;
	240	+
232	232	* The SDMX MeasureDimension is not mapped to VTL (it disappears in the VTL Data Structure);
233	233	* The SDMX Measure is not mapped to VTL as well (it disappears in the VTL Data Structure);
234	234	* An SDMX DataAttribute is mapped in different ways according to its AttributeRelationship:
235		-** 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;
	244	+** 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
	245	+
	246	+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;
	247	+
	248	+*
236	236	** 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).
237	237	** 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	238
239	239	The summary mapping table of the "pivot" mapping from SDMX to VTL for the SDMX data structures that contain a MeasureDimension is the following:
240	240
241		-(% style="width:769.294px" %)
242		-|(% style="width:401px" %)**SDMX**|(% style="width:366px" %)**VTL**
243		-|(% style="width:401px" %)Dimension|(% style="width:366px" %)(Simple) Identifier
244		-|(% style="width:401px" %)TimeDimension|(% style="width:366px" %)(Time) Identifier
245		-|(% style="width:401px" %)MeasureDimension & one Measure|(% style="width:366px" %)(((
246		-One Measure for each Code of the SDMX MeasureDimension
	254	+|**SDMX**|**VTL**
	255	+|Dimension|(Simple) Identifier
	256	+|TimeDimension|(Time) Identifier
	257	+|MeasureDimension & one Measure|(((
	258	+One Measure for each Code of the
	259	+
	260	+SDMX MeasureDimension
247	247	)))
248		-|(% style="width:401px" %)DataAttribute not depending on the MeasureDimension|(% style="width:366px" %)Attribute
249		-|(% style="width:401px" %)DataAttribute depending on the MeasureDimension|(% style="width:366px" %)(((
250		-One Attribute for each Code of the SDMX MeasureDimension
	262	+|DataAttribute not depending on the MeasureDimension|Attribute
	263	+|DataAttribute depending on the MeasureDimension|(((
	264	+One Attribute for each Code of the
	265	+
	266	+SDMX MeasureDimension
251	251	)))
252	252
253	253	Using this mapping method, the components of the data structure can change in the conversion from SDMX to VTL and it must be taken into account that the VTL statements can reference only the components of the resulting VTL data structure.
...	...	@@ -255,11 +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) Identifiers, (time) Identifier and Attributes.
	274	+* 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)
	275	+
	276	+Identifiers, (time) Identifier and Attributes.
	277	+
259	259	* 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
260	260	* 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
261	261
262		-==== 12.3.3.3 From SDMX DataAttributes to VTL Measures ====
	281	+**12.3.3.3 From SDMX DataAttributes to VTL Measures**
263	263
264	264	* 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	265
...	...	@@ -271,7 +271,7 @@
271	271
272	272	=== 12.3.4 Mapping from VTL to SDMX data structures ===
273	273
274		-==== 12.3.4.1 Basic Mapping ====
	293	+**12.3.4.1 Basic Mapping**
275	275
276	276	The main mapping method **from VTL to SDMX** is called **Basic **mapping as well.
277	277
...	...	@@ -281,12 +281,11 @@
281	281
282	282	Mapping table:
283	283
284		-(% style="width:667.294px" %)
285		-|(% style="width:272px" %)**VTL**|(% style="width:392px" %)**SDMX**
286		-|(% style="width:272px" %)(Simple) Identifier|(% style="width:392px" %)Dimension
287		-|(% style="width:272px" %)(Time) Identifier|(% style="width:392px" %)TimeDimension
288		-|(% style="width:272px" %)Measure|(% style="width:392px" %)Measure
289		-|(% style="width:272px" %)Attribute|(% style="width:392px" %)DataAttribute
	303	+|**VTL**|**SDMX**
	304	+|(Simple) Identifier|Dimension
	305	+|(Time) Identifier|TimeDimension
	306	+|Measure|Measure
	307	+|Attribute|DataAttribute
290	290
291	291	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.
292	292
...	...	@@ -296,7 +296,7 @@
296	296
297	297	As said, 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.
298	298
299		-==== 12.3.4.2 Unpivot Mapping ====
	317	+**12.3.4.2 Unpivot Mapping**
300	300
301	301	An alternative mapping method from VTL to SDMX is the **Unpivot **mapping.
302	302
...	...	@@ -320,12 +320,11 @@
320	320
321	321	The summary mapping table of the **unpivot** mapping method is the following:
322	322
323		-(% style="width:994.294px" %)
324		-|(% style="width:306px" %)**VTL**|(% style="width:684px" %)**SDMX**
325		-|(% style="width:306px" %)(Simple) Identifier|(% style="width:684px" %)Dimension
326		-|(% style="width:306px" %)(Time) Identifier|(% style="width:684px" %)TimeDimension
327		-|(% style="width:306px" %)All Measure Components|(% style="width:684px" %)MeasureDimension (having one Code for each VTL measure component) & one Measure
328		-|(% style="width:306px" %)Attribute|(% style="width:684px" %)DataAttribute depending on all SDMX Dimensions including the TimeDimension and except the MeasureDimension
	341	+|**VTL**|**SDMX**
	342	+|(Simple) Identifier|Dimension
	343	+|(Time) Identifier|TimeDimension
	344	+|All Measure Components|MeasureDimension (having one Code for each VTL measure component) & one Measure
	345	+|Attribute|DataAttribute depending on all SDMX Dimensions including the TimeDimension and except the MeasureDimension
329	329
330	330	At observation / data point level:
331	331
...	...	@@ -339,7 +339,7 @@
339	339
340	340	In any case, the resulting SDMX definitions must be compliant with the SDMX consistency rules. For example, the possible Codes of the SDMX MeasureDimension need to be listed in a SDMX Codelist, with proper id, agency and version; moreover, the SDMX DSD must have the AttributeRelationship for the DataAttributes, which does not exist in VTL.
341	341
342		-==== 12.3.4.3 From VTL Measures to SDMX Data Attributes ====
	359	+**12.3.4.3 From VTL Measures to SDMX Data Attributes**
343	343
344	344	More than all for the multi-measure VTL structures (having more than one Measure Component), it may happen that the Measures of the VTL Data Structure need to be managed as DataAttributes in SDMX. Therefore, a third mapping method consists in transforming some VTL measures in a corresponding SDMX Measures and all the other VTL Measures in SDMX DataAttributes. This method is called M2A (“M2A” stands for “Measures to DataAttributes”).
345	345
...	...	@@ -347,13 +347,12 @@
347	347
348	348	The mapping table is the following:
349	349
350		-(% style="width:689.294px" %)
351		-|(% style="width:344px" %)**VTL**|(% style="width:341px" %)**SDMX**
352		-|(% style="width:344px" %)(Simple) Identifier|(% style="width:341px" %)Dimension
353		-|(% style="width:344px" %)(Time) Identifier|(% style="width:341px" %)TimeDimension
354		-|(% style="width:344px" %)Some Measures|(% style="width:341px" %)Measure
355		-|(% style="width:344px" %)Other Measures|(% style="width:341px" %)DataAttribute
356		-|(% style="width:344px" %)Attribute|(% style="width:341px" %)DataAttribute
	367	+|VTL|SDMX
	368	+|(Simple) Identifier|Dimension
	369	+|(Time) Identifier|TimeDimension
	370	+|Some Measures|Measure
	371	+|Other Measures|DataAttribute
	372	+|Attribute|DataAttribute
357	357
358	358	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.
359	359
...	...	@@ -371,20 +371,20 @@
371	371
372	372	Until now it has been assumed to map one SMDX Dataflow to one VTL Data Set and vice-versa. This mapping one-to-one is not mandatory according to VTL because a VTL Data Set is meant to be a set of observations (data points) on a logical plane, having the same logical data structure and the same general meaning, independently of the possible physical representation or storage (see VTL 2.0 User Manual page 24), therefore a SDMX Dataflow can be seen either as a unique set of data observations (corresponding to one VTL Data Set) or as the union of many sets of data observations (each one corresponding to a distinct VTL Data Set).
373	373
374		-As a matter of fact, in some cases it can be useful to define VTL operations involving definite parts of a SDMX Dataflow instead than the whole.{{footnote}}A typical example of this kind is the validation, and more in general the manipulation, of individual time series belonging to the same Dataflow, identifiable through the DimensionComponents of the Dataflow except the TimeDimension. The coding of these kind of operations might be simplified by mapping distinct time series (i.e. different parts of a SDMX Dataflow) to distinct VTL Data Sets.{{/footnote}}
	390	+As a matter of fact, in some cases it can be useful to define VTL operations involving definite parts of a SDMX Dataflow instead than the whole.^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^25^^>>path:#sdfootnote25sym||name="sdfootnote25anc"]](%%)^^
375	375
376		-Therefore, in order to make the coding of VTL operations simpler when applied on parts of SDMX Dataflows, it is allowed to map distinct parts of a SDMX Dataflow to distinct VTL Data Sets according to the following rules and conventions. This kind of mapping is possible both from SDMX to VTL and from VTL to SDMX, as better explained below.{{footnote}}Please note that this kind of mapping is only an option at disposal of the definer of VTL Transformations; in fact it remains always possible to manipulate the needed parts of SDMX Dataflows by means of VTL operators (e.g. “sub”, “filter”, “calc”, “union” …), maintaining a mapping one-to-one between SDMX Dataflows and VTL Data Sets.{{/footnote}}
	392	+Therefore, in order to make the coding of VTL operations simpler when applied on parts of SDMX Dataflows, it is allowed to map distinct parts of a SDMX Dataflow to distinct VTL Data Sets according to the following rules and conventions. This kind of mapping is possible both from SDMX to VTL and from VTL to SDMX, as better explained below.^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^26^^>>path:#sdfootnote26sym||name="sdfootnote26anc"]](%%)^^
377	377
378	378	Given a SDMX Dataflow and some predefined Dimensions of its DataStructure, it is allowed to map the subsets of observations that have the same combination of values for such Dimensions to correspondent VTL datasets.
379	379
380	380	For example, assuming that the SDMX Dataflow DF1(1.0.0) has the Dimensions INDICATOR, TIME_PERIOD and COUNTRY, and that the user declares the Dimensions INDICATOR and COUNTRY as basis for the mapping (i.e. the mapping dimensions): the observations that have the same values for INDICATOR and COUNTRY would be mapped to the same VTL dataset (and vice-versa). In practice, this kind mapping is obtained like follows:
381	381
382		-* For a given SDMX Dataflow, the user (VTL definer) declares the DimensionComponents on which the mapping will be based, in a given order.{{footnote}}This definition is made through the ToVtlSubspace and ToVtlSpaceKey classes and/or the FromVtlSuperspace and FromVtlSpaceKey classes, depending on the direction of the mapping (“key” means “dimension”). The mapping of Dataflow subsets can be applied independently in the two directions, also according to different Dimensions. When no Dimension is declared for a given direction, it is assumed that the option of mapping different parts of a SDMX Dataflow to different VTL Data Sets is not used.{{/footnote}} Following the example above, imagine that the user declares the Dimensions INDICATOR and COUNTRY.
	398	+* For a given SDMX Dataflow, the user (VTL definer) declares the DimensionComponents on which the mapping will be based, in a given order.^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^27^^>>path:#sdfootnote27sym||name="sdfootnote27anc"]](%%)^^ Following the example above, imagine that the user declares the Dimensions INDICATOR and COUNTRY.
383	383	* The VTL Data Set is given a name using a special notation also called “ordered concatenation” and composed of the following parts:
384	384	** The reference to the SDMX Dataflow (expressed according to the rules described in the previous paragraphs, i.e. URN, abbreviated URN or another alias); for example DF(1.0.0);
385		-** a slash (“/”) as a separator;{{footnote}}As a consequence of this formalism, a slash in the name of the VTL Data Set assumes the specific meaning of separator between the name of the Dataflow and the values of some of its Dimensions.{{/footnote}}
	401	+** a slash (“/”) as a separator; ^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^28^^>>path:#sdfootnote28sym||name="sdfootnote28anc"]](%%)^^
386	386
387		-The reference to a specific part of the SDMX Dataflow above, expressed as the concatenation of the values that the SDMX DimensionComponents declared above must have, separated by dots (“.”) and written in the order in which these DimensionComponents are defined{{footnote}}This is the order in which the dimensions are defined in the ToVtlSpaceKey class or in the FromVtlSpaceKey class, depending on the direction of the mapping.{{/footnote}}. For example POPULATION.USA would mean that such a VTL Data Set is mapped to the SDMX observations for which the dimension //INDICATOR// is equal to POPULATION and the dimension //COUNTRY// is equal to USA.
	403	+The reference to a specific part of the SDMX Dataflow above, expressed as the concatenation of the values that the SDMX DimensionComponents declared above must have, separated by dots (“.”) and written in the order in which these DimensionComponents are defined^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^29^^>>path:#sdfootnote29sym||name="sdfootnote29anc"]](%%)^^. For example POPULATION.USA would mean that such a VTL Data Set is mapped to the SDMX observations for which the dimension //INDICATOR// is equal to POPULATION and the dimension //COUNTRY// is equal to USA.
388	388
389	389	In the VTL Transformations, this kind of dataset name must be referenced between single quotes because the slash (“/”) is not a regular character according to the VTL rules.
390	390
...	...	@@ -400,7 +400,7 @@
400	400
401	401	Let us now analyse the different meaning of this kind of mapping in the two mapping directions, i.e. from SDMX to VTL and from VTL to SDMX.
402	402
403		-As already said, the mapping from SDMX to VTL happens when the SDMX dataflows are operand of VTL Transformations, instead the mapping from VTL to SDMX happens when the VTL Data Sets that is result of Transformations{{footnote}}It should be remembered that, according to the VTL consistency rules, a given VTL dataset cannot be the result of more than one VTL Transformation.{{/footnote}} need to be treated as SDMX objects. This kind of mapping can be applied independently in the two directions and the Dimensions on which the mapping is based can be different in the two directions: these Dimensions are defined in the ToVtlSpaceKey and in the FromVtlSpaceKey classes respectively.
	419	+As already said, the mapping from SDMX to VTL happens when the SDMX dataflows are operand of VTL Transformations, instead the mapping from VTL to SDMX happens when the VTL Data Sets that is result of Transformations^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^30^^>>path:#sdfootnote30sym||name="sdfootnote30anc"]](%%)^^ need to be treated as SDMX objects. This kind of mapping can be applied independently in the two directions and the Dimensions on which the mapping is based can be different in the two directions: these Dimensions are defined in the ToVtlSpaceKey and in the FromVtlSpaceKey classes respectively.
404	404
405	405	First, let us see what happens in the __mapping direction from SDMX to VTL__, i.e. when parts of a SDMX Dataflow (e.g. DF1(1.0.0)) need to be mapped to distinct VTL Data Sets that are operand of some VTL Transformations.
406	406
...	...	@@ -408,20 +408,27 @@
408	408
409	409	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.
410	410
411		-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 …).
	427	+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^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^31^^>>path:#sdfootnote31sym||name="sdfootnote31anc"]](%%)^^. After that, the mapping method from SDMX to VTL specified for the Dataflow DF1(1.0.0) is applied (i.e.
412	412
413		-In the example above, for all the datasets of the kind ‘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.
	429	+basic, pivot …).
414	414
	431	+In the example above, for all the datasets of the kind
	432	+
	433	+‘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.
	434	+
415	415	It should be noted that the desired VTL Data Sets (i.e. of the kind ‘DF1(1.0.0)/// INDICATORvalue//.//COUNTRYvalue//’) can be obtained also by applying the VTL operator “**sub**” (subspace) to the Dataflow DF1(1.0.0), like in the following VTL expression:
416	416
417	417	‘DF1(1.0.0)/POPULATION.USA’ :=
	438	+
418	418	DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA” ];
419	419
420	420	‘DF1(1.0.0)/POPULATION.CANADA’ :=
	442	+
421	421	DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
	444	+
422	422	… … …
423	423
424		-In fact the VTL operator “sub” has exactly the same behaviour. Therefore, mapping different parts of a SDMX Dataflow to different VTL Data Sets in the direction from SDMX to VTL through the ordered concatenation notation is equivalent to a proper use of the operator “**sub**” on such a Dataflow.{{footnote}}In case the ordered concatenation notation is used, the VTL Transformation described above, e.g. ‘DF1(1.0)/POPULATION.USA’ := DF1(1.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA”], is implicitly executed. In order to test the overall compliance of the VTL program to the VTL consistency rules, it has to be considered as part of the VTL program even if it is not explicitly coded.{{/footnote}}
	447	+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. ^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^32^^>>path:#sdfootnote32sym||name="sdfootnote32anc"]](%%)^^
425	425
426	426	In the direction from SDMX to VTL it is allowed to omit the value of one or more
427	427
...	...	@@ -432,6 +432,7 @@
432	432	This is equivalent to the application of the VTL “sub” operator only to the identifier //INDICATOR//:
433	433
434	434	‘DF1(1.0.0)/POPULATION.’ :=
	458	+
435	435	DF1(1.0.0) [ sub INDICATOR=“POPULATION” ];
436	436
437	437	Therefore the VTL Data Set ‘DF1(1.0.0)/POPULATION.’ would have the identifiers COUNTRY and TIME_PERIOD.
...	...	@@ -448,12 +448,12 @@
448	448
449	449	Dataflow DF2(1.0.0) having the Dimensions TIME_PERIOD, INDICATOR, and COUNTRY and that such a programmer finds it convenient to calculate separately the parts of DF2(1.0.0) that have different combinations of values for INDICATOR and COUNTRY:
450	450
451		-* each part is calculated as a VTL derived Data Set, result of a dedicated VTL Transformation;{{footnote}}If the whole DF2(1.0) is calculated by means of just one VTL Transformation, then the mapping between the SDMX Dataflow and the corresponding VTL dataset is one-to-one and this kind of mapping (one SDMX Dataflow to many VTL datasets) does not apply.{{/footnote}}
452		-* the data structure of all these VTL Data Sets has the TIME_PERIOD identifier and does not have the INDICATOR and COUNTRY identifiers.{{footnote}}This is possible as each VTL dataset corresponds to one particular combination of values of INDICATOR and COUNTRY.{{/footnote}}
	475	+* each part is calculated as a VTL derived Data Set, result of a dedicated VTL Transformation; ^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^33^^>>path:#sdfootnote33sym||name="sdfootnote33anc"]](%%)^^
	476	+* the data structure of all these VTL Data Sets has the TIME_PERIOD identifier and does not have the INDICATOR and COUNTRY identifiers.^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^34^^>>path:#sdfootnote34sym||name="sdfootnote34anc"]](%%)^^
453	453
454		-Under these hypothesis, such derived VTL Data Sets can be mapped to DF2(1.0.0) by declaring the DimensionComponents INDICATOR and COUNTRY as mapping dimensions{{footnote}}The mapping dimensions are defined as FromVtlSpaceKeys of the FromVtlSuperSpace of the VtlDataflowMapping relevant to DF2(1.0).{{/footnote}}.
	478	+Under these hypothesis, such derived VTL Data Sets can be mapped to DF2(1.0.0) by declaring the DimensionComponents INDICATOR and COUNTRY as mapping dimensions^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^35^^>>path:#sdfootnote35sym||name="sdfootnote35anc"]](%%)^^.
455	455
456		-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}}
	480	+The corresponding VTL Transformations, assuming that the result needs to be persistent, would be of this kind:^^ [[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^36^^>>path:#sdfootnote36sym||name="sdfootnote36anc"]](%%)^^
457	457
458	458	‘DF2(1.0.0)/INDICATORvalue.COUNTRYvalue’ <- expression
459	459
...	...	@@ -462,8 +462,11 @@
462	462	‘DF2(1.0.0)/GDPPERCAPITA.USA’ <- expression11; ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ <- expression12;
463	463
464	464	… … …
	489	+
465	465	‘DF2(1.0.0)/POPGROWTH.USA’ <- expression21;
	491	+
466	466	‘DF2(1.0.0)/POPGROWTH.CANADA’ <- expression22;
	493	+
467	467	… … …
468	468
469	469	As said, it is assumed that these VTL derived Data Sets have the TIME_PERIOD as the only identifier. In the mapping from VTL to SMDX, the Dimensions INDICATOR and COUNTRY are added to the VTL data structure on order to obtain the SDMX one, with the following values respectively:
...	...	@@ -471,30 +471,44 @@
471	471	VTL dataset INDICATOR value COUNTRY value
472	472
473	473	‘DF2(1.0.0)/GDPPERCAPITA.USA’ GDPPERCAPITA USA
	501	+
474	474	‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ GDPPERCAPITA CANADA … … …
	503	+
475	475	‘DF2(1.0.0)/POPGROWTH.USA’ POPGROWTH USA
	505	+
476	476	‘DF2(1.0.0)/POPGROWTH.CANADA’ POPGROWTH CANADA
	507	+
477	477	… … …
478	478
479	479	It should be noted that the application of this many-to-one mapping from VTL to SDMX is equivalent to an appropriate sequence of VTL Transformations. These use the VTL operator “calc” to add the proper VTL identifiers (in the example, INDICATOR and COUNTRY) and to assign to them the proper values and the operator “union” in order to obtain the final VTL dataset (in the example DF2(1.0.0)), that can be mapped oneto-one to the homonymous SDMX Dataflow. Following the same example, these VTL Transformations would be:
480	480
481	481	DF2bis_GDPPERCAPITA_USA := ‘DF2(1.0.0)/GDPPERCAPITA.USA’ [calc identifier INDICATOR := ”GDPPERCAPITA”, identifier COUNTRY := ”USA”];
	513	+
482	482	DF2bis_GDPPERCAPITA_CANADA := ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ [calc identifier INDICATOR:=”GDPPERCAPITA”, identifier COUNTRY:=”CANADA”]; … … …
	515	+
483	483	DF2bis_POPGROWTH_USA := ‘DF2(1.0.0)/POPGROWTH.USA’
	517	+
484	484	[calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”USA”];
	519	+
485	485	DF2bis_POPGROWTH_CANADA’ := ‘DF2(1.0.0)/POPGROWTH.CANADA’ [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”CANADA”]; … … …
	521	+
486	486	DF2(1.0) <- UNION (DF2bis_GDPPERCAPITA_USA’,
	523	+
487	487	DF2bis_GDPPERCAPITA_CANADA’,
	525	+
488	488	… ,
	527	+
489	489	DF2bis_POPGROWTH_USA’,
	529	+
490	490	DF2bis_POPGROWTH_CANADA’
	531	+
491	491	…);
492	492
493	493	In other words, starting from the datasets explicitly calculated through VTL (in the example ‘DF2(1.0)/GDPPERCAPITA.USA’ and so on), the first step consists in calculating other (non-persistent) VTL datasets (in the example
494	494
495		-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.
	536	+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)^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^37^^>>path:#sdfootnote37sym||name="sdfootnote37anc"]](%%)^^, which can be mapped one-to-one to the homonymous SDMX Dataflow having the dimension components TIME_PERIOD, INDICATOR and COUNTRY.
496	496
497		-Therefore, mapping different VTL datasets having the same data structure to different parts of a SDMX Dataflow, i.e. in the direction from VTL to SDMX, through the ordered concatenation notation is equivalent to a proper use of the operators “calc” and “union” on such datasets.{{footnote}}In case the ordered concatenation notation from VTL to SDMX is used, the set of Transformations described above is implicitly performed; therefore, in order to test the overall compliance of the VTL program to the VTL consistency rules, these implicit Transformations have to be considered as part of the VTL program even if they are not explicitly coded.{{/footnote}}
	538	+Therefore, mapping different VTL datasets having the same data structure to different parts of a SDMX Dataflow, i.e. in the direction from VTL to SDMX, through the ordered concatenation notation is equivalent to a proper use of the operators “calc” and “union” on such datasets. ^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^38^^>>path:#sdfootnote38sym||name="sdfootnote38anc"]](%%)[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^39^^>>path:#sdfootnote39sym||name="sdfootnote39anc"]](%%)^^
498	498
499	499	It is worth noting that in the direction from VTL to SDMX it is mandatory to specify the value for every Dimension on which the mapping is based (in other word, in the name of the calculated VTL dataset is __not__ possible to omit the value of some of the Dimensions).
500	500
...	...	@@ -502,51 +502,52 @@
502	502
503	503	With reference to the VTL “model for Variables and Value domains”, the following additional mappings have to be considered:
504	504
505		-(% style="width:1170.29px" %)
506		-|**VTL**|(% style="width:754px" %)**SDMX**
507		-|**Data Set Component**|(% style="width:754px" %)Although this abstraction exists in SDMX, it does not have an explicit definition and correspond to a Component (either a DimensionComponent or a Measure or a DataAttribute) belonging to one specific Dataflow{{footnote}}Through SDMX Constraints, it is possible to specify the values that a Component of a Dataflow can assume.{{/footnote}}
508		-|**Represented Variable**|(% style="width:754px" %)(((
	546	+|VTL|SDMX
	547	+|**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^^
	548	+|**Represented Variable**|(((
509	509	**Concept** with a definite
510	510
511	511	Representation
512	512	)))
513		-|**Value Domain**|(% style="width:754px" %)(((
	553	+|**Value Domain**|(((
514	514	**Representation** (see the Structure
515	515
516	516	Pattern in the Base Package)
517	517	)))
518		-|**Enumerated Value Domain / Code List**|(% style="width:754px" %)**Codelist**
519		-|**Code**|(% style="width:754px" %)(((
	558	+|**Enumerated Value Domain / Code List**|**Codelist**
	559	+|**Code**|(((
520	520	**Code** (for enumerated
521	521
522	522	DimensionComponent, Measure, DataAttribute)
523	523	)))
524		-|**Described Value Domain**|(% style="width:754px" %)(((
525		-non-enumerated** Representation**
	564	+|**Described Value Domain**|(((
	565	+non-enumerated** &nbsp;&nbsp;&nbsp;Representation**
526	526
527	527	(having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
528	528	)))
529		-|**Value**|(% style="width:754px" %)Although this abstraction exists in SDMX, it does not have an explicit definition and correspond to a **Code** of a Codelist (for enumerated Representations) or
530		-| |(% style="width:754px" %)(((
531		-to a valid **value **(for non-enumerated** **Representations)
	569	+|**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
	570	+| |(((
	571	+to a valid **value &nbsp;&nbsp;&nbsp;**(for non-enumerated** &nbsp;&nbsp;&nbsp;**
	572	+
	573	+Representations)
532	532	)))
533		-|**Value Domain Subset / Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
534		-|**Enumerated Value Domain Subset / Enumerated Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
535		-|**Described Value Domain Subset / Described Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
536		-|**Set list**|(% style="width:754px" %)This abstraction does not exist in SDMX
	575	+|**Value Domain Subset / Set**|This abstraction does not exist in SDMX
	576	+|**Enumerated Value Domain Subset / Enumerated Set**|This abstraction does not exist in SDMX
	577	+|**Described Value Domain Subset / Described Set**|This abstraction does not exist in SDMX
	578	+|**Set list**|This abstraction does not exist in SDMX
537	537
538	538	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).
539	539
540		-Another difference consists in the fact that all Value Domains are considered as identifiable objects in VTL either if enumerated or not, while in SDMX the Codelist (corresponding to a VTL enumerated Value Domain) is identifiable, while the SDMX non-enumerated Representation (corresponding to a VTL non-enumerated Value Domain) is not identifiable. As a consequence, the definition of the VTL Rulesets, which in VTL can refer either to enumerated or non-enumerated value domains, in SDMX can refer only to enumerated Value Domains (i.e. to SDMX Codelists). As for the mapping between VTL variables and SDMX Concepts, it should be noted that these artefacts do not coincide perfectly. In fact, the VTL variables are represented variables, defined always on the same Value Domain (“Representation” in SDMX) independently of the data set / data structure in which they appear{{footnote}}By using represented variables, VTL can assume that data structures having the same variables as identifiers can be composed one another because the correspondent values can match.{{/footnote}}, while the SDMX Concepts can have different Representations in different DataStructures.{{footnote}}A Concept becomes a Component in a DataStructureDefinition, and Components can have different LocalRepresentations in different DataStructureDefinitions, also overriding the (possible) base representation of the Concept.{{/footnote}} This means that one SDMX Concept can correspond to many VTL Variables, one for each representation the Concept has.
	582	+Another difference consists in the fact that all Value Domains are considered as identifiable objects in VTL either if enumerated or not, while in SDMX the Codelist (corresponding to a VTL enumerated Value Domain) is identifiable, while the SDMX non-enumerated Representation (corresponding to a VTL non-enumerated Value Domain) is not identifiable. As a consequence, the definition of the VTL Rulesets, which in VTL can refer either to enumerated or non-enumerated value domains, in SDMX can refer only to enumerated Value Domains (i.e. to SDMX Codelists). As for the mapping between VTL variables and SDMX Concepts, it should be noted that these artefacts do not coincide perfectly. In fact, the VTL variables are represented variables, defined always on the same Value Domain (“Representation” in SDMX) independently of the data set / data structure in which they appear^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^40^^>>path:#sdfootnote40sym||name="sdfootnote40anc"]](%%)^^, while the SDMX Concepts can have different Representations in different DataStructures.^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^41^^>>path:#sdfootnote41sym||name="sdfootnote41anc"]](%%)^^ This means that one SDMX Concept can correspond to many VTL Variables, one for each representation the Concept has.
541	541
542	542	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
543	543
544		-DS_c := DS_a + DS_b (where DS_a, DS_b, DS_c are VTL Data Sets)
	586	+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.
545	545
546		-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.
547		-
548	548	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
549	549
	590	+[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_59eee18f.gif||alt="Shape5" height="1" width="192"]]
	591	+
550	550	Transformations to ensure that the VTL expressions are consistent with the actual representations of the correspondent SDMX Concepts.
551	551
552	552	It remains up to the SDMX-VTL definer also the assurance of the consistency between a VTL Ruleset defined on Variables and the SDMX Components on which the Ruleset is applied. In fact, a VTL Ruleset is expressed by means of the values of the Variables (i.e. SDMX Concepts), i.e. assuming definite representations for them (e.g. ISOalpha-3 for country). If the Ruleset is applied to SDMX Components that have the same name of the Concept they refer to but different representations (e.g. ISO-alpha-2 for country), the Ruleset cannot work properly.
...	...	@@ -561,8 +561,7 @@
561	561
562	562	[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_e3df33ae.png||height="543" width="483"]]
563	563
564		-(% class="wikigeneratedid" id="HFigure222013VTLDataTypes" %)
565		-**Figure 22 – VTL Data Types**
	606	+==== Figure 22 – VTL Data Types ====
566	566
567	567	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.
568	568
...	...	@@ -569,12 +569,131 @@
569	569	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):
570	570
571	571
572		-**Figure 23 – VTL Basic Scalar Types**
573	573
574	574	(((
575		-
	615	+//n//
	616	+
	617	+//a//
	618	+
	619	+//e//
	620	+
	621	+//l//
	622	+
	623	+//o//
	624	+
	625	+//o//
	626	+
	627	+//B//
	628	+
	629	+//n//
	630	+
	631	+//o//
	632	+
	633	+//i//
	634	+
	635	+//t//
	636	+
	637	+//a//
	638	+
	639	+//r//
	640	+
	641	+//u//
	642	+
	643	+//D//
	644	+
	645	+//d//
	646	+
	647	+//o//
	648	+
	649	+//i//
	650	+
	651	+//r//
	652	+
	653	+//e//
	654	+
	655	+//p//
	656	+
	657	+//_//
	658	+
	659	+//e//
	660	+
	661	+//m//
	662	+
	663	+//i//
	664	+
	665	+//T//
	666	+
	667	+//e//
	668	+
	669	+//t//
	670	+
	671	+//a//
	672	+
	673	+//D//
	674	+
	675	+//e//
	676	+
	677	+//m//
	678	+
	679	+//i//
	680	+
	681	+//T//
	682	+
	683	+//r//
	684	+
	685	+//e//
	686	+
	687	+//g//
	688	+
	689	+//e//
	690	+
	691	+//t//
	692	+
	693	+//n//
	694	+
	695	+//I//
	696	+
	697	+//r//
	698	+
	699	+//e//
	700	+
	701	+//b//
	702	+
	703	+//m//
	704	+
	705	+//u//
	706	+
	707	+//N//
	708	+
	709	+//g//
	710	+
	711	+//n//
	712	+
	713	+//i//
	714	+
	715	+//r//
	716	+
	717	+//t//
	718	+
	719	+//S//
	720	+
	721	+//r//
	722	+
	723	+//a//
	724	+
	725	+//l//
	726	+
	727	+//a//
	728	+
	729	+//c//
	730	+
	731	+//S//
	732	+
	733	+[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_82d45833.gif||alt="Shape6" height="231" width="184"]]
576	576	)))
577	577
	736	+==== Figure 23 – VTL Basic Scalar Types ====
	737	+
578	578	=== 12.4.2 VTL basic scalar types and SDMX data types ===
579	579
580	580	The VTL assumes that a basic scalar type has a unique internal representation and can have more external representations.
...	...	@@ -597,159 +597,204 @@
597	597
598	598	The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types.
599	599
600		-(% style="width:823.294px" %)
601		-|(% style="width:509px" %)**SDMX data type (BasicComponentDataType)**|(% style="width:312px" %)**Default VTL basic scalar type**
602		-|(% style="width:509px" %)(((
	760	+|SDMX data type (BasicComponentDataType)|Default VTL basic scalar type
	761	+|(((
603	603	String
	763	+
604	604	(string allowing any character)
605		-)))|(% style="width:312px" %)string
606		-|(% style="width:509px" %)(((
	765	+)))|string
	766	+|(((
607	607	Alpha
	768	+
608	608	(string which only allows A-z)
609		-)))|(% style="width:312px" %)string
610		-|(% style="width:509px" %)(((
	770	+)))|string
	771	+|(((
611	611	AlphaNumeric
	773	+
612	612	(string which only allows A-z and 0-9)
613		-)))|(% style="width:312px" %)string
614		-|(% style="width:509px" %)(((
	775	+)))|string
	776	+|(((
615	615	Numeric
	778	+
616	616	(string which only allows 0-9, but is not numeric so that is can having leading zeros)
617		-)))|(% style="width:312px" %)string
618		-|(% style="width:509px" %)(((
	780	+)))|string
	781	+|(((
619	619	BigInteger
	783	+
620	620	(corresponds to XML Schema xs:integer datatype; infinite set of integer values)
621		-)))|(% style="width:312px" %)integer
622		-|(% style="width:509px" %)(((
	785	+)))|integer
	786	+|(((
623	623	Integer
624		-(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647 (inclusive))
625		-)))|(% style="width:312px" %)integer
626		-|(% style="width:509px" %)(((
	788	+
	789	+(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647
	790	+
	791	+(inclusive))
	792	+)))|integer
	793	+|(((
627	627	Long
628		-(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and +9223372036854775807 (inclusive))
629		-)))|(% style="width:312px" %)integer
630		-|(% style="width:509px" %)(((
	795	+
	796	+(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and
	797	+
	798	++9223372036854775807 (inclusive))
	799	+)))|integer
	800	+|(((
631	631	Short
	802	+
632	632	(corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive))
633		-)))|(% style="width:312px" %)integer
634		-|(% style="width:509px" %)Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|(% style="width:312px" %)number
635		-|(% style="width:509px" %)(((
	804	+)))|integer
	805	+|Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|number
	806	+|(((
636	636	Float
	808	+
637	637	(corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type)
638		-)))|(% style="width:312px" %)number
639		-|(% style="width:509px" %)(((
	810	+)))|number
	811	+|(((
640	640	Double
	813	+
641	641	(corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type)
642		-)))|(% style="width:312px" %)number
643		-|(% style="width:509px" %)(((
	815	+)))|number
	816	+|(((
644	644	Boolean
645		-(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of binary-valued logic: {true, false})
646		-)))|(% style="width:312px" %)boolean
647	647
648		-(% style="width:822.294px" %)
649		-|(% colspan="2" style="width:507px" %)(((
	819	+(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of
	820	+
	821	+binary-valued logic: {true, false})
	822	+)))|boolean
	823	+
	824	+| |(% colspan="2" %)(((
650	650	URI
	826	+
651	651	(corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference)
652		-)))|(% colspan="1" style="width:311px" %)string
653		-|(% colspan="2" style="width:507px" %)(((
	828	+)))|(% colspan="2" %)string
	829	+| |(% colspan="2" %)(((
654	654	Count
	831	+
655	655	(an integer following a sequential pattern, increasing by 1 for each occurrence)
656		-)))|(% colspan="1" style="width:311px" %)integer
657		-|(% colspan="2" style="width:507px" %)(((
	833	+)))|(% colspan="2" %)integer
	834	+| |(% colspan="2" %)(((
658	658	InclusiveValueRange
	836	+
659	659	(decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
660		-)))|(% colspan="1" style="width:311px" %)number
661		-|(% colspan="2" style="width:507px" %)(((
	838	+)))|(% colspan="2" %)number
	839	+| |(% colspan="2" %)(((
662	662	ExclusiveValueRange
	841	+
663	663	(decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
664		-)))|(% colspan="1" style="width:311px" %)number
665		-|(% colspan="2" style="width:507px" %)(((
	843	+)))|(% colspan="2" %)number
	844	+| |(% colspan="2" %)(((
666	666	Incremental
	846	+
667	667	(decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation)
668		-)))|(% colspan="1" style="width:311px" %)number
669		-|(% colspan="2" style="width:507px" %)(((
	848	+)))|(% colspan="2" %)number
	849	+| |(% colspan="2" %)(((
670	670	ObservationalTimePeriod
	851	+
671	671	(superset of StandardTimePeriod and TimeRange)
672		-)))|(% colspan="1" style="width:311px" %)time
673		-|(% colspan="2" style="width:507px" %)(((
	853	+)))|(% colspan="2" %)time
	854	+| |(% colspan="2" %)(((
674	674	StandardTimePeriod
675		-(superset of BasicTimePeriod and ReportingTimePeriod)
676		-)))|(% colspan="1" style="width:311px" %)time
677		-|(% colspan="2" style="width:507px" %)(((
	856	+
	857	+(superset of BasicTimePeriod and
	858	+
	859	+ReportingTimePeriod)
	860	+)))|(% colspan="2" %)time
	861	+| |(% colspan="2" %)(((
678	678	BasicTimePeriod
	863	+
679	679	(superset of GregorianTimePeriod and DateTime)
680		-)))|(% colspan="1" style="width:311px" %)date
681		-|(% colspan="2" style="width:507px" %)(((
	865	+)))|(% colspan="2" %)date
	866	+| |(% colspan="2" %)(((
682	682	GregorianTimePeriod
	868	+
683	683	(superset of GregorianYear, GregorianYearMonth, and GregorianDay)
684		-)))|(% colspan="1" style="width:311px" %)date
685		-|(% colspan="2" style="width:507px" %)GregorianYear (YYYY)|(% colspan="1" style="width:311px" %)date
686		-|(% colspan="2" style="width:507px" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="1" style="width:311px" %)date
687		-|(% colspan="2" style="width:507px" %)GregorianDay (YYYY-MM-DD)|(% colspan="1" style="width:311px" %)date
688		-|(% colspan="2" style="width:507px" %)(((
	870	+)))|(% colspan="2" %)date
	871	+| |(% colspan="2" %)GregorianYear (YYYY)|(% colspan="2" %)date
	872	+| |(% colspan="2" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="2" %)date
	873	+| |(% colspan="2" %)GregorianDay (YYYY-MM-DD)|(% colspan="2" %)date
	874	+| |(% colspan="2" %)(((
689	689	ReportingTimePeriod
690		-(superset of RepostingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, ReportingDay)
691		-)))|(% colspan="1" style="width:311px" %)time_period
692		-|(% colspan="2" style="width:507px" %)(((
	876	+
	877	+(superset of RepostingYear, ReportingSemester,
	878	+
	879	+ReportingTrimester, ReportingQuarter,
	880	+
	881	+ReportingMonth, ReportingWeek, ReportingDay)
	882	+)))|(% colspan="2" %)time_period
	883	+| |(% colspan="2" %)(((
693	693	ReportingYear
	885	+
694	694	(YYYY-A1 – 1 year period)
695		-)))|(% colspan="1" style="width:311px" %)time_period
696		-|(% colspan="2" style="width:507px" %)(((
	887	+)))|(% colspan="2" %)time_period
	888	+| |(% colspan="2" %)(((
697	697	ReportingSemester
	890	+
698	698	(YYYY-Ss – 6 month period)
699		-)))|(% colspan="1" style="width:311px" %)time_period
700		-|(% colspan="2" style="width:507px" %)(((
	892	+)))|(% colspan="2" %)time_period
	893	+| |(% colspan="2" %)(((
701	701	ReportingTrimester
	895	+
702	702	(YYYY-Tt – 4 month period)
703		-)))|(% colspan="1" style="width:311px" %)time_period
704		-|(% colspan="2" style="width:507px" %)(((
	897	+)))|(% colspan="2" %)time_period
	898	+| |(% colspan="2" %)(((
705	705	ReportingQuarter
	900	+
706	706	(YYYY-Qq – 3 month period)
707		-)))|(% colspan="1" style="width:311px" %)time_period
708		-|(% colspan="2" style="width:507px" %)(((
	902	+)))|(% colspan="2" %)time_period
	903	+| |(% colspan="2" %)(((
709	709	ReportingMonth
	905	+
710	710	(YYYY-Mmm – 1 month period)
711		-)))|(% colspan="1" style="width:311px" %)time_period
712		-|(% colspan="2" style="width:507px" %)ReportingWeek|(% colspan="1" style="width:311px" %)time_period
713		-|(% colspan="1" style="width:507px" %)(YYYY-Www – 7 day period; following ISO 8601 definition of a week in a year)|(% colspan="2" style="width:312px" %)
714		-|(% colspan="1" style="width:507px" %)(((
	907	+)))|(% colspan="2" %)time_period
	908	+| |(% colspan="2" %)ReportingWeek|(% colspan="2" %)time_period
	909	+| |(% colspan="2" %) |(% colspan="2" %)
	910	+| |(% colspan="2" %) |(% colspan="2" %)
	911	+|(% colspan="2" %)(YYYY-Www – 7 day period; following ISO 8601 definition of a week in a year)|(% colspan="2" %) |
	912	+|(% colspan="2" %)(((
715	715	ReportingDay
	914	+
716	716	(YYYY-Dddd – 1 day period)
717		-)))|(% colspan="2" style="width:312px" %)time_period
718		-|(% colspan="1" style="width:507px" %)(((
	916	+)))|(% colspan="2" %)time_period|
	917	+|(% colspan="2" %)(((
719	719	DateTime
	919	+
720	720	(YYYY-MM-DDThh:mm:ss)
721		-)))|(% colspan="2" style="width:312px" %)date
722		-|(% colspan="1" style="width:507px" %)(((
	921	+)))|(% colspan="2" %)date|
	922	+|(% colspan="2" %)(((
723	723	TimeRange
	924	+
724	724	(YYYY-MM-DD(Thh:mm:ss)?/<duration>)
725		-)))|(% colspan="2" style="width:312px" %)time
726		-|(% colspan="1" style="width:507px" %)(((
	926	+)))|(% colspan="2" %)time|
	927	+|(% colspan="2" %)(((
727	727	Month
	929	+
728	728	(~-~-MM; speicifies a month independent of a year; e.g. February is black history month in the United States)
729		-)))|(% colspan="2" style="width:312px" %)string
730		-|(% colspan="1" style="width:507px" %)(((
	931	+)))|(% colspan="2" %)string|
	932	+|(% colspan="2" %)(((
731	731	MonthDay
	934	+
732	732	(~-~-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)
733		-)))|(% colspan="2" style="width:312px" %)string
734		-|(% colspan="1" style="width:507px" %)(((
	936	+)))|(% colspan="2" %)string|
	937	+|(% colspan="2" %)(((
735	735	Day
	939	+
736	736	(~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday)
737		-)))|(% colspan="2" style="width:312px" %)string
738		-|(% colspan="1" style="width:507px" %)(((
	941	+)))|(% colspan="2" %)string|
	942	+|(% colspan="2" %)(((
739	739	Time
	944	+
740	740	(hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM)
741		-)))|(% colspan="2" style="width:312px" %)string
742		-|(% colspan="1" style="width:507px" %)(((
	946	+)))|(% colspan="2" %)string|
	947	+|(% colspan="2" %)(((
743	743	Duration
	949	+
744	744	(corresponds to XML Schema xs:duration datatype)
745		-)))|(% colspan="2" style="width:312px" %)duration
746		-|(% colspan="1" style="width:507px" %)XHTML|(% colspan="2" style="width:312px" %)Metadata type – not applicable
747		-|(% colspan="1" style="width:507px" %)KeyValues|(% colspan="2" style="width:312px" %)Metadata type – not applicable
748		-|(% colspan="1" style="width:507px" %)IdentifiableReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
749		-|(% colspan="1" style="width:507px" %)DataSetReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
	951	+)))|(% colspan="2" %)duration|
	952	+|(% colspan="2" %)XHTML|(% colspan="2" %)Metadata type – not applicable|
	953	+|(% colspan="2" %)KeyValues|(% colspan="2" %)Metadata type – not applicable|
	954	+|(% colspan="2" %)IdentifiableReference|(% colspan="2" %)Metadata type – not applicable|
	955	+|(% colspan="2" %)DataSetReference|(% colspan="2" %)Metadata type – not applicable|
750	750
751		-(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes" %)
752		-**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
	957	+==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
753	753
754	754	When VTL takes in input SDMX artefacts, it is assumed that a type conversion according to the table above always happens. In case a different VTL basic scalar type is desired, it can be achieved in the VTL program taking in input the default VTL basic scalar type above and applying to it the VTL type conversion features (see the implicit and explicit type conversion and the "cast" operator in the VTL Reference Manual).
755	755
...	...	@@ -757,32 +757,39 @@
757	757
758	758	The following table describes the default conversion from the VTL basic scalar types to the SDMX data types .
759	759
760		-(% style="width:1073.29px" %)
761		-|(% style="width:207px" %)(((
762		-**VTL basic scalar type**
763		-)))|(% style="width:462px" %)(((
764		-**Default SDMX data type (BasicComponentDataType)**
765		-)))|(% style="width:402px" %)**Default output format**
766		-|(% style="width:207px" %)String|(% style="width:462px" %)String|(% style="width:402px" %)Like XML (xs:string)
767		-|(% style="width:207px" %)Number|(% style="width:462px" %)Float|(% style="width:402px" %)Like XML (xs:float)
768		-|(% style="width:207px" %)Integer|(% style="width:462px" %)Integer|(% style="width:402px" %)Like XML (xs:int)
769		-|(% style="width:207px" %)Date|(% style="width:462px" %)DateTime|(% style="width:402px" %)YYYY-MM-DDT00:00:00Z
770		-|(% style="width:207px" %)Time|(% style="width:462px" %)StandardTimePeriod|(% style="width:402px" %)<date>/<date> (as defined above)
771		-|(% style="width:207px" %)time_period|(% style="width:462px" %)(((
	965	+|(((
	966	+VTL basic
	967	+
	968	+scalar type
	969	+)))|(((
	970	+Default SDMX data type
	971	+
	972	+(BasicComponentDataType
	973	+
	974	+)
	975	+)))|Default output format
	976	+|String|String|Like XML (xs:string)
	977	+|Number|Float|Like XML (xs:float)
	978	+|Integer|Integer|Like XML (xs:int)
	979	+|Date|DateTime|YYYY-MM-DDT00:00:00Z
	980	+|Time|StandardTimePeriod|<date>/<date> (as defined above)
	981	+|time_period|(((
772	772	ReportingTimePeriod
	983	+
773	773	(StandardReportingPeriod)
774		-)))|(% style="width:402px" %)(((
	985	+)))|(((
775	775	YYYY-Pppp
	987	+
776	776	(according to SDMX )
777	777	)))
778		-|(% style="width:207px" %)Duration|(% style="width:462px" %)Duration|(% style="width:402px" %)(((
	990	+|Duration|Duration|(((
779	779	Like XML (xs:duration)
	992	+
780	780	PnYnMnDTnHnMnS
781	781	)))
782		-|(% style="width:207px" %)Boolean|(% style="width:462px" %)Boolean|(% style="width:402px" %)Like XML (xs:boolean) with the values "true" or "false"
	995	+|Boolean|Boolean|Like XML (xs:boolean) with the values "true" or "false"
783	783
784		-(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes-1" %)
785		-**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
	997	+==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
786	786
787	787	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).
788	788
...	...	@@ -836,7 +836,7 @@
836	836	|N|fixed number of digits used in the preceding textual representation of the month or the day
837	837	| |
838	838
839		-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}}.
	1051	+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^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^42^^>>path:#sdfootnote42sym||name="sdfootnote42anc"]](%%)^^.
840	840
841	841	=== 12.4.5 Null Values ===
842	842
...	...	@@ -854,8 +854,10 @@
854	854
855	855	A different format can be specified in the attribute "vtlLiteralFormat" of the CustomType artefact (see also the section Transformations and Expressions of the SDMX information model).
856	856
857		-Like in the case of the conversion of NULLs described in the previous paragraph, the overriding assumption is applied, for a certain VTL basic scalar type, if a value is found for the vtlLiteralFormat attribute of the CustomType of such VTL basic scalar type. The overriding assumption is applied for all the literals of a related VTL TransformationScheme.
	1069	+Like in the case of the conversion of NULLs described in the previous paragraph, the overriding assumption is applied, for a certain VTL basic scalar type, if a value is found for the vtlLiteralFormat attribute of the CustomType of such VTL basic scalar type. The overriding assumption is applied for all the literals of a related VTL
858	858
	1071	+TransformationScheme.
	1072	+
859	859	In case a literal is operand of a VTL Cast operation, the format specified in the Cast overrides all the possible otherwise specified formats.
860	860
861	861	{{putFootnotes/}}