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
	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.
18	18
19		-Transformation (nameable artefact). Each Transformation assigns the outcome of the evaluation of a VTL expression to a result.
20		-
21	21	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.
22	22
23	23	== 12.2 References to SDMX artefacts from VTL statements ==
...	...	@@ -28,10 +28,8 @@
28	28
29	29	The alias of an SDMX artefact can be its URN (Universal Resource Name), an abbreviation of its URN or another user-defined name.
30	30
31		-In any case, the aliases used in the VTL Transformations have to be mapped to the
	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.
32	32
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		-
35	35	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.
36	36
37	37	The references through the URN and the abbreviated URN are described in the following paragraphs.
...	...	@@ -202,7 +202,7 @@
202	202
203	203	=== 12.3.3 Mapping from SDMX to VTL data structures ===
204	204
205		-**12.3.3.1 Basic Mapping**
	201	+==== 12.3.3.1 Basic Mapping ====
206	206
207	207	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:
208	208
...	...	@@ -215,55 +215,43 @@
215	215
216	216	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).
217	217
218		-With the Basic mapping, one SDMX observation^^27^^ generates one VTL data point.
	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.
219	219
220	220	==== 12.3.3.2 Pivot Mapping ====
221	221
222	222	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.
223	223
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
	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}}.
225	225
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		-
228	228	Among other things, the Pivot method provides also backward compatibility with the SDMX 2.1 data structures that contained a MeasureDimension.
229	229
230	230	If applied to SDMX structures that do not contain any MeasureDimension, this method behaves like the Basic mapping (see the previous paragraph).
231	231
232		-^^27^^ Here an SDMX observation is meant to correspond to one combination of values of the DimensionComponents.
	226	+Here an SDMX observation is meant to correspond to one combination of values of the DimensionComponents.
233	233
234	234	The SDMX structures that contain a MeasureDimension are mapped as described below (this mapping is equivalent to a pivoting operation):
235	235
236	236	* A SDMX simple dimension becomes a VTL (simple) identifier and a SDMX TimeDimension becomes a VTL (time) identifier;
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		-
	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;
241	241	* The SDMX MeasureDimension is not mapped to VTL (it disappears in the VTL Data Structure);
242	242	* The SDMX Measure is not mapped to VTL as well (it disappears in the VTL Data Structure);
243	243	* An SDMX DataAttribute is mapped in different ways according to its AttributeRelationship:
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		-*
	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;
249	249	** 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).
250	250	** 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.
251	251
252	252	The summary mapping table of the "pivot" mapping from SDMX to VTL for the SDMX data structures that contain a MeasureDimension is the following:
253	253
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
	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
261	261	)))
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
	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
267	267	)))
268	268
269	269	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.
...	...	@@ -271,14 +271,11 @@
271	271	At observation / data point level, calling Cj (j=1, … n) the j^^th^^ Code of the MeasureDimension:
272	272
273	273	* 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;
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		-
	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.
278	278	* 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
279	279	* 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
280	280
281		-**12.3.3.3 From SDMX DataAttributes to VTL Measures**
	262	+==== 12.3.3.3 From SDMX DataAttributes to VTL Measures ====
282	282
283	283	* 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
284	284
...	...	@@ -290,7 +290,7 @@
290	290
291	291	=== 12.3.4 Mapping from VTL to SDMX data structures ===
292	292
293		-**12.3.4.1 Basic Mapping**
	274	+==== 12.3.4.1 Basic Mapping ====
294	294
295	295	The main mapping method **from VTL to SDMX** is called **Basic **mapping as well.
296	296
...	...	@@ -300,11 +300,12 @@
300	300
301	301	Mapping table:
302	302
303		-|**VTL**|**SDMX**
304		-|(Simple) Identifier|Dimension
305		-|(Time) Identifier|TimeDimension
306		-|Measure|Measure
307		-|Attribute|DataAttribute
	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
308	308
309	309	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.
310	310
...	...	@@ -314,7 +314,7 @@
314	314
315	315	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.
316	316
317		-**12.3.4.2 Unpivot Mapping**
	299	+==== 12.3.4.2 Unpivot Mapping ====
318	318
319	319	An alternative mapping method from VTL to SDMX is the **Unpivot **mapping.
320	320
...	...	@@ -338,11 +338,12 @@
338	338
339	339	The summary mapping table of the **unpivot** mapping method is the following:
340	340
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
	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
346	346
347	347	At observation / data point level:
348	348
...	...	@@ -356,7 +356,7 @@
356	356
357	357	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.
358	358
359		-**12.3.4.3 From VTL Measures to SDMX Data Attributes**
	342	+==== 12.3.4.3 From VTL Measures to SDMX Data Attributes ====
360	360
361	361	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”).
362	362
...	...	@@ -364,12 +364,13 @@
364	364
365	365	The mapping table is the following:
366	366
367		-|VTL|SDMX
368		-|(Simple) Identifier|Dimension
369		-|(Time) Identifier|TimeDimension
370		-|Some Measures|Measure
371		-|Other Measures|DataAttribute
372		-|Attribute|DataAttribute
	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
373	373
374	374	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.
375	375
...	...	@@ -387,20 +387,20 @@
387	387
388	388	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).
389	389
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"]](%%)^^
	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}}
391	391
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"]](%%)^^
	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}}
393	393
394	394	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.
395	395
396	396	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:
397	397
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.
	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.
399	399	* The VTL Data Set is given a name using a special notation also called “ordered concatenation” and composed of the following parts:
400	400	** 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);
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"]](%%)^^
	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}}
402	402
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.
	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.
404	404
405	405	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.
406	406
...	...	@@ -416,7 +416,7 @@
416	416
417	417	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.
418	418
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.
	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.
420	420
421	421	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.
422	422
...	...	@@ -424,28 +424,16 @@
424	424
425	425	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.
426	426
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.
	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 …).
428	428
429		-basic, pivot …).
	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.
430	430
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		-
435	435	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:
436	436
437		-‘DF1(1.0.0)/POPULATION.USA’ :=
	417	+[[image:1747388275998-621.png]]
438	438
439		-DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA” ];
	419	+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}}
440	440
441		-‘DF1(1.0.0)/POPULATION.CANADA’ :=
442		-
443		-DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
444		-
445		-… … …
446		-
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"]](%%)^^
448		-
449	449	In the direction from SDMX to VTL it is allowed to omit the value of one or more
450	450
451	451	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.
...	...	@@ -454,10 +454,8 @@
454	454
455	455	This is equivalent to the application of the VTL “sub” operator only to the identifier //INDICATOR//:
456	456
457		-‘DF1(1.0.0)/POPULATION.’ :=
	429	+[[image:1747388244829-693.png]]
458	458
459		-DF1(1.0.0) [ sub INDICATOR=“POPULATION” ];
460		-
461	461	Therefore the VTL Data Set ‘DF1(1.0.0)/POPULATION.’ would have the identifiers COUNTRY and TIME_PERIOD.
462	462
463	463	Heterogeneous invocations of the same Dataflow are allowed, i.e. omitting different
...	...	@@ -472,70 +472,34 @@
472	472
473	473	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:
474	474
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"]](%%)^^
	445	+* 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}}
	446	+* 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}}
477	477
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"]](%%)^^.
	448	+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}}.
479	479
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"]](%%)^^
	450	+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}}
481	481
482	482	‘DF2(1.0.0)/INDICATORvalue.COUNTRYvalue’ <- expression
483	483
484	484	Some examples follow, for some specific values of INDICATOR and COUNTRY:
485	485
486		-‘DF2(1.0.0)/GDPPERCAPITA.USA’ <- expression11; ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ <- expression12;
	456	+[[image:1747388222879-916.png]]
487	487
488		-… … …
	458	+[[image:1747388206717-256.png]]
489	489
490		-‘DF2(1.0.0)/POPGROWTH.USA’ <- expression21;
491		-
492		-‘DF2(1.0.0)/POPGROWTH.CANADA’ <- expression22;
493		-
494		-… … …
495		-
496	496	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:
497	497
498		-VTL dataset INDICATOR value COUNTRY value
	462	+[[image:1747388148322-387.png]]
499	499
500		-‘DF2(1.0.0)/GDPPERCAPITA.USA’ GDPPERCAPITA USA
501		-
502		-‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ GDPPERCAPITA CANADA … … …
503		-
504		-‘DF2(1.0.0)/POPGROWTH.USA’ POPGROWTH USA
505		-
506		-‘DF2(1.0.0)/POPGROWTH.CANADA’ POPGROWTH CANADA
507		-
508		-… … …
509		-
510	510	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:
511	511
512		-DF2bis_GDPPERCAPITA_USA := ‘DF2(1.0.0)/GDPPERCAPITA.USA’ [calc identifier INDICATOR := ”GDPPERCAPITA”, identifier COUNTRY := ”USA”];
	466	+[[image:1747388179021-814.png]]
513	513
514		-DF2bis_GDPPERCAPITA_CANADA := ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ [calc identifier INDICATOR:=”GDPPERCAPITA”, identifier COUNTRY:=”CANADA”]; … … …
515		-
516		-DF2bis_POPGROWTH_USA := ‘DF2(1.0.0)/POPGROWTH.USA’
517		-
518		-[calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”USA”];
519		-
520		-DF2bis_POPGROWTH_CANADA’ := ‘DF2(1.0.0)/POPGROWTH.CANADA’ [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”CANADA”]; … … …
521		-
522		-DF2(1.0) <- UNION (DF2bis_GDPPERCAPITA_USA’,
523		-
524		-DF2bis_GDPPERCAPITA_CANADA’,
525		-
526		-… ,
527		-
528		-DF2bis_POPGROWTH_USA’,
529		-
530		-DF2bis_POPGROWTH_CANADA’
531		-
532		-…);
533		-
534	534	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
535	535
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.
	470	+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.
537	537
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"]](%%)^^
	472	+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}}
539	539
540	540	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).
541	541
...	...	@@ -543,52 +543,43 @@
543	543
544	544	With reference to the VTL “model for Variables and Value domains”, the following additional mappings have to be considered:
545	545
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**|(((
	480	+(% style="width:895.294px" %)
	481	+|(% style="width:278px" %)**VTL**|(% style="width:613px" %)**SDMX**
	482	+|(% style="width:278px" %)**Data Set Component**|(% style="width:613px" %)Although this abstraction exists in SDMX, it does not have an explicit definition and correspond to a Component (either a DimensionComponent or a Measure or a DataAttribute) belonging to one specific Dataflow{{footnote}}Through SDMX Constraints, it is possible to specify the values that a Component of a Dataflow can assume.{{/footnote}}
	483	+|(% style="width:278px" %)**Represented Variable**|(% style="width:613px" %)(((
549	549	**Concept** with a definite
550	550
551	551	Representation
552	552	)))
553		-|**Value Domain**|(((
554		-**Representation** (see the Structure
555		-
556		-Pattern in the Base Package)
	488	+|(% style="width:278px" %)**Value Domain**|(% style="width:613px" %)(((
	489	+**Representation** (see the Structure Pattern in the Base Package)
557	557	)))
558		-|**Enumerated Value Domain / Code List**|**Codelist**
559		-|**Code**|(((
560		-**Code** (for enumerated
561		-
562		-DimensionComponent, Measure, DataAttribute)
	491	+|(% style="width:278px" %)**Enumerated Value Domain /
	492	+Code List**|(% style="width:613px" %)**Codelist**
	493	+|(% style="width:278px" %)**Code**|(% style="width:613px" %)(((
	494	+**Code** (for enumerated DimensionComponent, Measure, DataAttribute)
563	563	)))
564		-|**Described Value Domain**|(((
565		-non-enumerated** &nbsp;&nbsp;&nbsp;Representation**
566		-
567		-(having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
	496	+|(% style="width:278px" %)**Described Value Domain**|(% style="width:613px" %)(((
	497	+non-enumerated** Representation **(having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
568	568	)))
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;**
	499	+|(% style="width:278px" %)**Value**|(% style="width:613px" %)Although this abstraction exists in SDMX, it does not have an explicit definition and correspond to a **Code** of a Codelist (for enumerated Representations) or to a valid **value **(for non-enumerated** **Representations)
	500	+|(% style="width:278px" %)**Value Domain Subset / Set**|(% style="width:613px" %)This abstraction does not exist in SDMX
	501	+|(% style="width:278px" %)**Enumerated Value Domain Subset / Enumerated Set**|(% style="width:613px" %)This abstraction does not exist in SDMX
	502	+|(% style="width:278px" %)**Described Value Domain Subset / Described Set**|(% style="width:613px" %)This abstraction does not exist in SDMX
	503	+|(% style="width:278px" %)**Set list**|(% style="width:613px" %)This abstraction does not exist in SDMX
572	572
573		-Representations)
574		-)))
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
579		-
580	580	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).
581	581
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.
	507	+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.
583	583
584	584	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
585	585
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.
	511	+DS_c := DS_a + DS_b (where DS_a, DS_b, DS_c are VTL Data Sets)
587	587
	513	+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.
	514	+
588	588	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
589	589
590		-[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_59eee18f.gif||alt="Shape5" height="1" width="192"]]
591		-
592	592	Transformations to ensure that the VTL expressions are consistent with the actual representations of the correspondent SDMX Concepts.
593	593
594	594	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.
...	...	@@ -603,7 +603,8 @@
603	603
604	604	[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_e3df33ae.png||height="543" width="483"]]
605	605
606		-==== Figure 22 – VTL Data Types ====
	531	+(% class="wikigeneratedid" id="HFigure222013VTLDataTypes" %)
	532	+**Figure 22 – VTL Data Types**
607	607
608	608	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.
609	609
...	...	@@ -610,131 +610,12 @@
610	610	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):
611	611
612	612
	539	+**Figure 23 – VTL Basic Scalar Types**
613	613
614	614	(((
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"]]
	542	+
734	734	)))
735	735
736		-==== Figure 23 – VTL Basic Scalar Types ====
737		-
738	738	=== 12.4.2 VTL basic scalar types and SDMX data types ===
739	739
740	740	The VTL assumes that a basic scalar type has a unique internal representation and can have more external representations.
...	...	@@ -757,204 +757,159 @@
757	757
758	758	The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types.
759	759
760		-|SDMX data type (BasicComponentDataType)|Default VTL basic scalar type
761		-|(((
	567	+(% style="width:823.294px" %)
	568	+|(% style="width:509px" %)**SDMX data type (BasicComponentDataType)**|(% style="width:312px" %)**Default VTL basic scalar type**
	569	+|(% style="width:509px" %)(((
762	762	String
763		-
764	764	(string allowing any character)
765		-)))|string
766		-|(((
	572	+)))|(% style="width:312px" %)string
	573	+|(% style="width:509px" %)(((
767	767	Alpha
768		-
769	769	(string which only allows A-z)
770		-)))|string
771		-|(((
	576	+)))|(% style="width:312px" %)string
	577	+|(% style="width:509px" %)(((
772	772	AlphaNumeric
773		-
774	774	(string which only allows A-z and 0-9)
775		-)))|string
776		-|(((
	580	+)))|(% style="width:312px" %)string
	581	+|(% style="width:509px" %)(((
777	777	Numeric
778		-
779	779	(string which only allows 0-9, but is not numeric so that is can having leading zeros)
780		-)))|string
781		-|(((
	584	+)))|(% style="width:312px" %)string
	585	+|(% style="width:509px" %)(((
782	782	BigInteger
783		-
784	784	(corresponds to XML Schema xs:integer datatype; infinite set of integer values)
785		-)))|integer
786		-|(((
	588	+)))|(% style="width:312px" %)integer
	589	+|(% style="width:509px" %)(((
787	787	Integer
788		-
789		-(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647
790		-
791		-(inclusive))
792		-)))|integer
793		-|(((
	591	+(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647 (inclusive))
	592	+)))|(% style="width:312px" %)integer
	593	+|(% style="width:509px" %)(((
794	794	Long
795		-
796		-(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and
797		-
798		-+9223372036854775807 (inclusive))
799		-)))|integer
800		-|(((
	595	+(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and +9223372036854775807 (inclusive))
	596	+)))|(% style="width:312px" %)integer
	597	+|(% style="width:509px" %)(((
801	801	Short
802		-
803	803	(corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive))
804		-)))|integer
805		-|Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|number
806		-|(((
	600	+)))|(% style="width:312px" %)integer
	601	+|(% 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
	602	+|(% style="width:509px" %)(((
807	807	Float
808		-
809	809	(corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type)
810		-)))|number
811		-|(((
	605	+)))|(% style="width:312px" %)number
	606	+|(% style="width:509px" %)(((
812	812	Double
813		-
814	814	(corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type)
815		-)))|number
816		-|(((
	609	+)))|(% style="width:312px" %)number
	610	+|(% style="width:509px" %)(((
817	817	Boolean
	612	+(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of binary-valued logic: {true, false})
	613	+)))|(% style="width:312px" %)boolean
818	818
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" %)(((
	615	+(% style="width:822.294px" %)
	616	+|(% colspan="2" style="width:507px" %)(((
825	825	URI
826		-
827	827	(corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference)
828		-)))|(% colspan="2" %)string
829		-| |(% colspan="2" %)(((
	619	+)))|(% colspan="1" style="width:311px" %)string
	620	+|(% colspan="2" style="width:507px" %)(((
830	830	Count
831		-
832	832	(an integer following a sequential pattern, increasing by 1 for each occurrence)
833		-)))|(% colspan="2" %)integer
834		-| |(% colspan="2" %)(((
	623	+)))|(% colspan="1" style="width:311px" %)integer
	624	+|(% colspan="2" style="width:507px" %)(((
835	835	InclusiveValueRange
836		-
837	837	(decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
838		-)))|(% colspan="2" %)number
839		-| |(% colspan="2" %)(((
	627	+)))|(% colspan="1" style="width:311px" %)number
	628	+|(% colspan="2" style="width:507px" %)(((
840	840	ExclusiveValueRange
841		-
842	842	(decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
843		-)))|(% colspan="2" %)number
844		-| |(% colspan="2" %)(((
	631	+)))|(% colspan="1" style="width:311px" %)number
	632	+|(% colspan="2" style="width:507px" %)(((
845	845	Incremental
846		-
847	847	(decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation)
848		-)))|(% colspan="2" %)number
849		-| |(% colspan="2" %)(((
	635	+)))|(% colspan="1" style="width:311px" %)number
	636	+|(% colspan="2" style="width:507px" %)(((
850	850	ObservationalTimePeriod
851		-
852	852	(superset of StandardTimePeriod and TimeRange)
853		-)))|(% colspan="2" %)time
854		-| |(% colspan="2" %)(((
	639	+)))|(% colspan="1" style="width:311px" %)time
	640	+|(% colspan="2" style="width:507px" %)(((
855	855	StandardTimePeriod
856		-
857		-(superset of BasicTimePeriod and
858		-
859		-ReportingTimePeriod)
860		-)))|(% colspan="2" %)time
861		-| |(% colspan="2" %)(((
	642	+(superset of BasicTimePeriod and ReportingTimePeriod)
	643	+)))|(% colspan="1" style="width:311px" %)time
	644	+|(% colspan="2" style="width:507px" %)(((
862	862	BasicTimePeriod
863		-
864	864	(superset of GregorianTimePeriod and DateTime)
865		-)))|(% colspan="2" %)date
866		-| |(% colspan="2" %)(((
	647	+)))|(% colspan="1" style="width:311px" %)date
	648	+|(% colspan="2" style="width:507px" %)(((
867	867	GregorianTimePeriod
868		-
869	869	(superset of GregorianYear, GregorianYearMonth, and GregorianDay)
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" %)(((
	651	+)))|(% colspan="1" style="width:311px" %)date
	652	+|(% colspan="2" style="width:507px" %)GregorianYear (YYYY)|(% colspan="1" style="width:311px" %)date
	653	+|(% colspan="2" style="width:507px" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="1" style="width:311px" %)date
	654	+|(% colspan="2" style="width:507px" %)GregorianDay (YYYY-MM-DD)|(% colspan="1" style="width:311px" %)date
	655	+|(% colspan="2" style="width:507px" %)(((
875	875	ReportingTimePeriod
876		-
877		-(superset of RepostingYear, ReportingSemester,
878		-
879		-ReportingTrimester, ReportingQuarter,
880		-
881		-ReportingMonth, ReportingWeek, ReportingDay)
882		-)))|(% colspan="2" %)time_period
883		-| |(% colspan="2" %)(((
	657	+(superset of RepostingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, ReportingDay)
	658	+)))|(% colspan="1" style="width:311px" %)time_period
	659	+|(% colspan="2" style="width:507px" %)(((
884	884	ReportingYear
885		-
886	886	(YYYY-A1 – 1 year period)
887		-)))|(% colspan="2" %)time_period
888		-| |(% colspan="2" %)(((
	662	+)))|(% colspan="1" style="width:311px" %)time_period
	663	+|(% colspan="2" style="width:507px" %)(((
889	889	ReportingSemester
890		-
891	891	(YYYY-Ss – 6 month period)
892		-)))|(% colspan="2" %)time_period
893		-| |(% colspan="2" %)(((
	666	+)))|(% colspan="1" style="width:311px" %)time_period
	667	+|(% colspan="2" style="width:507px" %)(((
894	894	ReportingTrimester
895		-
896	896	(YYYY-Tt – 4 month period)
897		-)))|(% colspan="2" %)time_period
898		-| |(% colspan="2" %)(((
	670	+)))|(% colspan="1" style="width:311px" %)time_period
	671	+|(% colspan="2" style="width:507px" %)(((
899	899	ReportingQuarter
900		-
901	901	(YYYY-Qq – 3 month period)
902		-)))|(% colspan="2" %)time_period
903		-| |(% colspan="2" %)(((
	674	+)))|(% colspan="1" style="width:311px" %)time_period
	675	+|(% colspan="2" style="width:507px" %)(((
904	904	ReportingMonth
905		-
906	906	(YYYY-Mmm – 1 month period)
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" %)(((
	678	+)))|(% colspan="1" style="width:311px" %)time_period
	679	+|(% colspan="2" style="width:507px" %)ReportingWeek|(% colspan="1" style="width:311px" %)time_period
	680	+|(% 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" %)
	681	+|(% colspan="1" style="width:507px" %)(((
913	913	ReportingDay
914		-
915	915	(YYYY-Dddd – 1 day period)
916		-)))|(% colspan="2" %)time_period|
917		-|(% colspan="2" %)(((
	684	+)))|(% colspan="2" style="width:312px" %)time_period
	685	+|(% colspan="1" style="width:507px" %)(((
918	918	DateTime
919		-
920	920	(YYYY-MM-DDThh:mm:ss)
921		-)))|(% colspan="2" %)date|
922		-|(% colspan="2" %)(((
	688	+)))|(% colspan="2" style="width:312px" %)date
	689	+|(% colspan="1" style="width:507px" %)(((
923	923	TimeRange
924		-
925	925	(YYYY-MM-DD(Thh:mm:ss)?/<duration>)
926		-)))|(% colspan="2" %)time|
927		-|(% colspan="2" %)(((
	692	+)))|(% colspan="2" style="width:312px" %)time
	693	+|(% colspan="1" style="width:507px" %)(((
928	928	Month
929		-
930	930	(~-~-MM; speicifies a month independent of a year; e.g. February is black history month in the United States)
931		-)))|(% colspan="2" %)string|
932		-|(% colspan="2" %)(((
	696	+)))|(% colspan="2" style="width:312px" %)string
	697	+|(% colspan="1" style="width:507px" %)(((
933	933	MonthDay
934		-
935	935	(~-~-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)
936		-)))|(% colspan="2" %)string|
937		-|(% colspan="2" %)(((
	700	+)))|(% colspan="2" style="width:312px" %)string
	701	+|(% colspan="1" style="width:507px" %)(((
938	938	Day
939		-
940	940	(~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday)
941		-)))|(% colspan="2" %)string|
942		-|(% colspan="2" %)(((
	704	+)))|(% colspan="2" style="width:312px" %)string
	705	+|(% colspan="1" style="width:507px" %)(((
943	943	Time
944		-
945	945	(hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM)
946		-)))|(% colspan="2" %)string|
947		-|(% colspan="2" %)(((
	708	+)))|(% colspan="2" style="width:312px" %)string
	709	+|(% colspan="1" style="width:507px" %)(((
948	948	Duration
949		-
950	950	(corresponds to XML Schema xs:duration datatype)
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|
	712	+)))|(% colspan="2" style="width:312px" %)duration
	713	+|(% colspan="1" style="width:507px" %)XHTML|(% colspan="2" style="width:312px" %)Metadata type – not applicable
	714	+|(% colspan="1" style="width:507px" %)KeyValues|(% colspan="2" style="width:312px" %)Metadata type – not applicable
	715	+|(% colspan="1" style="width:507px" %)IdentifiableReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
	716	+|(% colspan="1" style="width:507px" %)DataSetReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
956	956
957		-==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
	718	+(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes" %)
	719	+**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
958	958
959	959	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).
960	960
...	...	@@ -962,39 +962,32 @@
962	962
963	963	The following table describes the default conversion from the VTL basic scalar types to the SDMX data types .
964	964
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|(((
	727	+(% style="width:1073.29px" %)
	728	+|(% style="width:207px" %)(((
	729	+**VTL basic scalar type**
	730	+)))|(% style="width:462px" %)(((
	731	+**Default SDMX data type (BasicComponentDataType)**
	732	+)))|(% style="width:402px" %)**Default output format**
	733	+|(% style="width:207px" %)String|(% style="width:462px" %)String|(% style="width:402px" %)Like XML (xs:string)
	734	+|(% style="width:207px" %)Number|(% style="width:462px" %)Float|(% style="width:402px" %)Like XML (xs:float)
	735	+|(% style="width:207px" %)Integer|(% style="width:462px" %)Integer|(% style="width:402px" %)Like XML (xs:int)
	736	+|(% style="width:207px" %)Date|(% style="width:462px" %)DateTime|(% style="width:402px" %)YYYY-MM-DDT00:00:00Z
	737	+|(% style="width:207px" %)Time|(% style="width:462px" %)StandardTimePeriod|(% style="width:402px" %)<date>/<date> (as defined above)
	738	+|(% style="width:207px" %)time_period|(% style="width:462px" %)(((
982	982	ReportingTimePeriod
983		-
984	984	(StandardReportingPeriod)
985		-)))|(((
	741	+)))|(% style="width:402px" %)(((
986	986	YYYY-Pppp
987		-
988	988	(according to SDMX )
989	989	)))
990		-|Duration|Duration|(((
	745	+|(% style="width:207px" %)Duration|(% style="width:462px" %)Duration|(% style="width:402px" %)(((
991	991	Like XML (xs:duration)
992		-
993	993	PnYnMnDTnHnMnS
994	994	)))
995		-|Boolean|Boolean|Like XML (xs:boolean) with the values "true" or "false"
	749	+|(% style="width:207px" %)Boolean|(% style="width:462px" %)Boolean|(% style="width:402px" %)Like XML (xs:boolean) with the values "true" or "false"
996	996
997		-==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
	751	+(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes-1" %)
	752	+**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
998	998
999	999	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).
1000	1000
...	...	@@ -1048,7 +1048,7 @@
1048	1048	|N|fixed number of digits used in the preceding textual representation of the month or the day
1049	1049	| |
1050	1050
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"]](%%)^^.
	806	+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}}.
1052	1052
1053	1053	=== 12.4.5 Null Values ===
1054	1054
...	...	@@ -1066,10 +1066,8 @@
1066	1066
1067	1067	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).
1068	1068
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
	824	+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.
1070	1070
1071		-TransformationScheme.
1072		-
1073	1073	In case a literal is operand of a VTL Cast operation, the format specified in the Cast overrides all the possible otherwise specified formats.
1074	1074
1075	1075	{{putFootnotes/}}

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