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 ==
...	...	@@ -213,19 +213,21 @@
213	213
214	214	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).
215	215
216		-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.
217	217
218		-==== 12.3.3.2 Pivot Mapping ====
	220	+**12.3.3.2 Pivot Mapping**
219	219
220	220	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.
221	221
222		-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	+
224	224	Among other things, the Pivot method provides also backward compatibility with the SDMX 2.1 data structures that contained a MeasureDimension.
225	225
226	226	If applied to SDMX structures that do not contain any MeasureDimension, this method behaves like the Basic mapping (see the previous paragraph).
227	227
228		-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.
229	229
230	230	The SDMX structures that contain a MeasureDimension are mapped as described below (this mapping is equivalent to a pivoting operation):
231	231
...	...	@@ -241,22 +241,25 @@
241	241
242	242	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;
243	243
244		-*
	248	+*
245	245	** 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).
246	246	** 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.
247	247
248	248	The summary mapping table of the "pivot" mapping from SDMX to VTL for the SDMX data structures that contain a MeasureDimension is the following:
249	249
250		-(% style="width:769.294px" %)
251		-|(% style="width:401px" %)**SDMX**|(% style="width:366px" %)**VTL**
252		-|(% style="width:401px" %)Dimension|(% style="width:366px" %)(Simple) Identifier
253		-|(% style="width:401px" %)TimeDimension|(% style="width:366px" %)(Time) Identifier
254		-|(% style="width:401px" %)MeasureDimension & one Measure|(% style="width:366px" %)(((
255		-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
256	256	)))
257		-|(% style="width:401px" %)DataAttribute not depending on the MeasureDimension|(% style="width:366px" %)Attribute
258		-|(% style="width:401px" %)DataAttribute depending on the MeasureDimension|(% style="width:366px" %)(((
259		-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
260	260	)))
261	261
262	262	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.
...	...	@@ -271,7 +271,7 @@
271	271	* 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
272	272	* 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
273	273
274		-==== 12.3.3.3 From SDMX DataAttributes to VTL Measures ====
	281	+**12.3.3.3 From SDMX DataAttributes to VTL Measures**
275	275
276	276	* 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
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...	...	@@ -283,7 +283,7 @@
283	283
284	284	=== 12.3.4 Mapping from VTL to SDMX data structures ===
285	285
286		-==== 12.3.4.1 Basic Mapping ====
	293	+**12.3.4.1 Basic Mapping**
287	287
288	288	The main mapping method **from VTL to SDMX** is called **Basic **mapping as well.
289	289
...	...	@@ -293,12 +293,11 @@
293	293
294	294	Mapping table:
295	295
296		-(% style="width:667.294px" %)
297		-|(% style="width:272px" %)**VTL**|(% style="width:392px" %)**SDMX**
298		-|(% style="width:272px" %)(Simple) Identifier|(% style="width:392px" %)Dimension
299		-|(% style="width:272px" %)(Time) Identifier|(% style="width:392px" %)TimeDimension
300		-|(% style="width:272px" %)Measure|(% style="width:392px" %)Measure
301		-|(% 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
302	302
303	303	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.
304	304
...	...	@@ -308,7 +308,7 @@
308	308
309	309	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.
310	310
311		-==== 12.3.4.2 Unpivot Mapping ====
	317	+**12.3.4.2 Unpivot Mapping**
312	312
313	313	An alternative mapping method from VTL to SDMX is the **Unpivot **mapping.
314	314
...	...	@@ -332,12 +332,11 @@
332	332
333	333	The summary mapping table of the **unpivot** mapping method is the following:
334	334
335		-(% style="width:994.294px" %)
336		-|(% style="width:306px" %)**VTL**|(% style="width:684px" %)**SDMX**
337		-|(% style="width:306px" %)(Simple) Identifier|(% style="width:684px" %)Dimension
338		-|(% style="width:306px" %)(Time) Identifier|(% style="width:684px" %)TimeDimension
339		-|(% style="width:306px" %)All Measure Components|(% style="width:684px" %)MeasureDimension (having one Code for each VTL measure component) & one Measure
340		-|(% 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
341	341
342	342	At observation / data point level:
343	343
...	...	@@ -351,7 +351,7 @@
351	351
352	352	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.
353	353
354		-==== 12.3.4.3 From VTL Measures to SDMX Data Attributes ====
	359	+**12.3.4.3 From VTL Measures to SDMX Data Attributes**
355	355
356	356	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”).
357	357
...	...	@@ -359,13 +359,12 @@
359	359
360	360	The mapping table is the following:
361	361
362		-(% style="width:689.294px" %)
363		-|(% style="width:344px" %)VTL|(% style="width:341px" %)SDMX
364		-|(% style="width:344px" %)(Simple) Identifier|(% style="width:341px" %)Dimension
365		-|(% style="width:344px" %)(Time) Identifier|(% style="width:341px" %)TimeDimension
366		-|(% style="width:344px" %)Some Measures|(% style="width:341px" %)Measure
367		-|(% style="width:344px" %)Other Measures|(% style="width:341px" %)DataAttribute
368		-|(% 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
369	369
370	370	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.
371	371
...	...	@@ -383,20 +383,20 @@
383	383
384	384	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).
385	385
386		-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"]](%%)^^
387	387
388		-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"]](%%)^^
389	389
390	390	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.
391	391
392	392	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:
393	393
394		-* 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.
395	395	* The VTL Data Set is given a name using a special notation also called “ordered concatenation” and composed of the following parts:
396	396	** 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);
397		-** 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"]](%%)^^
398	398
399		-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.
400	400
401	401	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.
402	402
...	...	@@ -412,7 +412,7 @@
412	412
413	413	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.
414	414
415		-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.
416	416
417	417	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.
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...	...	@@ -420,7 +420,7 @@
420	420
421	421	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.
422	422
423		-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.
	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.
424	424
425	425	basic, pivot …).
426	426
...	...	@@ -440,7 +440,7 @@
440	440
441	441	… … …
442	442
443		-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"]](%%)^^
444	444
445	445	In the direction from SDMX to VTL it is allowed to omit the value of one or more
446	446
...	...	@@ -468,12 +468,12 @@
468	468
469	469	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:
470	470
471		-* 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}}
472		-* 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"]](%%)^^
473	473
474		-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"]](%%)^^.
475	475
476		-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"]](%%)^^
477	477
478	478	‘DF2(1.0.0)/INDICATORvalue.COUNTRYvalue’ <- expression
479	479
...	...	@@ -529,9 +529,9 @@
529	529
530	530	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
531	531
532		-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.
533	533
534		-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"]](%%)^^
535	535
536	536	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).
537	537
...	...	@@ -539,51 +539,52 @@
539	539
540	540	With reference to the VTL “model for Variables and Value domains”, the following additional mappings have to be considered:
541	541
542		-(% style="width:1170.29px" %)
543		-|**VTL**|(% style="width:754px" %)**SDMX**
544		-|**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}}
545		-|**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**|(((
546	546	**Concept** with a definite
547	547
548	548	Representation
549	549	)))
550		-|**Value Domain**|(% style="width:754px" %)(((
	553	+|**Value Domain**|(((
551	551	**Representation** (see the Structure
552	552
553	553	Pattern in the Base Package)
554	554	)))
555		-|**Enumerated Value Domain / Code List**|(% style="width:754px" %)**Codelist**
556		-|**Code**|(% style="width:754px" %)(((
	558	+|**Enumerated Value Domain / Code List**|**Codelist**
	559	+|**Code**|(((
557	557	**Code** (for enumerated
558	558
559	559	DimensionComponent, Measure, DataAttribute)
560	560	)))
561		-|**Described Value Domain**|(% style="width:754px" %)(((
562		-non-enumerated** Representation**
	564	+|**Described Value Domain**|(((
	565	+non-enumerated** &nbsp;&nbsp;&nbsp;Representation**
563	563
564	564	(having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
565	565	)))
566		-|**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
567		-| |(% style="width:754px" %)(((
568		-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)
569	569	)))
570		-|**Value Domain Subset / Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
571		-|**Enumerated Value Domain Subset / Enumerated Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
572		-|**Described Value Domain Subset / Described Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
573		-|**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
574	574
575	575	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).
576	576
577		-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.
578	578
579	579	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
580	580
581		-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.
582	582
583		-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.
584		-
585	585	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
586	586
	590	+[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_59eee18f.gif||alt="Shape5" height="1" width="192"]]
	591	+
587	587	Transformations to ensure that the VTL expressions are consistent with the actual representations of the correspondent SDMX Concepts.
588	588
589	589	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.
...	...	@@ -598,8 +598,7 @@
598	598
599	599	[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_e3df33ae.png||height="543" width="483"]]
600	600
601		-(% class="wikigeneratedid" id="HFigure222013VTLDataTypes" %)
602		-**Figure 22 – VTL Data Types**
	606	+==== Figure 22 – VTL Data Types ====
603	603
604	604	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.
605	605
...	...	@@ -606,12 +606,131 @@
606	606	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):
607	607
608	608
609		-**Figure 23 – VTL Basic Scalar Types**
610	610
611	611	(((
612		-
	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"]]
613	613	)))
614	614
	736	+==== Figure 23 – VTL Basic Scalar Types ====
	737	+
615	615	=== 12.4.2 VTL basic scalar types and SDMX data types ===
616	616
617	617	The VTL assumes that a basic scalar type has a unique internal representation and can have more external representations.
...	...	@@ -634,159 +634,204 @@
634	634
635	635	The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types.
636	636
637		-(% style="width:823.294px" %)
638		-|(% style="width:509px" %)**SDMX data type (BasicComponentDataType)**|(% style="width:312px" %)**Default VTL basic scalar type**
639		-|(% style="width:509px" %)(((
	760	+|SDMX data type (BasicComponentDataType)|Default VTL basic scalar type
	761	+|(((
640	640	String
	763	+
641	641	(string allowing any character)
642		-)))|(% style="width:312px" %)string
643		-|(% style="width:509px" %)(((
	765	+)))|string
	766	+|(((
644	644	Alpha
	768	+
645	645	(string which only allows A-z)
646		-)))|(% style="width:312px" %)string
647		-|(% style="width:509px" %)(((
	770	+)))|string
	771	+|(((
648	648	AlphaNumeric
	773	+
649	649	(string which only allows A-z and 0-9)
650		-)))|(% style="width:312px" %)string
651		-|(% style="width:509px" %)(((
	775	+)))|string
	776	+|(((
652	652	Numeric
	778	+
653	653	(string which only allows 0-9, but is not numeric so that is can having leading zeros)
654		-)))|(% style="width:312px" %)string
655		-|(% style="width:509px" %)(((
	780	+)))|string
	781	+|(((
656	656	BigInteger
	783	+
657	657	(corresponds to XML Schema xs:integer datatype; infinite set of integer values)
658		-)))|(% style="width:312px" %)integer
659		-|(% style="width:509px" %)(((
	785	+)))|integer
	786	+|(((
660	660	Integer
661		-(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647 (inclusive))
662		-)))|(% style="width:312px" %)integer
663		-|(% style="width:509px" %)(((
	788	+
	789	+(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647
	790	+
	791	+(inclusive))
	792	+)))|integer
	793	+|(((
664	664	Long
665		-(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and +9223372036854775807 (inclusive))
666		-)))|(% style="width:312px" %)integer
667		-|(% style="width:509px" %)(((
	795	+
	796	+(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and
	797	+
	798	++9223372036854775807 (inclusive))
	799	+)))|integer
	800	+|(((
668	668	Short
	802	+
669	669	(corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive))
670		-)))|(% style="width:312px" %)integer
671		-|(% 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
672		-|(% 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	+|(((
673	673	Float
	808	+
674	674	(corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type)
675		-)))|(% style="width:312px" %)number
676		-|(% style="width:509px" %)(((
	810	+)))|number
	811	+|(((
677	677	Double
	813	+
678	678	(corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type)
679		-)))|(% style="width:312px" %)number
680		-|(% style="width:509px" %)(((
	815	+)))|number
	816	+|(((
681	681	Boolean
682		-(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of binary-valued logic: {true, false})
683		-)))|(% style="width:312px" %)boolean
684	684
685		-(% style="width:822.294px" %)
686		-|(% 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" %)(((
687	687	URI
	826	+
688	688	(corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference)
689		-)))|(% colspan="1" style="width:311px" %)string
690		-|(% colspan="2" style="width:507px" %)(((
	828	+)))|(% colspan="2" %)string
	829	+| |(% colspan="2" %)(((
691	691	Count
	831	+
692	692	(an integer following a sequential pattern, increasing by 1 for each occurrence)
693		-)))|(% colspan="1" style="width:311px" %)integer
694		-|(% colspan="2" style="width:507px" %)(((
	833	+)))|(% colspan="2" %)integer
	834	+| |(% colspan="2" %)(((
695	695	InclusiveValueRange
	836	+
696	696	(decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
697		-)))|(% colspan="1" style="width:311px" %)number
698		-|(% colspan="2" style="width:507px" %)(((
	838	+)))|(% colspan="2" %)number
	839	+| |(% colspan="2" %)(((
699	699	ExclusiveValueRange
	841	+
700	700	(decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
701		-)))|(% colspan="1" style="width:311px" %)number
702		-|(% colspan="2" style="width:507px" %)(((
	843	+)))|(% colspan="2" %)number
	844	+| |(% colspan="2" %)(((
703	703	Incremental
	846	+
704	704	(decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation)
705		-)))|(% colspan="1" style="width:311px" %)number
706		-|(% colspan="2" style="width:507px" %)(((
	848	+)))|(% colspan="2" %)number
	849	+| |(% colspan="2" %)(((
707	707	ObservationalTimePeriod
	851	+
708	708	(superset of StandardTimePeriod and TimeRange)
709		-)))|(% colspan="1" style="width:311px" %)time
710		-|(% colspan="2" style="width:507px" %)(((
	853	+)))|(% colspan="2" %)time
	854	+| |(% colspan="2" %)(((
711	711	StandardTimePeriod
712		-(superset of BasicTimePeriod and ReportingTimePeriod)
713		-)))|(% colspan="1" style="width:311px" %)time
714		-|(% colspan="2" style="width:507px" %)(((
	856	+
	857	+(superset of BasicTimePeriod and
	858	+
	859	+ReportingTimePeriod)
	860	+)))|(% colspan="2" %)time
	861	+| |(% colspan="2" %)(((
715	715	BasicTimePeriod
	863	+
716	716	(superset of GregorianTimePeriod and DateTime)
717		-)))|(% colspan="1" style="width:311px" %)date
718		-|(% colspan="2" style="width:507px" %)(((
	865	+)))|(% colspan="2" %)date
	866	+| |(% colspan="2" %)(((
719	719	GregorianTimePeriod
	868	+
720	720	(superset of GregorianYear, GregorianYearMonth, and GregorianDay)
721		-)))|(% colspan="1" style="width:311px" %)date
722		-|(% colspan="2" style="width:507px" %)GregorianYear (YYYY)|(% colspan="1" style="width:311px" %)date
723		-|(% colspan="2" style="width:507px" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="1" style="width:311px" %)date
724		-|(% colspan="2" style="width:507px" %)GregorianDay (YYYY-MM-DD)|(% colspan="1" style="width:311px" %)date
725		-|(% 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" %)(((
726	726	ReportingTimePeriod
727		-(superset of RepostingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, ReportingDay)
728		-)))|(% colspan="1" style="width:311px" %)time_period
729		-|(% 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" %)(((
730	730	ReportingYear
	885	+
731	731	(YYYY-A1 – 1 year period)
732		-)))|(% colspan="1" style="width:311px" %)time_period
733		-|(% colspan="2" style="width:507px" %)(((
	887	+)))|(% colspan="2" %)time_period
	888	+| |(% colspan="2" %)(((
734	734	ReportingSemester
	890	+
735	735	(YYYY-Ss – 6 month period)
736		-)))|(% colspan="1" style="width:311px" %)time_period
737		-|(% colspan="2" style="width:507px" %)(((
	892	+)))|(% colspan="2" %)time_period
	893	+| |(% colspan="2" %)(((
738	738	ReportingTrimester
	895	+
739	739	(YYYY-Tt – 4 month period)
740		-)))|(% colspan="1" style="width:311px" %)time_period
741		-|(% colspan="2" style="width:507px" %)(((
	897	+)))|(% colspan="2" %)time_period
	898	+| |(% colspan="2" %)(((
742	742	ReportingQuarter
	900	+
743	743	(YYYY-Qq – 3 month period)
744		-)))|(% colspan="1" style="width:311px" %)time_period
745		-|(% colspan="2" style="width:507px" %)(((
	902	+)))|(% colspan="2" %)time_period
	903	+| |(% colspan="2" %)(((
746	746	ReportingMonth
	905	+
747	747	(YYYY-Mmm – 1 month period)
748		-)))|(% colspan="1" style="width:311px" %)time_period
749		-|(% colspan="2" style="width:507px" %)ReportingWeek|(% colspan="1" style="width:311px" %)time_period
750		-|(% 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" %)
751		-|(% 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" %)(((
752	752	ReportingDay
	914	+
753	753	(YYYY-Dddd – 1 day period)
754		-)))|(% colspan="2" style="width:312px" %)time_period
755		-|(% colspan="1" style="width:507px" %)(((
	916	+)))|(% colspan="2" %)time_period|
	917	+|(% colspan="2" %)(((
756	756	DateTime
	919	+
757	757	(YYYY-MM-DDThh:mm:ss)
758		-)))|(% colspan="2" style="width:312px" %)date
759		-|(% colspan="1" style="width:507px" %)(((
	921	+)))|(% colspan="2" %)date|
	922	+|(% colspan="2" %)(((
760	760	TimeRange
	924	+
761	761	(YYYY-MM-DD(Thh:mm:ss)?/<duration>)
762		-)))|(% colspan="2" style="width:312px" %)time
763		-|(% colspan="1" style="width:507px" %)(((
	926	+)))|(% colspan="2" %)time|
	927	+|(% colspan="2" %)(((
764	764	Month
	929	+
765	765	(~-~-MM; speicifies a month independent of a year; e.g. February is black history month in the United States)
766		-)))|(% colspan="2" style="width:312px" %)string
767		-|(% colspan="1" style="width:507px" %)(((
	931	+)))|(% colspan="2" %)string|
	932	+|(% colspan="2" %)(((
768	768	MonthDay
	934	+
769	769	(~-~-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)
770		-)))|(% colspan="2" style="width:312px" %)string
771		-|(% colspan="1" style="width:507px" %)(((
	936	+)))|(% colspan="2" %)string|
	937	+|(% colspan="2" %)(((
772	772	Day
	939	+
773	773	(~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday)
774		-)))|(% colspan="2" style="width:312px" %)string
775		-|(% colspan="1" style="width:507px" %)(((
	941	+)))|(% colspan="2" %)string|
	942	+|(% colspan="2" %)(((
776	776	Time
	944	+
777	777	(hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM)
778		-)))|(% colspan="2" style="width:312px" %)string
779		-|(% colspan="1" style="width:507px" %)(((
	946	+)))|(% colspan="2" %)string|
	947	+|(% colspan="2" %)(((
780	780	Duration
	949	+
781	781	(corresponds to XML Schema xs:duration datatype)
782		-)))|(% colspan="2" style="width:312px" %)duration
783		-|(% colspan="1" style="width:507px" %)XHTML|(% colspan="2" style="width:312px" %)Metadata type – not applicable
784		-|(% colspan="1" style="width:507px" %)KeyValues|(% colspan="2" style="width:312px" %)Metadata type – not applicable
785		-|(% colspan="1" style="width:507px" %)IdentifiableReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
786		-|(% 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|
787	787
788		-(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes" %)
789		-**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 ====
790	790
791	791	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).
792	792
...	...	@@ -794,32 +794,39 @@
794	794
795	795	The following table describes the default conversion from the VTL basic scalar types to the SDMX data types .
796	796
797		-(% style="width:1073.29px" %)
798		-|(% style="width:207px" %)(((
799		-**VTL basic scalar type**
800		-)))|(% style="width:462px" %)(((
801		-**Default SDMX data type (BasicComponentDataType)**
802		-)))|(% style="width:402px" %)**Default output format**
803		-|(% style="width:207px" %)String|(% style="width:462px" %)String|(% style="width:402px" %)Like XML (xs:string)
804		-|(% style="width:207px" %)Number|(% style="width:462px" %)Float|(% style="width:402px" %)Like XML (xs:float)
805		-|(% style="width:207px" %)Integer|(% style="width:462px" %)Integer|(% style="width:402px" %)Like XML (xs:int)
806		-|(% style="width:207px" %)Date|(% style="width:462px" %)DateTime|(% style="width:402px" %)YYYY-MM-DDT00:00:00Z
807		-|(% style="width:207px" %)Time|(% style="width:462px" %)StandardTimePeriod|(% style="width:402px" %)<date>/<date> (as defined above)
808		-|(% 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|(((
809	809	ReportingTimePeriod
	983	+
810	810	(StandardReportingPeriod)
811		-)))|(% style="width:402px" %)(((
	985	+)))|(((
812	812	YYYY-Pppp
	987	+
813	813	(according to SDMX )
814	814	)))
815		-|(% style="width:207px" %)Duration|(% style="width:462px" %)Duration|(% style="width:402px" %)(((
	990	+|Duration|Duration|(((
816	816	Like XML (xs:duration)
	992	+
817	817	PnYnMnDTnHnMnS
818	818	)))
819		-|(% 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"
820	820
821		-(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes-1" %)
822		-**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 ====
823	823
824	824	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).
825	825
...	...	@@ -873,7 +873,7 @@
873	873	|N|fixed number of digits used in the preceding textual representation of the month or the day
874	874	| |
875	875
876		-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"]](%%)^^.
877	877
878	878	=== 12.4.5 Null Values ===
879	879
...	...	@@ -891,8 +891,10 @@
891	891
892	892	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).
893	893
894		-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
895	895
	1071	+TransformationScheme.
	1072	+
896	896	In case a literal is operand of a VTL Cast operation, the format specified in the Cast overrides all the possible otherwise specified formats.
897	897
898	898	{{putFootnotes/}}