Changes for page 12 Validation and Transformation Language (VTL)

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...	...	@@ -14,8 +14,10 @@
14	14
15	15	The VTL language can be applied to SDMX artefacts by mapping the SDMX IM model artefacts to the model artefacts that VTL can manipulate{{footnote}}In this chapter, in order to distinguish VTL and SDMX model artefacts, the VTL ones are written in the Arial font while the SDMX ones in Courier New{{/footnote}}. Thus, the SDMX artefacts can be used in VTL as inputs and/or outputs of Transformations. It is important to be aware that the artefacts do not always have the same names in the SDMX and VTL IMs, nor do they always have the same meaning. The more evident example is given by the SDMX Dataset and the VTL "Data Set", which do not correspond one another: as a matter of fact, the VTL "Data Set" maps to the SDMX "Dataflow", while the SDMX "Dataset" has no explicit mapping to VTL (such an abstraction is not needed in the definition of VTL Transformations). A SDMX "Dataset", however, is an instance of a SDMX "Dataflow" and can be the artefact on which the VTL transformations are executed (i.e., the Transformations are defined on Dataflows and are applied to Dataflow instances that can be Datasets).
16	16
17		-The VTL programs (Transformation Schemes) are represented in SDMX through the TransformationScheme maintainable class which is composed of Transformation (nameable artefact). Each Transformation assigns the outcome of the evaluation of a VTL expression to a result.
	17	+The VTL programs (Transformation Schemes) are represented in SDMX through the TransformationScheme maintainable class which is composed of
18	18
	19	+Transformation (nameable artefact). Each Transformation assigns the outcome of the evaluation of a VTL expression to a result.
	20	+
19	19	This section does not explain the VTL language or any of the content published in the VTL guides. Rather, this is a description of how the VTL can be used in the SDMX context and applied to SDMX artefacts.
20	20
21	21	== 12.2 References to SDMX artefacts from VTL statements ==
...	...	@@ -26,8 +26,10 @@
26	26
27	27	The alias of an SDMX artefact can be its URN (Universal Resource Name), an abbreviation of its URN or another user-defined name.
28	28
29		-In any case, the aliases used in the VTL Transformations have to be mapped to the SDMX artefacts through the VtlMappingScheme and VtlMapping classes (see the section of the SDMX IM relevant to the VTL). A VtlMapping allows specifying the aliases to be used in the VTL Transformations, Rulesets{{footnote}}See also the section "VTL-DL Rulesets" in the VTL Reference Manual.{{/footnote}} or User Defined Operators{{footnote}}The VTLMappings are used also for User Defined Operators (UDO). Although UDOs are envisaged to be defined on generic operands, so that the specific artefacts to be manipulated are passed as parameters at their invocation, it is also possible that an UDO invokes directly some specific SDMX artefacts. These SDMX artefacts have to be mapped to the corresponding aliases used in the definition of the UDO through the VtlMappingScheme and VtlMapping classes as well.{{/footnote}} to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping.
	31	+In any case, the aliases used in the VTL Transformations have to be mapped to the
30	30
	33	+SDMX artefacts through the VtlMappingScheme and VtlMapping classes (see the section of the SDMX IM relevant to the VTL). A VtlMapping allows specifying the aliases to be used in the VTL Transformations, Rulesets{{footnote}}See also the section "VTL-DL Rulesets" in the VTL Reference Manual.{{/footnote}} or User Defined Operators{{footnote}}The VTLMappings are used also for User Defined Operators (UDO). Although UDOs are envisaged to be defined on generic operands, so that the specific artefacts to be manipulated are passed as parameters at their invocation, it is also possible that an UDO invokes directly some specific SDMX artefacts. These SDMX artefacts have to be mapped to the corresponding aliases used in the definition of the UDO through the VtlMappingScheme and VtlMapping classes as well.{{/footnote}} to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping.
	34	+
31	31	The correspondence between an alias and a SDMX artefact must be one-to-one, meaning that a generic alias identifies one and just one SDMX artefact while a SDMX artefact is identified by one and just one alias. In other words, within a VtlMappingScheme an artefact can have just one alias and different artefacts cannot have the same alias.
32	32
33	33	The references through the URN and the abbreviated URN are described in the following paragraphs.
...	...	@@ -198,7 +198,7 @@
198	198
199	199	=== 12.3.3 Mapping from SDMX to VTL data structures ===
200	200
201		-==== 12.3.3.1 Basic Mapping ====
	205	+**12.3.3.1 Basic Mapping**
202	202
203	203	The main mapping method from SDMX to VTL is called **Basic **mapping. This is considered as the default mapping method and is applied unless a different method is specified through the VtlMappingScheme and VtlDataflowMapping classes. When transforming **from SDMX to VTL**, this method consists in leaving the components unchanged and maintaining their names and roles, according to the following table:
204	204
...	...	@@ -228,11 +228,18 @@
228	228	The SDMX structures that contain a MeasureDimension are mapped as described below (this mapping is equivalent to a pivoting operation):
229	229
230	230	* A SDMX simple dimension becomes a VTL (simple) identifier and a SDMX TimeDimension becomes a VTL (time) identifier;
231		-* Each possible Code Cj of the SDMX MeasureDimension is mapped to a VTL Measure, having the same name as the SDMX Code (i.e. Cj); the VTL Measure Cj is a new VTL component even if the SDMX data structure has not such a Component;
	235	+* 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
	236	+
	237	+Component;
	238	+
232	232	* The SDMX MeasureDimension is not mapped to VTL (it disappears in the VTL Data Structure);
233	233	* The SDMX Measure is not mapped to VTL as well (it disappears in the VTL Data Structure);
234	234	* An SDMX DataAttribute is mapped in different ways according to its AttributeRelationship:
235		-** If, according to the SDMX AttributeRelationship, the values of the DataAttribute do not depend on the values of the MeasureDimension, the SDMX DataAttribute becomes a VTL Attribute having the same name. This happens if the AttributeRelationship is not specified (i.e. the DataAttribute does not depend on any DimensionComponent and therefore is at data set level), or if it refers to a set (or a group) of dimensions which does not include the MeasureDimension;
	242	+** 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
	243	+
	244	+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;
	245	+
	246	+*
236	236	** Otherwise, if, according to the SDMX AttributeRelationship, the values of the DataAttribute depend on the MeasureDimension, the SDMX DataAttribute is mapped to one VTL Attribute for each possible Code of the SDMX MeasureDimension. By default, the names of the VTL Attributes are obtained by concatenating the name of the SDMX DataAttribute and the names of the correspondent Code of the MeasureDimension separated by underscore. For example, if the SDMX DataAttribute is named DA and the possible Codes of the SDMX MeasureDimension are named C1, C2, …, Cn, then the corresponding VTL Attributes will be named DA_C1, DA_C2, …, DA_Cn (if different names are desired, they can be achieved afterwards by renaming the Attributes through VTL operators).
237	237	** Like in the Basic mapping, the resulting VTL Attributes are considered as dependent on all the VTL identifiers (i.e. "at data point / observation level"), because VTL does not have the SDMX notion of Attribute Relationship.
238	238
...	...	@@ -255,7 +255,10 @@
255	255	At observation / data point level, calling Cj (j=1, … n) the j^^th^^ Code of the MeasureDimension:
256	256
257	257	* The set of SDMX observations having the same values for all the Dimensions except than the MeasureDimension become one multi-measure VTL Data Point, having one Measure for each Code Cj of the SDMX MeasureDimension;
258		-* The values of the SDMX simple Dimensions, TimeDimension and DataAttributes not depending on the MeasureDimension (these components by definition have always the same values for all the observations of the set above) become the values of the corresponding VTL (simple) Identifiers, (time) Identifier and Attributes.
	269	+* 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)
	270	+
	271	+Identifiers, (time) Identifier and Attributes.
	272	+
259	259	* The value of the Measure of the SDMX observation belonging to the set above and having MeasureDimension=Cj becomes the value of the VTL Measure Cj
260	260	* For the SDMX DataAttributes depending on the MeasureDimension, the value of the DataAttribute DA of the SDMX observation belonging to the set above and having MeasureDimension=Cj becomes the value of the VTL Attribute DA_Cj
261	261
...	...	@@ -348,7 +348,7 @@
348	348	The mapping table is the following:
349	349
350	350	(% style="width:689.294px" %)
351		-|(% style="width:344px" %)**VTL**|(% style="width:341px" %)**SDMX**
	365	+|(% style="width:344px" %)VTL|(% style="width:341px" %)SDMX
352	352	|(% style="width:344px" %)(Simple) Identifier|(% style="width:341px" %)Dimension
353	353	|(% style="width:344px" %)(Time) Identifier|(% style="width:341px" %)TimeDimension
354	354	|(% style="width:344px" %)Some Measures|(% style="width:341px" %)Measure
...	...	@@ -408,16 +408,28 @@
408	408
409	409	SDMX Dataflow having INDICATOR=//INDICATORvalue //and COUNTRY=// COUNTRYvalue//. For example, the VTL dataset ‘DF1(1.0.0)/POPULATION.USA’ would contain all the observations of DF1(1.0.0) having INDICATOR = POPULATION and COUNTRY = USA.
410	410
411		-In order to obtain the data structure of these VTL Data Sets from the SDMX one, it is assumed that the SDMX DimensionComponents on which the mapping is based are dropped, i.e. not maintained in the VTL data structure; this is possible because their values are fixed for each one of the invoked VTL Data Sets{{footnote}}If these DimensionComponents would not be dropped, the various VTL Data Sets resulting from this kind of mapping would have non-matching values for the Identifiers corresponding to the mapping Dimensions (e.g. POPULATION and COUNTRY). As a consequence, taking into account that the typical binary VTL operations at dataset level (+, -, *, / and so on) are executed on the observations having matching values for the identifiers, it would not be possible to compose the resulting VTL datasets one another (e.g. it would not be possible to calculate the population ratio between USA and CANADA).{{/footnote}}. After that, the mapping method from SDMX to VTL specified for the Dataflow DF1(1.0.0) is applied (i.e. basic, pivot …).
	425	+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.
412	412
413		-In the example above, for all the datasets of the kind ‘DF1(1.0.0)///INDICATORvalue//.//COUNTRYvalue//’, the dimensions INDICATOR and COUNTRY would be dropped so that the data structure of all the resulting VTL Data Sets would have the identifier TIME_PERIOD only.
	427	+basic, pivot …).
414	414
	429	+In the example above, for all the datasets of the kind
	430	+
	431	+‘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.
	432	+
415	415	It should be noted that the desired VTL Data Sets (i.e. of the kind ‘DF1(1.0.0)/// INDICATORvalue//.//COUNTRYvalue//’) can be obtained also by applying the VTL operator “**sub**” (subspace) to the Dataflow DF1(1.0.0), like in the following VTL expression:
416	416
417		-[[image:1747388275998-621.png]]
	435	+‘DF1(1.0.0)/POPULATION.USA’ :=
418	418
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}}
	437	+DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA” ];
420	420
	439	+‘DF1(1.0.0)/POPULATION.CANADA’ :=
	440	+
	441	+DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
	442	+
	443	+… … …
	444	+
	445	+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 wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^32^^>>path:#sdfootnote32sym||name="sdfootnote32anc"]](%%)^^
	446	+
421	421	In the direction from SDMX to VTL it is allowed to omit the value of one or more
422	422
423	423	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.
...	...	@@ -426,8 +426,10 @@
426	426
427	427	This is equivalent to the application of the VTL “sub” operator only to the identifier //INDICATOR//:
428	428
429		-[[image:1747388244829-693.png]]
	455	+‘DF1(1.0.0)/POPULATION.’ :=
430	430
	457	+DF1(1.0.0) [ sub INDICATOR=“POPULATION” ];
	458	+
431	431	Therefore the VTL Data Set ‘DF1(1.0.0)/POPULATION.’ would have the identifiers COUNTRY and TIME_PERIOD.
432	432
433	433	Heterogeneous invocations of the same Dataflow are allowed, i.e. omitting different
...	...	@@ -442,34 +442,70 @@
442	442
443	443	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:
444	444
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}}
	473	+* 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 wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^33^^>>path:#sdfootnote33sym||name="sdfootnote33anc"]](%%)^^
	474	+* 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 wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^34^^>>path:#sdfootnote34sym||name="sdfootnote34anc"]](%%)^^
447	447
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}}.
	476	+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 wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^35^^>>path:#sdfootnote35sym||name="sdfootnote35anc"]](%%)^^.
449	449
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}}
	478	+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 wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^36^^>>path:#sdfootnote36sym||name="sdfootnote36anc"]](%%)^^
451	451
452	452	‘DF2(1.0.0)/INDICATORvalue.COUNTRYvalue’ <- expression
453	453
454	454	Some examples follow, for some specific values of INDICATOR and COUNTRY:
455	455
456		-[[image:1747388222879-916.png]]
	484	+‘DF2(1.0.0)/GDPPERCAPITA.USA’ <- expression11; ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ <- expression12;
457	457
458		-[[image:1747388206717-256.png]]
	486	+… … …
459	459
	488	+‘DF2(1.0.0)/POPGROWTH.USA’ <- expression21;
	489	+
	490	+‘DF2(1.0.0)/POPGROWTH.CANADA’ <- expression22;
	491	+
	492	+… … …
	493	+
460	460	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:
461	461
462		-[[image:1747388148322-387.png]]
	496	+VTL dataset INDICATOR value COUNTRY value
463	463
	498	+‘DF2(1.0.0)/GDPPERCAPITA.USA’ GDPPERCAPITA USA
	499	+
	500	+‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ GDPPERCAPITA CANADA … … …
	501	+
	502	+‘DF2(1.0.0)/POPGROWTH.USA’ POPGROWTH USA
	503	+
	504	+‘DF2(1.0.0)/POPGROWTH.CANADA’ POPGROWTH CANADA
	505	+
	506	+… … …
	507	+
464	464	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:
465	465
466		-[[image:1747388179021-814.png]]
	510	+DF2bis_GDPPERCAPITA_USA := ‘DF2(1.0.0)/GDPPERCAPITA.USA’ [calc identifier INDICATOR := ”GDPPERCAPITA”, identifier COUNTRY := ”USA”];
467	467
	512	+DF2bis_GDPPERCAPITA_CANADA := ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ [calc identifier INDICATOR:=”GDPPERCAPITA”, identifier COUNTRY:=”CANADA”]; … … …
	513	+
	514	+DF2bis_POPGROWTH_USA := ‘DF2(1.0.0)/POPGROWTH.USA’
	515	+
	516	+[calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”USA”];
	517	+
	518	+DF2bis_POPGROWTH_CANADA’ := ‘DF2(1.0.0)/POPGROWTH.CANADA’ [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”CANADA”]; … … …
	519	+
	520	+DF2(1.0) <- UNION (DF2bis_GDPPERCAPITA_USA’,
	521	+
	522	+DF2bis_GDPPERCAPITA_CANADA’,
	523	+
	524	+… ,
	525	+
	526	+DF2bis_POPGROWTH_USA’,
	527	+
	528	+DF2bis_POPGROWTH_CANADA’
	529	+
	530	+…);
	531	+
468	468	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
469	469
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.
	534	+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 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.
471	471
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}}
	536	+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 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 wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^39^^>>path:#sdfootnote39sym||name="sdfootnote39anc"]](%%)^^
473	473
474	474	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).
475	475
...	...	@@ -477,44 +477,52 @@
477	477
478	478	With reference to the VTL “model for Variables and Value domains”, the following additional mappings have to be considered:
479	479
480		-(% style="width:1170.29px" %)
481		-|(% style="width:392px" %)**VTL**|(% style="width:776px" %)**SDMX**
482		-|(% style="width:392px" %)**Data Set Component**|(% style="width:776px" %)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:392px" %)**Represented Variable**|(% style="width:776px" %)(((
	544	+|VTL|SDMX
	545	+|**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^^
	546	+|**Represented Variable**|(((
484	484	**Concept** with a definite
485	485
486	486	Representation
487	487	)))
488		-|(% style="width:392px" %)**Value Domain**|(% style="width:776px" %)(((
489		-**Representation** (see the Structure Pattern in the Base Package)
	551	+|**Value Domain**|(((
	552	+**Representation** (see the Structure
	553	+
	554	+Pattern in the Base Package)
490	490	)))
491		-|(% style="width:392px" %)**Enumerated Value Domain / Code List**|(% style="width:776px" %)**Codelist**
492		-|(% style="width:392px" %)**Code**|(% style="width:776px" %)(((
	556	+|**Enumerated Value Domain / Code List**|**Codelist**
	557	+|**Code**|(((
493	493	**Code** (for enumerated
494	494
495	495	DimensionComponent, Measure, DataAttribute)
496	496	)))
497		-|(% style="width:392px" %)**Described Value Domain**|(% style="width:776px" %)(((
498		-non-enumerated** Representation **(having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
	562	+|**Described Value Domain**|(((
	563	+non-enumerated** &nbsp;&nbsp;&nbsp;Representation**
	564	+
	565	+(having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
499	499	)))
500		-|(% style="width:392px" %)**Value**|(% style="width:776px" %)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)
501		-|(% style="width:392px" %)**Value Domain Subset / Set**|(% style="width:776px" %)This abstraction does not exist in SDMX
502		-|(% style="width:392px" %)**Enumerated Value Domain Subset / Enumerated Set**|(% style="width:776px" %)This abstraction does not exist in SDMX
503		-|(% style="width:392px" %)**Described Value Domain Subset / Described Set**|(% style="width:776px" %)This abstraction does not exist in SDMX
504		-|(% style="width:392px" %)**Set list**|(% style="width:776px" %)This abstraction does not exist in SDMX
	567	+|**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
	568	+| |(((
	569	+to a valid **value &nbsp;&nbsp;&nbsp;**(for non-enumerated** &nbsp;&nbsp;&nbsp;**
505	505
	571	+Representations)
	572	+)))
	573	+|**Value Domain Subset / Set**|This abstraction does not exist in SDMX
	574	+|**Enumerated Value Domain Subset / Enumerated Set**|This abstraction does not exist in SDMX
	575	+|**Described Value Domain Subset / Described Set**|This abstraction does not exist in SDMX
	576	+|**Set list**|This abstraction does not exist in SDMX
	577	+
506	506	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).
507	507
508		-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.
	580	+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 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 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.
509	509
510	510	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
511	511
512		-DS_c := DS_a + DS_b (where DS_a, DS_b, DS_c are VTL Data Sets)
	584	+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.
513	513
514		-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.
515		-
516	516	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
517	517
	588	+[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_59eee18f.gif||alt="Shape5" height="1" width="192"]]
	589	+
518	518	Transformations to ensure that the VTL expressions are consistent with the actual representations of the correspondent SDMX Concepts.
519	519
520	520	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.
...	...	@@ -529,8 +529,7 @@
529	529
530	530	[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_e3df33ae.png||height="543" width="483"]]
531	531
532		-(% class="wikigeneratedid" id="HFigure222013VTLDataTypes" %)
533		-**Figure 22 – VTL Data Types**
	604	+==== Figure 22 – VTL Data Types ====
534	534
535	535	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.
536	536
...	...	@@ -537,12 +537,131 @@
537	537	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):
538	538
539	539
540		-**Figure 23 – VTL Basic Scalar Types**
541	541
542	542	(((
543		-
	613	+//n//
	614	+
	615	+//a//
	616	+
	617	+//e//
	618	+
	619	+//l//
	620	+
	621	+//o//
	622	+
	623	+//o//
	624	+
	625	+//B//
	626	+
	627	+//n//
	628	+
	629	+//o//
	630	+
	631	+//i//
	632	+
	633	+//t//
	634	+
	635	+//a//
	636	+
	637	+//r//
	638	+
	639	+//u//
	640	+
	641	+//D//
	642	+
	643	+//d//
	644	+
	645	+//o//
	646	+
	647	+//i//
	648	+
	649	+//r//
	650	+
	651	+//e//
	652	+
	653	+//p//
	654	+
	655	+//_//
	656	+
	657	+//e//
	658	+
	659	+//m//
	660	+
	661	+//i//
	662	+
	663	+//T//
	664	+
	665	+//e//
	666	+
	667	+//t//
	668	+
	669	+//a//
	670	+
	671	+//D//
	672	+
	673	+//e//
	674	+
	675	+//m//
	676	+
	677	+//i//
	678	+
	679	+//T//
	680	+
	681	+//r//
	682	+
	683	+//e//
	684	+
	685	+//g//
	686	+
	687	+//e//
	688	+
	689	+//t//
	690	+
	691	+//n//
	692	+
	693	+//I//
	694	+
	695	+//r//
	696	+
	697	+//e//
	698	+
	699	+//b//
	700	+
	701	+//m//
	702	+
	703	+//u//
	704	+
	705	+//N//
	706	+
	707	+//g//
	708	+
	709	+//n//
	710	+
	711	+//i//
	712	+
	713	+//r//
	714	+
	715	+//t//
	716	+
	717	+//S//
	718	+
	719	+//r//
	720	+
	721	+//a//
	722	+
	723	+//l//
	724	+
	725	+//a//
	726	+
	727	+//c//
	728	+
	729	+//S//
	730	+
	731	+[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_82d45833.gif||alt="Shape6" height="231" width="184"]]
544	544	)))
545	545
	734	+==== Figure 23 – VTL Basic Scalar Types ====
	735	+
546	546	=== 12.4.2 VTL basic scalar types and SDMX data types ===
547	547
548	548	The VTL assumes that a basic scalar type has a unique internal representation and can have more external representations.
...	...	@@ -565,159 +565,204 @@
565	565
566	566	The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types.
567	567
568		-(% style="width:823.294px" %)
569		-|(% style="width:509px" %)**SDMX data type (BasicComponentDataType)**|(% style="width:312px" %)**Default VTL basic scalar type**
570		-|(% style="width:509px" %)(((
	758	+|SDMX data type (BasicComponentDataType)|Default VTL basic scalar type
	759	+|(((
571	571	String
	761	+
572	572	(string allowing any character)
573		-)))|(% style="width:312px" %)string
574		-|(% style="width:509px" %)(((
	763	+)))|string
	764	+|(((
575	575	Alpha
	766	+
576	576	(string which only allows A-z)
577		-)))|(% style="width:312px" %)string
578		-|(% style="width:509px" %)(((
	768	+)))|string
	769	+|(((
579	579	AlphaNumeric
	771	+
580	580	(string which only allows A-z and 0-9)
581		-)))|(% style="width:312px" %)string
582		-|(% style="width:509px" %)(((
	773	+)))|string
	774	+|(((
583	583	Numeric
	776	+
584	584	(string which only allows 0-9, but is not numeric so that is can having leading zeros)
585		-)))|(% style="width:312px" %)string
586		-|(% style="width:509px" %)(((
	778	+)))|string
	779	+|(((
587	587	BigInteger
	781	+
588	588	(corresponds to XML Schema xs:integer datatype; infinite set of integer values)
589		-)))|(% style="width:312px" %)integer
590		-|(% style="width:509px" %)(((
	783	+)))|integer
	784	+|(((
591	591	Integer
592		-(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647 (inclusive))
593		-)))|(% style="width:312px" %)integer
594		-|(% style="width:509px" %)(((
	786	+
	787	+(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647
	788	+
	789	+(inclusive))
	790	+)))|integer
	791	+|(((
595	595	Long
596		-(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and +9223372036854775807 (inclusive))
597		-)))|(% style="width:312px" %)integer
598		-|(% style="width:509px" %)(((
	793	+
	794	+(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and
	795	+
	796	++9223372036854775807 (inclusive))
	797	+)))|integer
	798	+|(((
599	599	Short
	800	+
600	600	(corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive))
601		-)))|(% style="width:312px" %)integer
602		-|(% 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
603		-|(% style="width:509px" %)(((
	802	+)))|integer
	803	+|Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|number
	804	+|(((
604	604	Float
	806	+
605	605	(corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type)
606		-)))|(% style="width:312px" %)number
607		-|(% style="width:509px" %)(((
	808	+)))|number
	809	+|(((
608	608	Double
	811	+
609	609	(corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type)
610		-)))|(% style="width:312px" %)number
611		-|(% style="width:509px" %)(((
	813	+)))|number
	814	+|(((
612	612	Boolean
613		-(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of binary-valued logic: {true, false})
614		-)))|(% style="width:312px" %)boolean
615	615
616		-(% style="width:822.294px" %)
617		-|(% colspan="2" style="width:507px" %)(((
	817	+(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of
	818	+
	819	+binary-valued logic: {true, false})
	820	+)))|boolean
	821	+
	822	+| |(% colspan="2" %)(((
618	618	URI
	824	+
619	619	(corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference)
620		-)))|(% colspan="1" style="width:311px" %)string
621		-|(% colspan="2" style="width:507px" %)(((
	826	+)))|(% colspan="2" %)string
	827	+| |(% colspan="2" %)(((
622	622	Count
	829	+
623	623	(an integer following a sequential pattern, increasing by 1 for each occurrence)
624		-)))|(% colspan="1" style="width:311px" %)integer
625		-|(% colspan="2" style="width:507px" %)(((
	831	+)))|(% colspan="2" %)integer
	832	+| |(% colspan="2" %)(((
626	626	InclusiveValueRange
	834	+
627	627	(decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
628		-)))|(% colspan="1" style="width:311px" %)number
629		-|(% colspan="2" style="width:507px" %)(((
	836	+)))|(% colspan="2" %)number
	837	+| |(% colspan="2" %)(((
630	630	ExclusiveValueRange
	839	+
631	631	(decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
632		-)))|(% colspan="1" style="width:311px" %)number
633		-|(% colspan="2" style="width:507px" %)(((
	841	+)))|(% colspan="2" %)number
	842	+| |(% colspan="2" %)(((
634	634	Incremental
	844	+
635	635	(decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation)
636		-)))|(% colspan="1" style="width:311px" %)number
637		-|(% colspan="2" style="width:507px" %)(((
	846	+)))|(% colspan="2" %)number
	847	+| |(% colspan="2" %)(((
638	638	ObservationalTimePeriod
	849	+
639	639	(superset of StandardTimePeriod and TimeRange)
640		-)))|(% colspan="1" style="width:311px" %)time
641		-|(% colspan="2" style="width:507px" %)(((
	851	+)))|(% colspan="2" %)time
	852	+| |(% colspan="2" %)(((
642	642	StandardTimePeriod
643		-(superset of BasicTimePeriod and ReportingTimePeriod)
644		-)))|(% colspan="1" style="width:311px" %)time
645		-|(% colspan="2" style="width:507px" %)(((
	854	+
	855	+(superset of BasicTimePeriod and
	856	+
	857	+ReportingTimePeriod)
	858	+)))|(% colspan="2" %)time
	859	+| |(% colspan="2" %)(((
646	646	BasicTimePeriod
	861	+
647	647	(superset of GregorianTimePeriod and DateTime)
648		-)))|(% colspan="1" style="width:311px" %)date
649		-|(% colspan="2" style="width:507px" %)(((
	863	+)))|(% colspan="2" %)date
	864	+| |(% colspan="2" %)(((
650	650	GregorianTimePeriod
	866	+
651	651	(superset of GregorianYear, GregorianYearMonth, and GregorianDay)
652		-)))|(% colspan="1" style="width:311px" %)date
653		-|(% colspan="2" style="width:507px" %)GregorianYear (YYYY)|(% colspan="1" style="width:311px" %)date
654		-|(% colspan="2" style="width:507px" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="1" style="width:311px" %)date
655		-|(% colspan="2" style="width:507px" %)GregorianDay (YYYY-MM-DD)|(% colspan="1" style="width:311px" %)date
656		-|(% colspan="2" style="width:507px" %)(((
	868	+)))|(% colspan="2" %)date
	869	+| |(% colspan="2" %)GregorianYear (YYYY)|(% colspan="2" %)date
	870	+| |(% colspan="2" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="2" %)date
	871	+| |(% colspan="2" %)GregorianDay (YYYY-MM-DD)|(% colspan="2" %)date
	872	+| |(% colspan="2" %)(((
657	657	ReportingTimePeriod
658		-(superset of RepostingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, ReportingDay)
659		-)))|(% colspan="1" style="width:311px" %)time_period
660		-|(% colspan="2" style="width:507px" %)(((
	874	+
	875	+(superset of RepostingYear, ReportingSemester,
	876	+
	877	+ReportingTrimester, ReportingQuarter,
	878	+
	879	+ReportingMonth, ReportingWeek, ReportingDay)
	880	+)))|(% colspan="2" %)time_period
	881	+| |(% colspan="2" %)(((
661	661	ReportingYear
	883	+
662	662	(YYYY-A1 – 1 year period)
663		-)))|(% colspan="1" style="width:311px" %)time_period
664		-|(% colspan="2" style="width:507px" %)(((
	885	+)))|(% colspan="2" %)time_period
	886	+| |(% colspan="2" %)(((
665	665	ReportingSemester
	888	+
666	666	(YYYY-Ss – 6 month period)
667		-)))|(% colspan="1" style="width:311px" %)time_period
668		-|(% colspan="2" style="width:507px" %)(((
	890	+)))|(% colspan="2" %)time_period
	891	+| |(% colspan="2" %)(((
669	669	ReportingTrimester
	893	+
670	670	(YYYY-Tt – 4 month period)
671		-)))|(% colspan="1" style="width:311px" %)time_period
672		-|(% colspan="2" style="width:507px" %)(((
	895	+)))|(% colspan="2" %)time_period
	896	+| |(% colspan="2" %)(((
673	673	ReportingQuarter
	898	+
674	674	(YYYY-Qq – 3 month period)
675		-)))|(% colspan="1" style="width:311px" %)time_period
676		-|(% colspan="2" style="width:507px" %)(((
	900	+)))|(% colspan="2" %)time_period
	901	+| |(% colspan="2" %)(((
677	677	ReportingMonth
	903	+
678	678	(YYYY-Mmm – 1 month period)
679		-)))|(% colspan="1" style="width:311px" %)time_period
680		-|(% colspan="2" style="width:507px" %)ReportingWeek|(% colspan="1" style="width:311px" %)time_period
681		-|(% 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" %)
682		-|(% colspan="1" style="width:507px" %)(((
	905	+)))|(% colspan="2" %)time_period
	906	+| |(% colspan="2" %)ReportingWeek|(% colspan="2" %)time_period
	907	+| |(% colspan="2" %) |(% colspan="2" %)
	908	+| |(% colspan="2" %) |(% colspan="2" %)
	909	+|(% colspan="2" %)(YYYY-Www – 7 day period; following ISO 8601 definition of a week in a year)|(% colspan="2" %) |
	910	+|(% colspan="2" %)(((
683	683	ReportingDay
	912	+
684	684	(YYYY-Dddd – 1 day period)
685		-)))|(% colspan="2" style="width:312px" %)time_period
686		-|(% colspan="1" style="width:507px" %)(((
	914	+)))|(% colspan="2" %)time_period|
	915	+|(% colspan="2" %)(((
687	687	DateTime
	917	+
688	688	(YYYY-MM-DDThh:mm:ss)
689		-)))|(% colspan="2" style="width:312px" %)date
690		-|(% colspan="1" style="width:507px" %)(((
	919	+)))|(% colspan="2" %)date|
	920	+|(% colspan="2" %)(((
691	691	TimeRange
	922	+
692	692	(YYYY-MM-DD(Thh:mm:ss)?/<duration>)
693		-)))|(% colspan="2" style="width:312px" %)time
694		-|(% colspan="1" style="width:507px" %)(((
	924	+)))|(% colspan="2" %)time|
	925	+|(% colspan="2" %)(((
695	695	Month
	927	+
696	696	(~-~-MM; speicifies a month independent of a year; e.g. February is black history month in the United States)
697		-)))|(% colspan="2" style="width:312px" %)string
698		-|(% colspan="1" style="width:507px" %)(((
	929	+)))|(% colspan="2" %)string|
	930	+|(% colspan="2" %)(((
699	699	MonthDay
	932	+
700	700	(~-~-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)
701		-)))|(% colspan="2" style="width:312px" %)string
702		-|(% colspan="1" style="width:507px" %)(((
	934	+)))|(% colspan="2" %)string|
	935	+|(% colspan="2" %)(((
703	703	Day
	937	+
704	704	(~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday)
705		-)))|(% colspan="2" style="width:312px" %)string
706		-|(% colspan="1" style="width:507px" %)(((
	939	+)))|(% colspan="2" %)string|
	940	+|(% colspan="2" %)(((
707	707	Time
	942	+
708	708	(hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM)
709		-)))|(% colspan="2" style="width:312px" %)string
710		-|(% colspan="1" style="width:507px" %)(((
	944	+)))|(% colspan="2" %)string|
	945	+|(% colspan="2" %)(((
711	711	Duration
	947	+
712	712	(corresponds to XML Schema xs:duration datatype)
713		-)))|(% colspan="2" style="width:312px" %)duration
714		-|(% colspan="1" style="width:507px" %)XHTML|(% colspan="2" style="width:312px" %)Metadata type – not applicable
715		-|(% colspan="1" style="width:507px" %)KeyValues|(% colspan="2" style="width:312px" %)Metadata type – not applicable
716		-|(% colspan="1" style="width:507px" %)IdentifiableReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
717		-|(% colspan="1" style="width:507px" %)DataSetReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
	949	+)))|(% colspan="2" %)duration|
	950	+|(% colspan="2" %)XHTML|(% colspan="2" %)Metadata type – not applicable|
	951	+|(% colspan="2" %)KeyValues|(% colspan="2" %)Metadata type – not applicable|
	952	+|(% colspan="2" %)IdentifiableReference|(% colspan="2" %)Metadata type – not applicable|
	953	+|(% colspan="2" %)DataSetReference|(% colspan="2" %)Metadata type – not applicable|
718	718
719		-(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes" %)
720		-**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
	955	+==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
721	721
722	722	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).
723	723
...	...	@@ -725,32 +725,39 @@
725	725
726	726	The following table describes the default conversion from the VTL basic scalar types to the SDMX data types .
727	727
728		-(% style="width:1073.29px" %)
729		-|(% style="width:207px" %)(((
730		-**VTL basic scalar type**
731		-)))|(% style="width:462px" %)(((
732		-**Default SDMX data type (BasicComponentDataType)**
733		-)))|(% style="width:402px" %)**Default output format**
734		-|(% style="width:207px" %)String|(% style="width:462px" %)String|(% style="width:402px" %)Like XML (xs:string)
735		-|(% style="width:207px" %)Number|(% style="width:462px" %)Float|(% style="width:402px" %)Like XML (xs:float)
736		-|(% style="width:207px" %)Integer|(% style="width:462px" %)Integer|(% style="width:402px" %)Like XML (xs:int)
737		-|(% style="width:207px" %)Date|(% style="width:462px" %)DateTime|(% style="width:402px" %)YYYY-MM-DDT00:00:00Z
738		-|(% style="width:207px" %)Time|(% style="width:462px" %)StandardTimePeriod|(% style="width:402px" %)<date>/<date> (as defined above)
739		-|(% style="width:207px" %)time_period|(% style="width:462px" %)(((
	963	+|(((
	964	+VTL basic
	965	+
	966	+scalar type
	967	+)))|(((
	968	+Default SDMX data type
	969	+
	970	+(BasicComponentDataType
	971	+
	972	+)
	973	+)))|Default output format
	974	+|String|String|Like XML (xs:string)
	975	+|Number|Float|Like XML (xs:float)
	976	+|Integer|Integer|Like XML (xs:int)
	977	+|Date|DateTime|YYYY-MM-DDT00:00:00Z
	978	+|Time|StandardTimePeriod|<date>/<date> (as defined above)
	979	+|time_period|(((
740	740	ReportingTimePeriod
	981	+
741	741	(StandardReportingPeriod)
742		-)))|(% style="width:402px" %)(((
	983	+)))|(((
743	743	YYYY-Pppp
	985	+
744	744	(according to SDMX )
745	745	)))
746		-|(% style="width:207px" %)Duration|(% style="width:462px" %)Duration|(% style="width:402px" %)(((
	988	+|Duration|Duration|(((
747	747	Like XML (xs:duration)
	990	+
748	748	PnYnMnDTnHnMnS
749	749	)))
750		-|(% style="width:207px" %)Boolean|(% style="width:462px" %)Boolean|(% style="width:402px" %)Like XML (xs:boolean) with the values "true" or "false"
	993	+|Boolean|Boolean|Like XML (xs:boolean) with the values "true" or "false"
751	751
752		-(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes-1" %)
753		-**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
	995	+==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
754	754
755	755	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).
756	756
...	...	@@ -804,7 +804,7 @@
804	804	|N|fixed number of digits used in the preceding textual representation of the month or the day
805	805	| |
806	806
807		-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}}.
	1049	+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 wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^42^^>>path:#sdfootnote42sym||name="sdfootnote42anc"]](%%)^^.
808	808
809	809	=== 12.4.5 Null Values ===
810	810
...	...	@@ -822,8 +822,10 @@
822	822
823	823	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).
824	824
825		-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.
	1067	+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
826	826
	1069	+TransformationScheme.
	1070	+
827	827	In case a literal is operand of a VTL Cast operation, the format specified in the Cast overrides all the possible otherwise specified formats.
828	828
829	829	{{putFootnotes/}}

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