Changes for page 12 Validation and Transformation Language (VTL)

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...	...	@@ -14,10 +14,8 @@
14	14
15	15	The VTL language can be applied to SDMX artefacts by mapping the SDMX IM model artefacts to the model artefacts that VTL can manipulate{{footnote}}In this chapter, in order to distinguish VTL and SDMX model artefacts, the VTL ones are written in the Arial font while the SDMX ones in Courier New{{/footnote}}. Thus, the SDMX artefacts can be used in VTL as inputs and/or outputs of Transformations. It is important to be aware that the artefacts do not always have the same names in the SDMX and VTL IMs, nor do they always have the same meaning. The more evident example is given by the SDMX Dataset and the VTL "Data Set", which do not correspond one another: as a matter of fact, the VTL "Data Set" maps to the SDMX "Dataflow", while the SDMX "Dataset" has no explicit mapping to VTL (such an abstraction is not needed in the definition of VTL Transformations). A SDMX "Dataset", however, is an instance of a SDMX "Dataflow" and can be the artefact on which the VTL transformations are executed (i.e., the Transformations are defined on Dataflows and are applied to Dataflow instances that can be Datasets).
16	16
17		-The VTL programs (Transformation Schemes) are represented in SDMX through the TransformationScheme maintainable class which is composed of
	17	+The VTL programs (Transformation Schemes) are represented in SDMX through the TransformationScheme maintainable class which is composed of Transformation (nameable artefact). Each Transformation assigns the outcome of the evaluation of a VTL expression to a result.
18	18
19		-Transformation (nameable artefact). Each Transformation assigns the outcome of the evaluation of a VTL expression to a result.
20		-
21	21	This section does not explain the VTL language or any of the content published in the VTL guides. Rather, this is a description of how the VTL can be used in the SDMX context and applied to SDMX artefacts.
22	22
23	23	== 12.2 References to SDMX artefacts from VTL statements ==
...	...	@@ -28,10 +28,8 @@
28	28
29	29	The alias of an SDMX artefact can be its URN (Universal Resource Name), an abbreviation of its URN or another user-defined name.
30	30
31		-In any case, the aliases used in the VTL Transformations have to be mapped to the
	29	+In any case, the aliases used in the VTL Transformations have to be mapped to the SDMX artefacts through the VtlMappingScheme and VtlMapping classes (see the section of the SDMX IM relevant to the VTL). A VtlMapping allows specifying the aliases to be used in the VTL Transformations, Rulesets{{footnote}}See also the section "VTL-DL Rulesets" in the VTL Reference Manual.{{/footnote}} or User Defined Operators{{footnote}}The VTLMappings are used also for User Defined Operators (UDO). Although UDOs are envisaged to be defined on generic operands, so that the specific artefacts to be manipulated are passed as parameters at their invocation, it is also possible that an UDO invokes directly some specific SDMX artefacts. These SDMX artefacts have to be mapped to the corresponding aliases used in the definition of the UDO through the VtlMappingScheme and VtlMapping classes as well.{{/footnote}} to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping.
32	32
33		-SDMX artefacts through the VtlMappingScheme and VtlMapping classes (see the section of the SDMX IM relevant to the VTL). A VtlMapping allows specifying the aliases to be used in the VTL Transformations, Rulesets{{footnote}}See also the section "VTL-DL Rulesets" in the VTL Reference Manual.{{/footnote}} or User Defined Operators{{footnote}}The VTLMappings are used also for User Defined Operators (UDO). Although UDOs are envisaged to be defined on generic operands, so that the specific artefacts to be manipulated are passed as parameters at their invocation, it is also possible that an UDO invokes directly some specific SDMX artefacts. These SDMX artefacts have to be mapped to the corresponding aliases used in the definition of the UDO through the VtlMappingScheme and VtlMapping classes as well.{{/footnote}} to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping.
34		-
35	35	The correspondence between an alias and a SDMX artefact must be one-to-one, meaning that a generic alias identifies one and just one SDMX artefact while a SDMX artefact is identified by one and just one alias. In other words, within a VtlMappingScheme an artefact can have just one alias and different artefacts cannot have the same alias.
36	36
37	37	The references through the URN and the abbreviated URN are described in the following paragraphs.
...	...	@@ -202,7 +202,7 @@
202	202
203	203	=== 12.3.3 Mapping from SDMX to VTL data structures ===
204	204
205		-**12.3.3.1 Basic Mapping**
	201	+==== 12.3.3.1 Basic Mapping ====
206	206
207	207	The main mapping method from SDMX to VTL is called **Basic **mapping. This is considered as the default mapping method and is applied unless a different method is specified through the VtlMappingScheme and VtlDataflowMapping classes. When transforming **from SDMX to VTL**, this method consists in leaving the components unchanged and maintaining their names and roles, according to the following table:
208	208
...	...	@@ -232,18 +232,11 @@
232	232	The SDMX structures that contain a MeasureDimension are mapped as described below (this mapping is equivalent to a pivoting operation):
233	233
234	234	* A SDMX simple dimension becomes a VTL (simple) identifier and a SDMX TimeDimension becomes a VTL (time) identifier;
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		-
	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;
239	239	* The SDMX MeasureDimension is not mapped to VTL (it disappears in the VTL Data Structure);
240	240	* The SDMX Measure is not mapped to VTL as well (it disappears in the VTL Data Structure);
241	241	* An SDMX DataAttribute is mapped in different ways according to its AttributeRelationship:
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		-*
	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;
247	247	** 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).
248	248	** 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.
249	249
...	...	@@ -266,10 +266,7 @@
266	266	At observation / data point level, calling Cj (j=1, … n) the j^^th^^ Code of the MeasureDimension:
267	267
268	268	* 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;
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		-
	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.
273	273	* 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
274	274	* 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
275	275
...	...	@@ -362,7 +362,7 @@
362	362	The mapping table is the following:
363	363
364	364	(% style="width:689.294px" %)
365		-|(% style="width:344px" %)VTL|(% style="width:341px" %)SDMX
	351	+|(% style="width:344px" %)**VTL**|(% style="width:341px" %)**SDMX**
366	366	|(% style="width:344px" %)(Simple) Identifier|(% style="width:341px" %)Dimension
367	367	|(% style="width:344px" %)(Time) Identifier|(% style="width:341px" %)TimeDimension
368	368	|(% style="width:344px" %)Some Measures|(% style="width:341px" %)Measure
...	...	@@ -422,26 +422,14 @@
422	422
423	423	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.
424	424
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.
	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 …).
426	426
427		-basic, pivot …).
	413	+In the example above, for all the datasets of the kind ‘DF1(1.0.0)///INDICATORvalue//.//COUNTRYvalue//’, the dimensions INDICATOR and COUNTRY would be dropped so that the data structure of all the resulting VTL Data Sets would have the identifier TIME_PERIOD only.
428	428
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		-
433	433	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:
434	434
435		-‘DF1(1.0.0)/POPULATION.USA’ :=
	417	+[[image:1747388275998-621.png]]
436	436
437		-DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA” ];
438		-
439		-‘DF1(1.0.0)/POPULATION.CANADA’ :=
440		-
441		-DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
442		-
443		-… … …
444		-
445	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.{{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}}
446	446
447	447	In the direction from SDMX to VTL it is allowed to omit the value of one or more
...	...	@@ -452,10 +452,8 @@
452	452
453	453	This is equivalent to the application of the VTL “sub” operator only to the identifier //INDICATOR//:
454	454
455		-‘DF1(1.0.0)/POPULATION.’ :=
	429	+[[image:1747388244829-693.png]]
456	456
457		-DF1(1.0.0) [ sub INDICATOR=“POPULATION” ];
458		-
459	459	Therefore the VTL Data Set ‘DF1(1.0.0)/POPULATION.’ would have the identifiers COUNTRY and TIME_PERIOD.
460	460
461	461	Heterogeneous invocations of the same Dataflow are allowed, i.e. omitting different
...	...	@@ -481,54 +481,18 @@
481	481
482	482	Some examples follow, for some specific values of INDICATOR and COUNTRY:
483	483
484		-‘DF2(1.0.0)/GDPPERCAPITA.USA’ <- expression11; ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ <- expression12;
	456	+[[image:1747388222879-916.png]]
485	485
486		-… … …
	458	+[[image:1747388206717-256.png]]
487	487
488		-‘DF2(1.0.0)/POPGROWTH.USA’ <- expression21;
489		-
490		-‘DF2(1.0.0)/POPGROWTH.CANADA’ <- expression22;
491		-
492		-… … …
493		-
494	494	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:
495	495
496		-VTL dataset INDICATOR value COUNTRY value
	462	+[[image:1747388148322-387.png]]
497	497
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		-
508	508	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:
509	509
510		-DF2bis_GDPPERCAPITA_USA := ‘DF2(1.0.0)/GDPPERCAPITA.USA’ [calc identifier INDICATOR := ”GDPPERCAPITA”, identifier COUNTRY := ”USA”];
	466	+[[image:1747388179021-814.png]]
511	511
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		-
532	532	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
533	533
534	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){{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.
...	...	@@ -542,37 +542,30 @@
542	542	With reference to the VTL “model for Variables and Value domains”, the following additional mappings have to be considered:
543	543
544	544	(% style="width:1170.29px" %)
545		-|**VTL**|(% style="width:754px" %)**SDMX**
546		-|**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}}
547		-|**Represented Variable**|(% style="width:754px" %)(((
	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" %)(((
548	548	**Concept** with a definite
549	549
550	550	Representation
551	551	)))
552		-|**Value Domain**|(% style="width:754px" %)(((
553		-**Representation** (see the Structure
554		-
555		-Pattern in the Base Package)
	488	+|(% style="width:392px" %)**Value Domain**|(% style="width:776px" %)(((
	489	+**Representation** (see the Structure Pattern in the Base Package)
556	556	)))
557		-|**Enumerated Value Domain / Code List**|(% style="width:754px" %)**Codelist**
558		-|**Code**|(% style="width:754px" %)(((
	491	+|(% style="width:392px" %)**Enumerated Value Domain / Code List**|(% style="width:776px" %)**Codelist**
	492	+|(% style="width:392px" %)**Code**|(% style="width:776px" %)(((
559	559	**Code** (for enumerated
560	560
561	561	DimensionComponent, Measure, DataAttribute)
562	562	)))
563		-|**Described Value Domain**|(% style="width:754px" %)(((
564		-non-enumerated** Representation**
565		-
566		-(having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
	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)
567	567	)))
568		-|**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
569		-| |(% style="width:754px" %)(((
570		-to a valid **value **(for non-enumerated** **Representations)
571		-)))
572		-|**Value Domain Subset / Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
573		-|**Enumerated Value Domain Subset / Enumerated Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
574		-|**Described Value Domain Subset / Described Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
575		-|**Set list**|(% style="width:754px" %)This abstraction does not exist in SDMX
	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
576	576
577	577	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).
578	578
...	...	@@ -580,8 +580,10 @@
580	580
581	581	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
582	582
583		-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.
	512	+DS_c := DS_a + DS_b (where DS_a, DS_b, DS_c are VTL Data Sets)
584	584
	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	+
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
587	587	Transformations to ensure that the VTL expressions are consistent with the actual representations of the correspondent SDMX Concepts.
...	...	@@ -598,7 +598,8 @@
598	598
599	599	[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_e3df33ae.png||height="543" width="483"]]
600	600
601		-==== Figure 22 – VTL Data Types ====
	532	+(% class="wikigeneratedid" id="HFigure222013VTLDataTypes" %)
	533	+**Figure 22 – VTL Data Types**
602	602
603	603	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.
604	604
...	...	@@ -605,131 +605,12 @@
605	605	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):
606	606
607	607
	540	+**Figure 23 – VTL Basic Scalar Types**
608	608
609	609	(((
610		-//n//
611		-
612		-//a//
613		-
614		-//e//
615		-
616		-//l//
617		-
618		-//o//
619		-
620		-//o//
621		-
622		-//B//
623		-
624		-//n//
625		-
626		-//o//
627		-
628		-//i//
629		-
630		-//t//
631		-
632		-//a//
633		-
634		-//r//
635		-
636		-//u//
637		-
638		-//D//
639		-
640		-//d//
641		-
642		-//o//
643		-
644		-//i//
645		-
646		-//r//
647		-
648		-//e//
649		-
650		-//p//
651		-
652		-//_//
653		-
654		-//e//
655		-
656		-//m//
657		-
658		-//i//
659		-
660		-//T//
661		-
662		-//e//
663		-
664		-//t//
665		-
666		-//a//
667		-
668		-//D//
669		-
670		-//e//
671		-
672		-//m//
673		-
674		-//i//
675		-
676		-//T//
677		-
678		-//r//
679		-
680		-//e//
681		-
682		-//g//
683		-
684		-//e//
685		-
686		-//t//
687		-
688		-//n//
689		-
690		-//I//
691		-
692		-//r//
693		-
694		-//e//
695		-
696		-//b//
697		-
698		-//m//
699		-
700		-//u//
701		-
702		-//N//
703		-
704		-//g//
705		-
706		-//n//
707		-
708		-//i//
709		-
710		-//r//
711		-
712		-//t//
713		-
714		-//S//
715		-
716		-//r//
717		-
718		-//a//
719		-
720		-//l//
721		-
722		-//a//
723		-
724		-//c//
725		-
726		-//S//
727		-
728		-[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_82d45833.gif||alt="Shape6" height="231" width="184"]]
	543	+
729	729	)))
730	730
731		-==== Figure 23 – VTL Basic Scalar Types ====
732		-
733	733	=== 12.4.2 VTL basic scalar types and SDMX data types ===
734	734
735	735	The VTL assumes that a basic scalar type has a unique internal representation and can have more external representations.
...	...	@@ -752,204 +752,159 @@
752	752
753	753	The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types.
754	754
755		-|SDMX data type (BasicComponentDataType)|Default VTL basic scalar type
756		-|(((
	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" %)(((
757	757	String
758		-
759	759	(string allowing any character)
760		-)))|string
761		-|(((
	573	+)))|(% style="width:312px" %)string
	574	+|(% style="width:509px" %)(((
762	762	Alpha
763		-
764	764	(string which only allows A-z)
765		-)))|string
766		-|(((
	577	+)))|(% style="width:312px" %)string
	578	+|(% style="width:509px" %)(((
767	767	AlphaNumeric
768		-
769	769	(string which only allows A-z and 0-9)
770		-)))|string
771		-|(((
	581	+)))|(% style="width:312px" %)string
	582	+|(% style="width:509px" %)(((
772	772	Numeric
773		-
774	774	(string which only allows 0-9, but is not numeric so that is can having leading zeros)
775		-)))|string
776		-|(((
	585	+)))|(% style="width:312px" %)string
	586	+|(% style="width:509px" %)(((
777	777	BigInteger
778		-
779	779	(corresponds to XML Schema xs:integer datatype; infinite set of integer values)
780		-)))|integer
781		-|(((
	589	+)))|(% style="width:312px" %)integer
	590	+|(% style="width:509px" %)(((
782	782	Integer
783		-
784		-(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647
785		-
786		-(inclusive))
787		-)))|integer
788		-|(((
	592	+(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647 (inclusive))
	593	+)))|(% style="width:312px" %)integer
	594	+|(% style="width:509px" %)(((
789	789	Long
790		-
791		-(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and
792		-
793		-+9223372036854775807 (inclusive))
794		-)))|integer
795		-|(((
	596	+(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and +9223372036854775807 (inclusive))
	597	+)))|(% style="width:312px" %)integer
	598	+|(% style="width:509px" %)(((
796	796	Short
797		-
798	798	(corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive))
799		-)))|integer
800		-|Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|number
801		-|(((
	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	802	Float
803		-
804	804	(corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type)
805		-)))|number
806		-|(((
	606	+)))|(% style="width:312px" %)number
	607	+|(% style="width:509px" %)(((
807	807	Double
808		-
809	809	(corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type)
810		-)))|number
811		-|(((
	610	+)))|(% style="width:312px" %)number
	611	+|(% style="width:509px" %)(((
812	812	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
813	813
814		-(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of
815		-
816		-binary-valued logic: {true, false})
817		-)))|boolean
818		-
819		-| |(% colspan="2" %)(((
	616	+(% style="width:822.294px" %)
	617	+|(% colspan="2" style="width:507px" %)(((
820	820	URI
821		-
822	822	(corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference)
823		-)))|(% colspan="2" %)string
824		-| |(% colspan="2" %)(((
	620	+)))|(% colspan="1" style="width:311px" %)string
	621	+|(% colspan="2" style="width:507px" %)(((
825	825	Count
826		-
827	827	(an integer following a sequential pattern, increasing by 1 for each occurrence)
828		-)))|(% colspan="2" %)integer
829		-| |(% colspan="2" %)(((
	624	+)))|(% colspan="1" style="width:311px" %)integer
	625	+|(% colspan="2" style="width:507px" %)(((
830	830	InclusiveValueRange
831		-
832	832	(decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
833		-)))|(% colspan="2" %)number
834		-| |(% colspan="2" %)(((
	628	+)))|(% colspan="1" style="width:311px" %)number
	629	+|(% colspan="2" style="width:507px" %)(((
835	835	ExclusiveValueRange
836		-
837	837	(decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
838		-)))|(% colspan="2" %)number
839		-| |(% colspan="2" %)(((
	632	+)))|(% colspan="1" style="width:311px" %)number
	633	+|(% colspan="2" style="width:507px" %)(((
840	840	Incremental
841		-
842	842	(decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation)
843		-)))|(% colspan="2" %)number
844		-| |(% colspan="2" %)(((
	636	+)))|(% colspan="1" style="width:311px" %)number
	637	+|(% colspan="2" style="width:507px" %)(((
845	845	ObservationalTimePeriod
846		-
847	847	(superset of StandardTimePeriod and TimeRange)
848		-)))|(% colspan="2" %)time
849		-| |(% colspan="2" %)(((
	640	+)))|(% colspan="1" style="width:311px" %)time
	641	+|(% colspan="2" style="width:507px" %)(((
850	850	StandardTimePeriod
851		-
852		-(superset of BasicTimePeriod and
853		-
854		-ReportingTimePeriod)
855		-)))|(% colspan="2" %)time
856		-| |(% colspan="2" %)(((
	643	+(superset of BasicTimePeriod and ReportingTimePeriod)
	644	+)))|(% colspan="1" style="width:311px" %)time
	645	+|(% colspan="2" style="width:507px" %)(((
857	857	BasicTimePeriod
858		-
859	859	(superset of GregorianTimePeriod and DateTime)
860		-)))|(% colspan="2" %)date
861		-| |(% colspan="2" %)(((
	648	+)))|(% colspan="1" style="width:311px" %)date
	649	+|(% colspan="2" style="width:507px" %)(((
862	862	GregorianTimePeriod
863		-
864	864	(superset of GregorianYear, GregorianYearMonth, and GregorianDay)
865		-)))|(% colspan="2" %)date
866		-| |(% colspan="2" %)GregorianYear (YYYY)|(% colspan="2" %)date
867		-| |(% colspan="2" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="2" %)date
868		-| |(% colspan="2" %)GregorianDay (YYYY-MM-DD)|(% colspan="2" %)date
869		-| |(% colspan="2" %)(((
	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" %)(((
870	870	ReportingTimePeriod
871		-
872		-(superset of RepostingYear, ReportingSemester,
873		-
874		-ReportingTrimester, ReportingQuarter,
875		-
876		-ReportingMonth, ReportingWeek, ReportingDay)
877		-)))|(% colspan="2" %)time_period
878		-| |(% colspan="2" %)(((
	658	+(superset of RepostingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, ReportingDay)
	659	+)))|(% colspan="1" style="width:311px" %)time_period
	660	+|(% colspan="2" style="width:507px" %)(((
879	879	ReportingYear
880		-
881	881	(YYYY-A1 – 1 year period)
882		-)))|(% colspan="2" %)time_period
883		-| |(% colspan="2" %)(((
	663	+)))|(% colspan="1" style="width:311px" %)time_period
	664	+|(% colspan="2" style="width:507px" %)(((
884	884	ReportingSemester
885		-
886	886	(YYYY-Ss – 6 month period)
887		-)))|(% colspan="2" %)time_period
888		-| |(% colspan="2" %)(((
	667	+)))|(% colspan="1" style="width:311px" %)time_period
	668	+|(% colspan="2" style="width:507px" %)(((
889	889	ReportingTrimester
890		-
891	891	(YYYY-Tt – 4 month period)
892		-)))|(% colspan="2" %)time_period
893		-| |(% colspan="2" %)(((
	671	+)))|(% colspan="1" style="width:311px" %)time_period
	672	+|(% colspan="2" style="width:507px" %)(((
894	894	ReportingQuarter
895		-
896	896	(YYYY-Qq – 3 month period)
897		-)))|(% colspan="2" %)time_period
898		-| |(% colspan="2" %)(((
	675	+)))|(% colspan="1" style="width:311px" %)time_period
	676	+|(% colspan="2" style="width:507px" %)(((
899	899	ReportingMonth
900		-
901	901	(YYYY-Mmm – 1 month period)
902		-)))|(% colspan="2" %)time_period
903		-| |(% colspan="2" %)ReportingWeek|(% colspan="2" %)time_period
904		-| |(% colspan="2" %) |(% colspan="2" %)
905		-| |(% colspan="2" %) |(% colspan="2" %)
906		-|(% colspan="2" %)(YYYY-Www – 7 day period; following ISO 8601 definition of a week in a year)|(% colspan="2" %) |
907		-|(% colspan="2" %)(((
	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" %)(((
908	908	ReportingDay
909		-
910	910	(YYYY-Dddd – 1 day period)
911		-)))|(% colspan="2" %)time_period|
912		-|(% colspan="2" %)(((
	685	+)))|(% colspan="2" style="width:312px" %)time_period
	686	+|(% colspan="1" style="width:507px" %)(((
913	913	DateTime
914		-
915	915	(YYYY-MM-DDThh:mm:ss)
916		-)))|(% colspan="2" %)date|
917		-|(% colspan="2" %)(((
	689	+)))|(% colspan="2" style="width:312px" %)date
	690	+|(% colspan="1" style="width:507px" %)(((
918	918	TimeRange
919		-
920	920	(YYYY-MM-DD(Thh:mm:ss)?/<duration>)
921		-)))|(% colspan="2" %)time|
922		-|(% colspan="2" %)(((
	693	+)))|(% colspan="2" style="width:312px" %)time
	694	+|(% colspan="1" style="width:507px" %)(((
923	923	Month
924		-
925	925	(~-~-MM; speicifies a month independent of a year; e.g. February is black history month in the United States)
926		-)))|(% colspan="2" %)string|
927		-|(% colspan="2" %)(((
	697	+)))|(% colspan="2" style="width:312px" %)string
	698	+|(% colspan="1" style="width:507px" %)(((
928	928	MonthDay
929		-
930	930	(~-~-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)
931		-)))|(% colspan="2" %)string|
932		-|(% colspan="2" %)(((
	701	+)))|(% colspan="2" style="width:312px" %)string
	702	+|(% colspan="1" style="width:507px" %)(((
933	933	Day
934		-
935	935	(~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday)
936		-)))|(% colspan="2" %)string|
937		-|(% colspan="2" %)(((
	705	+)))|(% colspan="2" style="width:312px" %)string
	706	+|(% colspan="1" style="width:507px" %)(((
938	938	Time
939		-
940	940	(hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM)
941		-)))|(% colspan="2" %)string|
942		-|(% colspan="2" %)(((
	709	+)))|(% colspan="2" style="width:312px" %)string
	710	+|(% colspan="1" style="width:507px" %)(((
943	943	Duration
944		-
945	945	(corresponds to XML Schema xs:duration datatype)
946		-)))|(% colspan="2" %)duration|
947		-|(% colspan="2" %)XHTML|(% colspan="2" %)Metadata type – not applicable|
948		-|(% colspan="2" %)KeyValues|(% colspan="2" %)Metadata type – not applicable|
949		-|(% colspan="2" %)IdentifiableReference|(% colspan="2" %)Metadata type – not applicable|
950		-|(% colspan="2" %)DataSetReference|(% colspan="2" %)Metadata type – not applicable|
	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
951	951
952		-==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
	719	+(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes" %)
	720	+**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
953	953
954	954	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).
955	955
...	...	@@ -957,39 +957,32 @@
957	957
958	958	The following table describes the default conversion from the VTL basic scalar types to the SDMX data types .
959	959
960		-|(((
961		-VTL basic
962		-
963		-scalar type
964		-)))|(((
965		-Default SDMX data type
966		-
967		-(BasicComponentDataType
968		-
969		-)
970		-)))|Default output format
971		-|String|String|Like XML (xs:string)
972		-|Number|Float|Like XML (xs:float)
973		-|Integer|Integer|Like XML (xs:int)
974		-|Date|DateTime|YYYY-MM-DDT00:00:00Z
975		-|Time|StandardTimePeriod|<date>/<date> (as defined above)
976		-|time_period|(((
	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" %)(((
977	977	ReportingTimePeriod
978		-
979	979	(StandardReportingPeriod)
980		-)))|(((
	742	+)))|(% style="width:402px" %)(((
981	981	YYYY-Pppp
982		-
983	983	(according to SDMX )
984	984	)))
985		-|Duration|Duration|(((
	746	+|(% style="width:207px" %)Duration|(% style="width:462px" %)Duration|(% style="width:402px" %)(((
986	986	Like XML (xs:duration)
987		-
988	988	PnYnMnDTnHnMnS
989	989	)))
990		-|Boolean|Boolean|Like XML (xs:boolean) with the values "true" or "false"
	750	+|(% style="width:207px" %)Boolean|(% style="width:462px" %)Boolean|(% style="width:402px" %)Like XML (xs:boolean) with the values "true" or "false"
991	991
992		-==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
	752	+(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes-1" %)
	753	+**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
993	993
994	994	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).
995	995
...	...	@@ -1043,7 +1043,7 @@
1043	1043	|N|fixed number of digits used in the preceding textual representation of the month or the day
1044	1044	| |
1045	1045
1046		-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 wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^42^^>>path:#sdfootnote42sym||name="sdfootnote42anc"]](%%)^^.
	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}}.
1047	1047
1048	1048	=== 12.4.5 Null Values ===
1049	1049
...	...	@@ -1061,10 +1061,8 @@
1061	1061
1062	1062	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).
1063	1063
1064		-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
	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.
1065	1065
1066		-TransformationScheme.
1067		-
1068	1068	In case a literal is operand of a VTL Cast operation, the format specified in the Cast overrides all the possible otherwise specified formats.
1069	1069
1070	1070	{{putFootnotes/}}

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