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
...	...	@@ -422,24 +422,17 @@
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	435	‘DF1(1.0.0)/POPULATION.USA’ :=
436		-
437	437	DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA” ];
438	438
439	439	‘DF1(1.0.0)/POPULATION.CANADA’ :=
440		-
441	441	DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
442		-
443	443	… … …
444	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}}
...	...	@@ -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.’ :=
	434	+[[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;
	461	+[[image:1747388222879-916.png]]
485	485
486		-… … …
	463	+[[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
	467	+[[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”];
	471	+[[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.
...	...	@@ -600,7 +600,8 @@
600	600
601	601	[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_e3df33ae.png||height="543" width="483"]]
602	602
603		-==== Figure 22 – VTL Data Types ====
	544	+(% class="wikigeneratedid" id="HFigure222013VTLDataTypes" %)
	545	+**Figure 22 – VTL Data Types**
604	604
605	605	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.
606	606
...	...	@@ -607,131 +607,12 @@
607	607	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):
608	608
609	609
	552	+**Figure 23 – VTL Basic Scalar Types**
610	610
611	611	(((
612		-//n//
613		-
614		-//a//
615		-
616		-//e//
617		-
618		-//l//
619		-
620		-//o//
621		-
622		-//o//
623		-
624		-//B//
625		-
626		-//n//
627		-
628		-//o//
629		-
630		-//i//
631		-
632		-//t//
633		-
634		-//a//
635		-
636		-//r//
637		-
638		-//u//
639		-
640		-//D//
641		-
642		-//d//
643		-
644		-//o//
645		-
646		-//i//
647		-
648		-//r//
649		-
650		-//e//
651		-
652		-//p//
653		-
654		-//_//
655		-
656		-//e//
657		-
658		-//m//
659		-
660		-//i//
661		-
662		-//T//
663		-
664		-//e//
665		-
666		-//t//
667		-
668		-//a//
669		-
670		-//D//
671		-
672		-//e//
673		-
674		-//m//
675		-
676		-//i//
677		-
678		-//T//
679		-
680		-//r//
681		-
682		-//e//
683		-
684		-//g//
685		-
686		-//e//
687		-
688		-//t//
689		-
690		-//n//
691		-
692		-//I//
693		-
694		-//r//
695		-
696		-//e//
697		-
698		-//b//
699		-
700		-//m//
701		-
702		-//u//
703		-
704		-//N//
705		-
706		-//g//
707		-
708		-//n//
709		-
710		-//i//
711		-
712		-//r//
713		-
714		-//t//
715		-
716		-//S//
717		-
718		-//r//
719		-
720		-//a//
721		-
722		-//l//
723		-
724		-//a//
725		-
726		-//c//
727		-
728		-//S//
729		-
730		-[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_82d45833.gif||alt="Shape6" height="231" width="184"]]
	555	+
731	731	)))
732	732
733		-==== Figure 23 – VTL Basic Scalar Types ====
734		-
735	735	=== 12.4.2 VTL basic scalar types and SDMX data types ===
736	736
737	737	The VTL assumes that a basic scalar type has a unique internal representation and can have more external representations.
...	...	@@ -754,204 +754,159 @@
754	754
755	755	The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types.
756	756
757		-|SDMX data type (BasicComponentDataType)|Default VTL basic scalar type
758		-|(((
	580	+(% style="width:823.294px" %)
	581	+|(% style="width:509px" %)**SDMX data type (BasicComponentDataType)**|(% style="width:312px" %)**Default VTL basic scalar type**
	582	+|(% style="width:509px" %)(((
759	759	String
760		-
761	761	(string allowing any character)
762		-)))|string
763		-|(((
	585	+)))|(% style="width:312px" %)string
	586	+|(% style="width:509px" %)(((
764	764	Alpha
765		-
766	766	(string which only allows A-z)
767		-)))|string
768		-|(((
	589	+)))|(% style="width:312px" %)string
	590	+|(% style="width:509px" %)(((
769	769	AlphaNumeric
770		-
771	771	(string which only allows A-z and 0-9)
772		-)))|string
773		-|(((
	593	+)))|(% style="width:312px" %)string
	594	+|(% style="width:509px" %)(((
774	774	Numeric
775		-
776	776	(string which only allows 0-9, but is not numeric so that is can having leading zeros)
777		-)))|string
778		-|(((
	597	+)))|(% style="width:312px" %)string
	598	+|(% style="width:509px" %)(((
779	779	BigInteger
780		-
781	781	(corresponds to XML Schema xs:integer datatype; infinite set of integer values)
782		-)))|integer
783		-|(((
	601	+)))|(% style="width:312px" %)integer
	602	+|(% style="width:509px" %)(((
784	784	Integer
785		-
786		-(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647
787		-
788		-(inclusive))
789		-)))|integer
790		-|(((
	604	+(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647 (inclusive))
	605	+)))|(% style="width:312px" %)integer
	606	+|(% style="width:509px" %)(((
791	791	Long
792		-
793		-(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and
794		-
795		-+9223372036854775807 (inclusive))
796		-)))|integer
797		-|(((
	608	+(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and +9223372036854775807 (inclusive))
	609	+)))|(% style="width:312px" %)integer
	610	+|(% style="width:509px" %)(((
798	798	Short
799		-
800	800	(corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive))
801		-)))|integer
802		-|Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|number
803		-|(((
	613	+)))|(% style="width:312px" %)integer
	614	+|(% 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
	615	+|(% style="width:509px" %)(((
804	804	Float
805		-
806	806	(corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type)
807		-)))|number
808		-|(((
	618	+)))|(% style="width:312px" %)number
	619	+|(% style="width:509px" %)(((
809	809	Double
810		-
811	811	(corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type)
812		-)))|number
813		-|(((
	622	+)))|(% style="width:312px" %)number
	623	+|(% style="width:509px" %)(((
814	814	Boolean
	625	+(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of binary-valued logic: {true, false})
	626	+)))|(% style="width:312px" %)boolean
815	815
816		-(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of
817		-
818		-binary-valued logic: {true, false})
819		-)))|boolean
820		-
821		-| |(% colspan="2" %)(((
	628	+(% style="width:822.294px" %)
	629	+|(% colspan="2" style="width:507px" %)(((
822	822	URI
823		-
824	824	(corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference)
825		-)))|(% colspan="2" %)string
826		-| |(% colspan="2" %)(((
	632	+)))|(% colspan="1" style="width:311px" %)string
	633	+|(% colspan="2" style="width:507px" %)(((
827	827	Count
828		-
829	829	(an integer following a sequential pattern, increasing by 1 for each occurrence)
830		-)))|(% colspan="2" %)integer
831		-| |(% colspan="2" %)(((
	636	+)))|(% colspan="1" style="width:311px" %)integer
	637	+|(% colspan="2" style="width:507px" %)(((
832	832	InclusiveValueRange
833		-
834	834	(decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
835		-)))|(% colspan="2" %)number
836		-| |(% colspan="2" %)(((
	640	+)))|(% colspan="1" style="width:311px" %)number
	641	+|(% colspan="2" style="width:507px" %)(((
837	837	ExclusiveValueRange
838		-
839	839	(decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
840		-)))|(% colspan="2" %)number
841		-| |(% colspan="2" %)(((
	644	+)))|(% colspan="1" style="width:311px" %)number
	645	+|(% colspan="2" style="width:507px" %)(((
842	842	Incremental
843		-
844	844	(decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation)
845		-)))|(% colspan="2" %)number
846		-| |(% colspan="2" %)(((
	648	+)))|(% colspan="1" style="width:311px" %)number
	649	+|(% colspan="2" style="width:507px" %)(((
847	847	ObservationalTimePeriod
848		-
849	849	(superset of StandardTimePeriod and TimeRange)
850		-)))|(% colspan="2" %)time
851		-| |(% colspan="2" %)(((
	652	+)))|(% colspan="1" style="width:311px" %)time
	653	+|(% colspan="2" style="width:507px" %)(((
852	852	StandardTimePeriod
853		-
854		-(superset of BasicTimePeriod and
855		-
856		-ReportingTimePeriod)
857		-)))|(% colspan="2" %)time
858		-| |(% colspan="2" %)(((
	655	+(superset of BasicTimePeriod and ReportingTimePeriod)
	656	+)))|(% colspan="1" style="width:311px" %)time
	657	+|(% colspan="2" style="width:507px" %)(((
859	859	BasicTimePeriod
860		-
861	861	(superset of GregorianTimePeriod and DateTime)
862		-)))|(% colspan="2" %)date
863		-| |(% colspan="2" %)(((
	660	+)))|(% colspan="1" style="width:311px" %)date
	661	+|(% colspan="2" style="width:507px" %)(((
864	864	GregorianTimePeriod
865		-
866	866	(superset of GregorianYear, GregorianYearMonth, and GregorianDay)
867		-)))|(% colspan="2" %)date
868		-| |(% colspan="2" %)GregorianYear (YYYY)|(% colspan="2" %)date
869		-| |(% colspan="2" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="2" %)date
870		-| |(% colspan="2" %)GregorianDay (YYYY-MM-DD)|(% colspan="2" %)date
871		-| |(% colspan="2" %)(((
	664	+)))|(% colspan="1" style="width:311px" %)date
	665	+|(% colspan="2" style="width:507px" %)GregorianYear (YYYY)|(% colspan="1" style="width:311px" %)date
	666	+|(% colspan="2" style="width:507px" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="1" style="width:311px" %)date
	667	+|(% colspan="2" style="width:507px" %)GregorianDay (YYYY-MM-DD)|(% colspan="1" style="width:311px" %)date
	668	+|(% colspan="2" style="width:507px" %)(((
872	872	ReportingTimePeriod
873		-
874		-(superset of RepostingYear, ReportingSemester,
875		-
876		-ReportingTrimester, ReportingQuarter,
877		-
878		-ReportingMonth, ReportingWeek, ReportingDay)
879		-)))|(% colspan="2" %)time_period
880		-| |(% colspan="2" %)(((
	670	+(superset of RepostingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, ReportingDay)
	671	+)))|(% colspan="1" style="width:311px" %)time_period
	672	+|(% colspan="2" style="width:507px" %)(((
881	881	ReportingYear
882		-
883	883	(YYYY-A1 – 1 year period)
884		-)))|(% colspan="2" %)time_period
885		-| |(% colspan="2" %)(((
	675	+)))|(% colspan="1" style="width:311px" %)time_period
	676	+|(% colspan="2" style="width:507px" %)(((
886	886	ReportingSemester
887		-
888	888	(YYYY-Ss – 6 month period)
889		-)))|(% colspan="2" %)time_period
890		-| |(% colspan="2" %)(((
	679	+)))|(% colspan="1" style="width:311px" %)time_period
	680	+|(% colspan="2" style="width:507px" %)(((
891	891	ReportingTrimester
892		-
893	893	(YYYY-Tt – 4 month period)
894		-)))|(% colspan="2" %)time_period
895		-| |(% colspan="2" %)(((
	683	+)))|(% colspan="1" style="width:311px" %)time_period
	684	+|(% colspan="2" style="width:507px" %)(((
896	896	ReportingQuarter
897		-
898	898	(YYYY-Qq – 3 month period)
899		-)))|(% colspan="2" %)time_period
900		-| |(% colspan="2" %)(((
	687	+)))|(% colspan="1" style="width:311px" %)time_period
	688	+|(% colspan="2" style="width:507px" %)(((
901	901	ReportingMonth
902		-
903	903	(YYYY-Mmm – 1 month period)
904		-)))|(% colspan="2" %)time_period
905		-| |(% colspan="2" %)ReportingWeek|(% colspan="2" %)time_period
906		-| |(% colspan="2" %) |(% colspan="2" %)
907		-| |(% colspan="2" %) |(% colspan="2" %)
908		-|(% colspan="2" %)(YYYY-Www – 7 day period; following ISO 8601 definition of a week in a year)|(% colspan="2" %) |
909		-|(% colspan="2" %)(((
	691	+)))|(% colspan="1" style="width:311px" %)time_period
	692	+|(% colspan="2" style="width:507px" %)ReportingWeek|(% colspan="1" style="width:311px" %)time_period
	693	+|(% 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" %)
	694	+|(% colspan="1" style="width:507px" %)(((
910	910	ReportingDay
911		-
912	912	(YYYY-Dddd – 1 day period)
913		-)))|(% colspan="2" %)time_period|
914		-|(% colspan="2" %)(((
	697	+)))|(% colspan="2" style="width:312px" %)time_period
	698	+|(% colspan="1" style="width:507px" %)(((
915	915	DateTime
916		-
917	917	(YYYY-MM-DDThh:mm:ss)
918		-)))|(% colspan="2" %)date|
919		-|(% colspan="2" %)(((
	701	+)))|(% colspan="2" style="width:312px" %)date
	702	+|(% colspan="1" style="width:507px" %)(((
920	920	TimeRange
921		-
922	922	(YYYY-MM-DD(Thh:mm:ss)?/<duration>)
923		-)))|(% colspan="2" %)time|
924		-|(% colspan="2" %)(((
	705	+)))|(% colspan="2" style="width:312px" %)time
	706	+|(% colspan="1" style="width:507px" %)(((
925	925	Month
926		-
927	927	(~-~-MM; speicifies a month independent of a year; e.g. February is black history month in the United States)
928		-)))|(% colspan="2" %)string|
929		-|(% colspan="2" %)(((
	709	+)))|(% colspan="2" style="width:312px" %)string
	710	+|(% colspan="1" style="width:507px" %)(((
930	930	MonthDay
931		-
932	932	(~-~-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)
933		-)))|(% colspan="2" %)string|
934		-|(% colspan="2" %)(((
	713	+)))|(% colspan="2" style="width:312px" %)string
	714	+|(% colspan="1" style="width:507px" %)(((
935	935	Day
936		-
937	937	(~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday)
938		-)))|(% colspan="2" %)string|
939		-|(% colspan="2" %)(((
	717	+)))|(% colspan="2" style="width:312px" %)string
	718	+|(% colspan="1" style="width:507px" %)(((
940	940	Time
941		-
942	942	(hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM)
943		-)))|(% colspan="2" %)string|
944		-|(% colspan="2" %)(((
	721	+)))|(% colspan="2" style="width:312px" %)string
	722	+|(% colspan="1" style="width:507px" %)(((
945	945	Duration
946		-
947	947	(corresponds to XML Schema xs:duration datatype)
948		-)))|(% colspan="2" %)duration|
949		-|(% colspan="2" %)XHTML|(% colspan="2" %)Metadata type – not applicable|
950		-|(% colspan="2" %)KeyValues|(% colspan="2" %)Metadata type – not applicable|
951		-|(% colspan="2" %)IdentifiableReference|(% colspan="2" %)Metadata type – not applicable|
952		-|(% colspan="2" %)DataSetReference|(% colspan="2" %)Metadata type – not applicable|
	725	+)))|(% colspan="2" style="width:312px" %)duration
	726	+|(% colspan="1" style="width:507px" %)XHTML|(% colspan="2" style="width:312px" %)Metadata type – not applicable
	727	+|(% colspan="1" style="width:507px" %)KeyValues|(% colspan="2" style="width:312px" %)Metadata type – not applicable
	728	+|(% colspan="1" style="width:507px" %)IdentifiableReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
	729	+|(% colspan="1" style="width:507px" %)DataSetReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
953	953
954		-==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
	731	+(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes" %)
	732	+**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
955	955
956	956	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).
957	957
...	...	@@ -959,39 +959,32 @@
959	959
960	960	The following table describes the default conversion from the VTL basic scalar types to the SDMX data types .
961	961
962		-|(((
963		-VTL basic
964		-
965		-scalar type
966		-)))|(((
967		-Default SDMX data type
968		-
969		-(BasicComponentDataType
970		-
971		-)
972		-)))|Default output format
973		-|String|String|Like XML (xs:string)
974		-|Number|Float|Like XML (xs:float)
975		-|Integer|Integer|Like XML (xs:int)
976		-|Date|DateTime|YYYY-MM-DDT00:00:00Z
977		-|Time|StandardTimePeriod|<date>/<date> (as defined above)
978		-|time_period|(((
	740	+(% style="width:1073.29px" %)
	741	+|(% style="width:207px" %)(((
	742	+**VTL basic scalar type**
	743	+)))|(% style="width:462px" %)(((
	744	+**Default SDMX data type (BasicComponentDataType)**
	745	+)))|(% style="width:402px" %)**Default output format**
	746	+|(% style="width:207px" %)String|(% style="width:462px" %)String|(% style="width:402px" %)Like XML (xs:string)
	747	+|(% style="width:207px" %)Number|(% style="width:462px" %)Float|(% style="width:402px" %)Like XML (xs:float)
	748	+|(% style="width:207px" %)Integer|(% style="width:462px" %)Integer|(% style="width:402px" %)Like XML (xs:int)
	749	+|(% style="width:207px" %)Date|(% style="width:462px" %)DateTime|(% style="width:402px" %)YYYY-MM-DDT00:00:00Z
	750	+|(% style="width:207px" %)Time|(% style="width:462px" %)StandardTimePeriod|(% style="width:402px" %)<date>/<date> (as defined above)
	751	+|(% style="width:207px" %)time_period|(% style="width:462px" %)(((
979	979	ReportingTimePeriod
980		-
981	981	(StandardReportingPeriod)
982		-)))|(((
	754	+)))|(% style="width:402px" %)(((
983	983	YYYY-Pppp
984		-
985	985	(according to SDMX )
986	986	)))
987		-|Duration|Duration|(((
	758	+|(% style="width:207px" %)Duration|(% style="width:462px" %)Duration|(% style="width:402px" %)(((
988	988	Like XML (xs:duration)
989		-
990	990	PnYnMnDTnHnMnS
991	991	)))
992		-|Boolean|Boolean|Like XML (xs:boolean) with the values "true" or "false"
	762	+|(% style="width:207px" %)Boolean|(% style="width:462px" %)Boolean|(% style="width:402px" %)Like XML (xs:boolean) with the values "true" or "false"
993	993
994		-==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
	764	+(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes-1" %)
	765	+**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
995	995
996	996	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).
997	997
...	...	@@ -1045,7 +1045,7 @@
1045	1045	|N|fixed number of digits used in the preceding textual representation of the month or the day
1046	1046	| |
1047	1047
1048		-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"]](%%)^^.
	819	+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	1049
1050	1050	=== 12.4.5 Null Values ===
1051	1051
...	...	@@ -1063,10 +1063,8 @@
1063	1063
1064	1064	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).
1065	1065
1066		-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
	837	+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	1067
1068		-TransformationScheme.
1069		-
1070	1070	In case a literal is operand of a VTL Cast operation, the format specified in the Cast overrides all the possible otherwise specified formats.
1071	1071
1072	1072	{{putFootnotes/}}

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