Changes for page 12 Validation and Transformation Language (VTL)

Summary

• Page properties (1 modified, 0 added, 0 removed)
• Attachments (0 modified, 0 added, 6 removed)
  • 1747388148322-387.png
  • 1747388179021-814.png
  • 1747388206717-256.png
  • 1747388222879-916.png
  • 1747388244829-693.png
  • 1747388275998-621.png

Details

Page properties

Content

...	...	@@ -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
...	...	@@ -408,14 +408,26 @@
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
	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	+
419	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}}
420	420
421	421	In the direction from SDMX to VTL it is allowed to omit the value of one or more
...	...	@@ -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
...	...	@@ -453,18 +453,54 @@
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	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.
...	...	@@ -572,159 +572,189 @@
572	572
573	573	The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types.
574	574
575		-(% style="width:823.294px" %)
576		-|(% style="width:509px" %)**SDMX data type (BasicComponentDataType)**|(% style="width:312px" %)**Default VTL basic scalar type**
577		-|(% style="width:509px" %)(((
	639	+|(% style="width:501px" %)SDMX data type (BasicComponentDataType)|(% style="width:1437px" %)Default VTL basic scalar type
	640	+|(% style="width:501px" %)(((
578	578	String
579	579	(string allowing any character)
580		-)))|(% style="width:312px" %)string
581		-|(% style="width:509px" %)(((
	643	+)))|(% style="width:1437px" %)string
	644	+|(% style="width:501px" %)(((
582	582	Alpha
583	583	(string which only allows A-z)
584		-)))|(% style="width:312px" %)string
585		-|(% style="width:509px" %)(((
	647	+)))|(% style="width:1437px" %)string
	648	+|(% style="width:501px" %)(((
586	586	AlphaNumeric
587	587	(string which only allows A-z and 0-9)
588		-)))|(% style="width:312px" %)string
589		-|(% style="width:509px" %)(((
	651	+)))|(% style="width:1437px" %)string
	652	+|(% style="width:501px" %)(((
590	590	Numeric
591	591	(string which only allows 0-9, but is not numeric so that is can having leading zeros)
592		-)))|(% style="width:312px" %)string
593		-|(% style="width:509px" %)(((
	655	+)))|(% style="width:1437px" %)string
	656	+|(% style="width:501px" %)(((
594	594	BigInteger
595	595	(corresponds to XML Schema xs:integer datatype; infinite set of integer values)
596		-)))|(% style="width:312px" %)integer
597		-|(% style="width:509px" %)(((
	659	+)))|(% style="width:1437px" %)integer
	660	+|(% style="width:501px" %)(((
598	598	Integer
599	599	(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647 (inclusive))
600		-)))|(% style="width:312px" %)integer
601		-|(% style="width:509px" %)(((
	663	+)))|(% style="width:1437px" %)integer
	664	+|(% style="width:501px" %)(((
602	602	Long
603		-(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and +9223372036854775807 (inclusive))
604		-)))|(% style="width:312px" %)integer
605		-|(% style="width:509px" %)(((
	666	+(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and
	667	+
	668	++9223372036854775807 (inclusive))
	669	+)))|(% style="width:1437px" %)integer
	670	+|(% style="width:501px" %)(((
606	606	Short
607	607	(corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive))
608		-)))|(% style="width:312px" %)integer
609		-|(% 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
610		-|(% style="width:509px" %)(((
	673	+)))|(% style="width:1437px" %)integer
	674	+|(% style="width:501px" %)Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|(% style="width:1437px" %)number
	675	+|(% style="width:501px" %)(((
611	611	Float
612	612	(corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type)
613		-)))|(% style="width:312px" %)number
614		-|(% style="width:509px" %)(((
	678	+)))|(% style="width:1437px" %)number
	679	+|(% style="width:501px" %)(((
615	615	Double
616	616	(corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type)
617		-)))|(% style="width:312px" %)number
618		-|(% style="width:509px" %)(((
	682	+)))|(% style="width:1437px" %)number
	683	+|(% style="width:501px" %)(((
619	619	Boolean
620	620	(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of binary-valued logic: {true, false})
621		-)))|(% style="width:312px" %)boolean
	686	+)))|(% style="width:1437px" %)boolean
622	622
623		-(% style="width:822.294px" %)
624		-|(% colspan="2" style="width:507px" %)(((
	688	+| |(% colspan="2" %)(((
625	625	URI
	690	+
626	626	(corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference)
627		-)))|(% colspan="1" style="width:311px" %)string
628		-|(% colspan="2" style="width:507px" %)(((
	692	+)))|(% colspan="2" %)string
	693	+| |(% colspan="2" %)(((
629	629	Count
	695	+
630	630	(an integer following a sequential pattern, increasing by 1 for each occurrence)
631		-)))|(% colspan="1" style="width:311px" %)integer
632		-|(% colspan="2" style="width:507px" %)(((
	697	+)))|(% colspan="2" %)integer
	698	+| |(% colspan="2" %)(((
633	633	InclusiveValueRange
	700	+
634	634	(decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
635		-)))|(% colspan="1" style="width:311px" %)number
636		-|(% colspan="2" style="width:507px" %)(((
	702	+)))|(% colspan="2" %)number
	703	+| |(% colspan="2" %)(((
637	637	ExclusiveValueRange
	705	+
638	638	(decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
639		-)))|(% colspan="1" style="width:311px" %)number
640		-|(% colspan="2" style="width:507px" %)(((
	707	+)))|(% colspan="2" %)number
	708	+| |(% colspan="2" %)(((
641	641	Incremental
	710	+
642	642	(decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation)
643		-)))|(% colspan="1" style="width:311px" %)number
644		-|(% colspan="2" style="width:507px" %)(((
	712	+)))|(% colspan="2" %)number
	713	+| |(% colspan="2" %)(((
645	645	ObservationalTimePeriod
	715	+
646	646	(superset of StandardTimePeriod and TimeRange)
647		-)))|(% colspan="1" style="width:311px" %)time
648		-|(% colspan="2" style="width:507px" %)(((
	717	+)))|(% colspan="2" %)time
	718	+| |(% colspan="2" %)(((
649	649	StandardTimePeriod
650		-(superset of BasicTimePeriod and ReportingTimePeriod)
651		-)))|(% colspan="1" style="width:311px" %)time
652		-|(% colspan="2" style="width:507px" %)(((
	720	+
	721	+(superset of BasicTimePeriod and
	722	+
	723	+ReportingTimePeriod)
	724	+)))|(% colspan="2" %)time
	725	+| |(% colspan="2" %)(((
653	653	BasicTimePeriod
	727	+
654	654	(superset of GregorianTimePeriod and DateTime)
655		-)))|(% colspan="1" style="width:311px" %)date
656		-|(% colspan="2" style="width:507px" %)(((
	729	+)))|(% colspan="2" %)date
	730	+| |(% colspan="2" %)(((
657	657	GregorianTimePeriod
	732	+
658	658	(superset of GregorianYear, GregorianYearMonth, and GregorianDay)
659		-)))|(% colspan="1" style="width:311px" %)date
660		-|(% colspan="2" style="width:507px" %)GregorianYear (YYYY)|(% colspan="1" style="width:311px" %)date
661		-|(% colspan="2" style="width:507px" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="1" style="width:311px" %)date
662		-|(% colspan="2" style="width:507px" %)GregorianDay (YYYY-MM-DD)|(% colspan="1" style="width:311px" %)date
663		-|(% colspan="2" style="width:507px" %)(((
	734	+)))|(% colspan="2" %)date
	735	+| |(% colspan="2" %)GregorianYear (YYYY)|(% colspan="2" %)date
	736	+| |(% colspan="2" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="2" %)date
	737	+| |(% colspan="2" %)GregorianDay (YYYY-MM-DD)|(% colspan="2" %)date
	738	+| |(% colspan="2" %)(((
664	664	ReportingTimePeriod
665		-(superset of RepostingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, ReportingDay)
666		-)))|(% colspan="1" style="width:311px" %)time_period
667		-|(% colspan="2" style="width:507px" %)(((
	740	+
	741	+(superset of RepostingYear, ReportingSemester,
	742	+
	743	+ReportingTrimester, ReportingQuarter,
	744	+
	745	+ReportingMonth, ReportingWeek, ReportingDay)
	746	+)))|(% colspan="2" %)time_period
	747	+| |(% colspan="2" %)(((
668	668	ReportingYear
	749	+
669	669	(YYYY-A1 – 1 year period)
670		-)))|(% colspan="1" style="width:311px" %)time_period
671		-|(% colspan="2" style="width:507px" %)(((
	751	+)))|(% colspan="2" %)time_period
	752	+| |(% colspan="2" %)(((
672	672	ReportingSemester
	754	+
673	673	(YYYY-Ss – 6 month period)
674		-)))|(% colspan="1" style="width:311px" %)time_period
675		-|(% colspan="2" style="width:507px" %)(((
	756	+)))|(% colspan="2" %)time_period
	757	+| |(% colspan="2" %)(((
676	676	ReportingTrimester
	759	+
677	677	(YYYY-Tt – 4 month period)
678		-)))|(% colspan="1" style="width:311px" %)time_period
679		-|(% colspan="2" style="width:507px" %)(((
	761	+)))|(% colspan="2" %)time_period
	762	+| |(% colspan="2" %)(((
680	680	ReportingQuarter
	764	+
681	681	(YYYY-Qq – 3 month period)
682		-)))|(% colspan="1" style="width:311px" %)time_period
683		-|(% colspan="2" style="width:507px" %)(((
	766	+)))|(% colspan="2" %)time_period
	767	+| |(% colspan="2" %)(((
684	684	ReportingMonth
	769	+
685	685	(YYYY-Mmm – 1 month period)
686		-)))|(% colspan="1" style="width:311px" %)time_period
687		-|(% colspan="2" style="width:507px" %)ReportingWeek|(% colspan="1" style="width:311px" %)time_period
688		-|(% 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" %)
689		-|(% colspan="1" style="width:507px" %)(((
	771	+)))|(% colspan="2" %)time_period
	772	+| |(% colspan="2" %)ReportingWeek|(% colspan="2" %)time_period
	773	+| |(% colspan="2" %) |(% colspan="2" %)
	774	+| |(% colspan="2" %) |(% colspan="2" %)
	775	+|(% colspan="2" %)(YYYY-Www – 7 day period; following ISO 8601 definition of a week in a year)|(% colspan="2" %) |
	776	+|(% colspan="2" %)(((
690	690	ReportingDay
	778	+
691	691	(YYYY-Dddd – 1 day period)
692		-)))|(% colspan="2" style="width:312px" %)time_period
693		-|(% colspan="1" style="width:507px" %)(((
	780	+)))|(% colspan="2" %)time_period|
	781	+|(% colspan="2" %)(((
694	694	DateTime
	783	+
695	695	(YYYY-MM-DDThh:mm:ss)
696		-)))|(% colspan="2" style="width:312px" %)date
697		-|(% colspan="1" style="width:507px" %)(((
	785	+)))|(% colspan="2" %)date|
	786	+|(% colspan="2" %)(((
698	698	TimeRange
	788	+
699	699	(YYYY-MM-DD(Thh:mm:ss)?/<duration>)
700		-)))|(% colspan="2" style="width:312px" %)time
701		-|(% colspan="1" style="width:507px" %)(((
	790	+)))|(% colspan="2" %)time|
	791	+|(% colspan="2" %)(((
702	702	Month
	793	+
703	703	(~-~-MM; speicifies a month independent of a year; e.g. February is black history month in the United States)
704		-)))|(% colspan="2" style="width:312px" %)string
705		-|(% colspan="1" style="width:507px" %)(((
	795	+)))|(% colspan="2" %)string|
	796	+|(% colspan="2" %)(((
706	706	MonthDay
	798	+
707	707	(~-~-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)
708		-)))|(% colspan="2" style="width:312px" %)string
709		-|(% colspan="1" style="width:507px" %)(((
	800	+)))|(% colspan="2" %)string|
	801	+|(% colspan="2" %)(((
710	710	Day
	803	+
711	711	(~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday)
712		-)))|(% colspan="2" style="width:312px" %)string
713		-|(% colspan="1" style="width:507px" %)(((
	805	+)))|(% colspan="2" %)string|
	806	+|(% colspan="2" %)(((
714	714	Time
	808	+
715	715	(hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM)
716		-)))|(% colspan="2" style="width:312px" %)string
717		-|(% colspan="1" style="width:507px" %)(((
	810	+)))|(% colspan="2" %)string|
	811	+|(% colspan="2" %)(((
718	718	Duration
	813	+
719	719	(corresponds to XML Schema xs:duration datatype)
720		-)))|(% colspan="2" style="width:312px" %)duration
721		-|(% colspan="1" style="width:507px" %)XHTML|(% colspan="2" style="width:312px" %)Metadata type – not applicable
722		-|(% colspan="1" style="width:507px" %)KeyValues|(% colspan="2" style="width:312px" %)Metadata type – not applicable
723		-|(% colspan="1" style="width:507px" %)IdentifiableReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
724		-|(% colspan="1" style="width:507px" %)DataSetReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
	815	+)))|(% colspan="2" %)duration|
	816	+|(% colspan="2" %)XHTML|(% colspan="2" %)Metadata type – not applicable|
	817	+|(% colspan="2" %)KeyValues|(% colspan="2" %)Metadata type – not applicable|
	818	+|(% colspan="2" %)IdentifiableReference|(% colspan="2" %)Metadata type – not applicable|
	819	+|(% colspan="2" %)DataSetReference|(% colspan="2" %)Metadata type – not applicable|
725	725
726		-(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes" %)
727		-**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
	821	+==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
728	728
729	729	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).
730	730
...	...	@@ -732,32 +732,39 @@
732	732
733	733	The following table describes the default conversion from the VTL basic scalar types to the SDMX data types .
734	734
735		-(% style="width:1073.29px" %)
736		-|(% style="width:207px" %)(((
737		-**VTL basic scalar type**
738		-)))|(% style="width:462px" %)(((
739		-**Default SDMX data type (BasicComponentDataType)**
740		-)))|(% style="width:402px" %)**Default output format**
741		-|(% style="width:207px" %)String|(% style="width:462px" %)String|(% style="width:402px" %)Like XML (xs:string)
742		-|(% style="width:207px" %)Number|(% style="width:462px" %)Float|(% style="width:402px" %)Like XML (xs:float)
743		-|(% style="width:207px" %)Integer|(% style="width:462px" %)Integer|(% style="width:402px" %)Like XML (xs:int)
744		-|(% style="width:207px" %)Date|(% style="width:462px" %)DateTime|(% style="width:402px" %)YYYY-MM-DDT00:00:00Z
745		-|(% style="width:207px" %)Time|(% style="width:462px" %)StandardTimePeriod|(% style="width:402px" %)<date>/<date> (as defined above)
746		-|(% style="width:207px" %)time_period|(% style="width:462px" %)(((
	829	+|(((
	830	+VTL basic
	831	+
	832	+scalar type
	833	+)))|(((
	834	+Default SDMX data type
	835	+
	836	+(BasicComponentDataType
	837	+
	838	+)
	839	+)))|Default output format
	840	+|String|String|Like XML (xs:string)
	841	+|Number|Float|Like XML (xs:float)
	842	+|Integer|Integer|Like XML (xs:int)
	843	+|Date|DateTime|YYYY-MM-DDT00:00:00Z
	844	+|Time|StandardTimePeriod|<date>/<date> (as defined above)
	845	+|time_period|(((
747	747	ReportingTimePeriod
	847	+
748	748	(StandardReportingPeriod)
749		-)))|(% style="width:402px" %)(((
	849	+)))|(((
750	750	YYYY-Pppp
	851	+
751	751	(according to SDMX )
752	752	)))
753		-|(% style="width:207px" %)Duration|(% style="width:462px" %)Duration|(% style="width:402px" %)(((
	854	+|Duration|Duration|(((
754	754	Like XML (xs:duration)
	856	+
755	755	PnYnMnDTnHnMnS
756	756	)))
757		-|(% style="width:207px" %)Boolean|(% style="width:462px" %)Boolean|(% style="width:402px" %)Like XML (xs:boolean) with the values "true" or "false"
	859	+|Boolean|Boolean|Like XML (xs:boolean) with the values "true" or "false"
758	758
759		-(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes-1" %)
760		-**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
	861	+==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
761	761
762	762	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).
763	763
...	...	@@ -811,7 +811,7 @@
811	811	|N|fixed number of digits used in the preceding textual representation of the month or the day
812	812	| |
813	813
814		-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}}.
	915	+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"]](%%)^^.
815	815
816	816	=== 12.4.5 Null Values ===
817	817
...	...	@@ -829,8 +829,10 @@
829	829
830	830	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).
831	831
832		-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.
	933	+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
833	833
	935	+TransformationScheme.
	936	+
834	834	In case a literal is operand of a VTL Cast operation, the format specified in the Cast overrides all the possible otherwise specified formats.
835	835
836	836	{{putFootnotes/}}

1747388148322-387.png

Author

...	...	@@ -1,1 +1,0 @@
1		-XWiki.helena

Size

...	...	@@ -1,1 +1,0 @@
1		-23.0 KB

Content

1747388179021-814.png

Author

...	...	@@ -1,1 +1,0 @@
1		-XWiki.helena

Size

...	...	@@ -1,1 +1,0 @@
1		-66.2 KB

Content

1747388206717-256.png

Author

...	...	@@ -1,1 +1,0 @@
1		-XWiki.helena

Size

...	...	@@ -1,1 +1,0 @@
1		-10.3 KB

Content

1747388222879-916.png

Author

...	...	@@ -1,1 +1,0 @@
1		-XWiki.helena

Size

...	...	@@ -1,1 +1,0 @@
1		-9.9 KB

Content

1747388244829-693.png

Author

...	...	@@ -1,1 +1,0 @@
1		-XWiki.helena

Size

...	...	@@ -1,1 +1,0 @@
1		-7.4 KB

Content

1747388275998-621.png

Author

...	...	@@ -1,1 +1,0 @@
1		-XWiki.helena

Size

...	...	@@ -1,1 +1,0 @@
1		-19.2 KB

Content