Changes for page 12 Validation and Transformation Language (VTL)

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...	...	@@ -14,8 +14,10 @@
14	14
15	15	The VTL language can be applied to SDMX artefacts by mapping the SDMX IM model artefacts to the model artefacts that VTL can manipulate{{footnote}}In this chapter, in order to distinguish VTL and SDMX model artefacts, the VTL ones are written in the Arial font while the SDMX ones in Courier New{{/footnote}}. Thus, the SDMX artefacts can be used in VTL as inputs and/or outputs of Transformations. It is important to be aware that the artefacts do not always have the same names in the SDMX and VTL IMs, nor do they always have the same meaning. The more evident example is given by the SDMX Dataset and the VTL "Data Set", which do not correspond one another: as a matter of fact, the VTL "Data Set" maps to the SDMX "Dataflow", while the SDMX "Dataset" has no explicit mapping to VTL (such an abstraction is not needed in the definition of VTL Transformations). A SDMX "Dataset", however, is an instance of a SDMX "Dataflow" and can be the artefact on which the VTL transformations are executed (i.e., the Transformations are defined on Dataflows and are applied to Dataflow instances that can be Datasets).
16	16
17		-The VTL programs (Transformation Schemes) are represented in SDMX through the TransformationScheme maintainable class which is composed of Transformation (nameable artefact). Each Transformation assigns the outcome of the evaluation of a VTL expression to a result.
	17	+The VTL programs (Transformation Schemes) are represented in SDMX through the TransformationScheme maintainable class which is composed of
18	18
	19	+Transformation (nameable artefact). Each Transformation assigns the outcome of the evaluation of a VTL expression to a result.
	20	+
19	19	This section does not explain the VTL language or any of the content published in the VTL guides. Rather, this is a description of how the VTL can be used in the SDMX context and applied to SDMX artefacts.
20	20
21	21	== 12.2 References to SDMX artefacts from VTL statements ==
...	...	@@ -26,8 +26,10 @@
26	26
27	27	The alias of an SDMX artefact can be its URN (Universal Resource Name), an abbreviation of its URN or another user-defined name.
28	28
29		-In any case, the aliases used in the VTL Transformations have to be mapped to the SDMX artefacts through the VtlMappingScheme and VtlMapping classes (see the section of the SDMX IM relevant to the VTL). A VtlMapping allows specifying the aliases to be used in the VTL Transformations, Rulesets{{footnote}}See also the section "VTL-DL Rulesets" in the VTL Reference Manual.{{/footnote}} or User Defined Operators{{footnote}}The VTLMappings are used also for User Defined Operators (UDO). Although UDOs are envisaged to be defined on generic operands, so that the specific artefacts to be manipulated are passed as parameters at their invocation, it is also possible that an UDO invokes directly some specific SDMX artefacts. These SDMX artefacts have to be mapped to the corresponding aliases used in the definition of the UDO through the VtlMappingScheme and VtlMapping classes as well.{{/footnote}} to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping.
	31	+In any case, the aliases used in the VTL Transformations have to be mapped to the
30	30
	33	+SDMX artefacts through the VtlMappingScheme and VtlMapping classes (see the section of the SDMX IM relevant to the VTL). A VtlMapping allows specifying the aliases to be used in the VTL Transformations, Rulesets{{footnote}}See also the section "VTL-DL Rulesets" in the VTL Reference Manual.{{/footnote}} or User Defined Operators{{footnote}}The VTLMappings are used also for User Defined Operators (UDO). Although UDOs are envisaged to be defined on generic operands, so that the specific artefacts to be manipulated are passed as parameters at their invocation, it is also possible that an UDO invokes directly some specific SDMX artefacts. These SDMX artefacts have to be mapped to the corresponding aliases used in the definition of the UDO through the VtlMappingScheme and VtlMapping classes as well.{{/footnote}} to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping.
	34	+
31	31	The correspondence between an alias and a SDMX artefact must be one-to-one, meaning that a generic alias identifies one and just one SDMX artefact while a SDMX artefact is identified by one and just one alias. In other words, within a VtlMappingScheme an artefact can have just one alias and different artefacts cannot have the same alias.
32	32
33	33	The references through the URN and the abbreviated URN are described in the following paragraphs.
...	...	@@ -198,7 +198,7 @@
198	198
199	199	=== 12.3.3 Mapping from SDMX to VTL data structures ===
200	200
201		-==== 12.3.3.1 Basic Mapping ====
	205	+**12.3.3.1 Basic Mapping**
202	202
203	203	The main mapping method from SDMX to VTL is called **Basic **mapping. This is considered as the default mapping method and is applied unless a different method is specified through the VtlMappingScheme and VtlDataflowMapping classes. When transforming **from SDMX to VTL**, this method consists in leaving the components unchanged and maintaining their names and roles, according to the following table:
204	204
...	...	@@ -228,11 +228,18 @@
228	228	The SDMX structures that contain a MeasureDimension are mapped as described below (this mapping is equivalent to a pivoting operation):
229	229
230	230	* A SDMX simple dimension becomes a VTL (simple) identifier and a SDMX TimeDimension becomes a VTL (time) identifier;
231		-* Each possible Code Cj of the SDMX MeasureDimension is mapped to a VTL Measure, having the same name as the SDMX Code (i.e. Cj); the VTL Measure Cj is a new VTL component even if the SDMX data structure has not such a Component;
	235	+* Each possible Code Cj of the SDMX MeasureDimension is mapped to a VTL Measure, having the same name as the SDMX Code (i.e. Cj); the VTL Measure Cj is a new VTL component even if the SDMX data structure has not such a
	236	+
	237	+Component;
	238	+
232	232	* The SDMX MeasureDimension is not mapped to VTL (it disappears in the VTL Data Structure);
233	233	* The SDMX Measure is not mapped to VTL as well (it disappears in the VTL Data Structure);
234	234	* An SDMX DataAttribute is mapped in different ways according to its AttributeRelationship:
235		-** If, according to the SDMX AttributeRelationship, the values of the DataAttribute do not depend on the values of the MeasureDimension, the SDMX DataAttribute becomes a VTL Attribute having the same name. This happens if the AttributeRelationship is not specified (i.e. the DataAttribute does not depend on any DimensionComponent and therefore is at data set level), or if it refers to a set (or a group) of dimensions which does not include the MeasureDimension;
	242	+** If, according to the SDMX AttributeRelationship, the values of the DataAttribute do not depend on the values of the MeasureDimension, the SDMX DataAttribute becomes a VTL Attribute having the same name. This happens if the
	243	+
	244	+AttributeRelationship is not specified (i.e. the DataAttribute does not depend on any DimensionComponent and therefore is at data set level), or if it refers to a set (or a group) of dimensions which does not include the MeasureDimension;
	245	+
	246	+*
236	236	** Otherwise, if, according to the SDMX AttributeRelationship, the values of the DataAttribute depend on the MeasureDimension, the SDMX DataAttribute is mapped to one VTL Attribute for each possible Code of the SDMX MeasureDimension. By default, the names of the VTL Attributes are obtained by concatenating the name of the SDMX DataAttribute and the names of the correspondent Code of the MeasureDimension separated by underscore. For example, if the SDMX DataAttribute is named DA and the possible Codes of the SDMX MeasureDimension are named C1, C2, …, Cn, then the corresponding VTL Attributes will be named DA_C1, DA_C2, …, DA_Cn (if different names are desired, they can be achieved afterwards by renaming the Attributes through VTL operators).
237	237	** Like in the Basic mapping, the resulting VTL Attributes are considered as dependent on all the VTL identifiers (i.e. "at data point / observation level"), because VTL does not have the SDMX notion of Attribute Relationship.
238	238
...	...	@@ -255,7 +255,10 @@
255	255	At observation / data point level, calling Cj (j=1, … n) the j^^th^^ Code of the MeasureDimension:
256	256
257	257	* The set of SDMX observations having the same values for all the Dimensions except than the MeasureDimension become one multi-measure VTL Data Point, having one Measure for each Code Cj of the SDMX MeasureDimension;
258		-* The values of the SDMX simple Dimensions, TimeDimension and DataAttributes not depending on the MeasureDimension (these components by definition have always the same values for all the observations of the set above) become the values of the corresponding VTL (simple) Identifiers, (time) Identifier and Attributes.
	269	+* The values of the SDMX simple Dimensions, TimeDimension and DataAttributes not depending on the MeasureDimension (these components by definition have always the same values for all the observations of the set above) become the values of the corresponding VTL (simple)
	270	+
	271	+Identifiers, (time) Identifier and Attributes.
	272	+
259	259	* The value of the Measure of the SDMX observation belonging to the set above and having MeasureDimension=Cj becomes the value of the VTL Measure Cj
260	260	* For the SDMX DataAttributes depending on the MeasureDimension, the value of the DataAttribute DA of the SDMX observation belonging to the set above and having MeasureDimension=Cj becomes the value of the VTL Attribute DA_Cj
261	261
...	...	@@ -348,7 +348,7 @@
348	348	The mapping table is the following:
349	349
350	350	(% style="width:689.294px" %)
351		-|(% style="width:344px" %)**VTL**|(% style="width:341px" %)**SDMX**
	365	+|(% style="width:344px" %)VTL|(% style="width:341px" %)SDMX
352	352	|(% style="width:344px" %)(Simple) Identifier|(% style="width:341px" %)Dimension
353	353	|(% style="width:344px" %)(Time) Identifier|(% style="width:341px" %)TimeDimension
354	354	|(% style="width:344px" %)Some Measures|(% style="width:341px" %)Measure
...	...	@@ -408,17 +408,24 @@
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	417	‘DF1(1.0.0)/POPULATION.USA’ :=
	436	+
418	418	DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA” ];
419	419
420	420	‘DF1(1.0.0)/POPULATION.CANADA’ :=
	440	+
421	421	DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
	442	+
422	422	… … …
423	423
424	424	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}}
...	...	@@ -432,6 +432,7 @@
432	432	This is equivalent to the application of the VTL “sub” operator only to the identifier //INDICATOR//:
433	433
434	434	‘DF1(1.0.0)/POPULATION.’ :=
	456	+
435	435	DF1(1.0.0) [ sub INDICATOR=“POPULATION” ];
436	436
437	437	Therefore the VTL Data Set ‘DF1(1.0.0)/POPULATION.’ would have the identifiers COUNTRY and TIME_PERIOD.
...	...	@@ -462,8 +462,11 @@
462	462	‘DF2(1.0.0)/GDPPERCAPITA.USA’ <- expression11; ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ <- expression12;
463	463
464	464	… … …
	487	+
465	465	‘DF2(1.0.0)/POPGROWTH.USA’ <- expression21;
	489	+
466	466	‘DF2(1.0.0)/POPGROWTH.CANADA’ <- expression22;
	491	+
467	467	… … …
468	468
469	469	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:
...	...	@@ -471,23 +471,37 @@
471	471	VTL dataset INDICATOR value COUNTRY value
472	472
473	473	‘DF2(1.0.0)/GDPPERCAPITA.USA’ GDPPERCAPITA USA
	499	+
474	474	‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ GDPPERCAPITA CANADA … … …
	501	+
475	475	‘DF2(1.0.0)/POPGROWTH.USA’ POPGROWTH USA
	503	+
476	476	‘DF2(1.0.0)/POPGROWTH.CANADA’ POPGROWTH CANADA
	505	+
477	477	… … …
478	478
479	479	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:
480	480
481	481	DF2bis_GDPPERCAPITA_USA := ‘DF2(1.0.0)/GDPPERCAPITA.USA’ [calc identifier INDICATOR := ”GDPPERCAPITA”, identifier COUNTRY := ”USA”];
	511	+
482	482	DF2bis_GDPPERCAPITA_CANADA := ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ [calc identifier INDICATOR:=”GDPPERCAPITA”, identifier COUNTRY:=”CANADA”]; … … …
	513	+
483	483	DF2bis_POPGROWTH_USA := ‘DF2(1.0.0)/POPGROWTH.USA’
	515	+
484	484	[calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”USA”];
	517	+
485	485	DF2bis_POPGROWTH_CANADA’ := ‘DF2(1.0.0)/POPGROWTH.CANADA’ [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”CANADA”]; … … …
	519	+
486	486	DF2(1.0) <- UNION (DF2bis_GDPPERCAPITA_USA’,
	521	+
487	487	DF2bis_GDPPERCAPITA_CANADA’,
	523	+
488	488	… ,
	525	+
489	489	DF2bis_POPGROWTH_USA’,
	527	+
490	490	DF2bis_POPGROWTH_CANADA’
	529	+
491	491	…);
492	492
493	493	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
...	...	@@ -537,16 +537,16 @@
537	537
538	538	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).
539	539
540		-Another difference consists in the fact that all Value Domains are considered as identifiable objects in VTL either if enumerated or not, while in SDMX the Codelist (corresponding to a VTL enumerated Value Domain) is identifiable, while the SDMX non-enumerated Representation (corresponding to a VTL non-enumerated Value Domain) is not identifiable. As a consequence, the definition of the VTL Rulesets, which in VTL can refer either to enumerated or non-enumerated value domains, in SDMX can refer only to enumerated Value Domains (i.e. to SDMX Codelists). As for the mapping between VTL variables and SDMX Concepts, it should be noted that these artefacts do not coincide perfectly. In fact, the VTL variables are represented variables, defined always on the same Value Domain (“Representation” in SDMX) independently of the data set / data structure in which they appear{{footnote}}By using represented variables, VTL can assume that data structures having the same variables as identifiers can be composed one another because the correspondent values can match.{{/footnote}}, while the SDMX Concepts can have different Representations in different DataStructures.{{footnote}}A Concept becomes a Component in a DataStructureDefinition, and Components can have different LocalRepresentations in different DataStructureDefinitions, also overriding the (possible) base representation of the Concept.{{/footnote}} This means that one SDMX Concept can correspond to many VTL Variables, one for each representation the Concept has.
	579	+Another difference consists in the fact that all Value Domains are considered as identifiable objects in VTL either if enumerated or not, while in SDMX the Codelist (corresponding to a VTL enumerated Value Domain) is identifiable, while the SDMX non-enumerated Representation (corresponding to a VTL non-enumerated Value Domain) is not identifiable. As a consequence, the definition of the VTL Rulesets, which in VTL can refer either to enumerated or non-enumerated value domains, in SDMX can refer only to enumerated Value Domains (i.e. to SDMX Codelists). As for the mapping between VTL variables and SDMX Concepts, it should be noted that these artefacts do not coincide perfectly. In fact, the VTL variables are represented variables, defined always on the same Value Domain (“Representation” in SDMX) independently of the data set / data structure in which they appear^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^40^^>>path:#sdfootnote40sym||name="sdfootnote40anc"]](%%)^^, while the SDMX Concepts can have different Representations in different DataStructures.^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^41^^>>path:#sdfootnote41sym||name="sdfootnote41anc"]](%%)^^ This means that one SDMX Concept can correspond to many VTL Variables, one for each representation the Concept has.
541	541
542	542	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
543	543
544		-DS_c := DS_a + DS_b (where DS_a, DS_b, DS_c are VTL Data Sets)
	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.
545	545
546		-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.
547		-
548	548	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
549	549
	587	+[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_59eee18f.gif||alt="Shape5" height="1" width="192"]]
	588	+
550	550	Transformations to ensure that the VTL expressions are consistent with the actual representations of the correspondent SDMX Concepts.
551	551
552	552	It remains up to the SDMX-VTL definer also the assurance of the consistency between a VTL Ruleset defined on Variables and the SDMX Components on which the Ruleset is applied. In fact, a VTL Ruleset is expressed by means of the values of the Variables (i.e. SDMX Concepts), i.e. assuming definite representations for them (e.g. ISOalpha-3 for country). If the Ruleset is applied to SDMX Components that have the same name of the Concept they refer to but different representations (e.g. ISO-alpha-2 for country), the Ruleset cannot work properly.
...	...	@@ -561,8 +561,7 @@
561	561
562	562	[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_e3df33ae.png||height="543" width="483"]]
563	563
564		-(% class="wikigeneratedid" id="HFigure222013VTLDataTypes" %)
565		-**Figure 22 – VTL Data Types**
	603	+==== Figure 22 – VTL Data Types ====
566	566
567	567	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.
568	568
...	...	@@ -569,12 +569,131 @@
569	569	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):
570	570
571	571
572		-**Figure 23 – VTL Basic Scalar Types**
573	573
574	574	(((
575		-
	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"]]
576	576	)))
577	577
	733	+==== Figure 23 – VTL Basic Scalar Types ====
	734	+
578	578	=== 12.4.2 VTL basic scalar types and SDMX data types ===
579	579
580	580	The VTL assumes that a basic scalar type has a unique internal representation and can have more external representations.
...	...	@@ -597,159 +597,204 @@
597	597
598	598	The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types.
599	599
600		-(% style="width:823.294px" %)
601		-|(% style="width:509px" %)**SDMX data type (BasicComponentDataType)**|(% style="width:312px" %)**Default VTL basic scalar type**
602		-|(% style="width:509px" %)(((
	757	+|SDMX data type (BasicComponentDataType)|Default VTL basic scalar type
	758	+|(((
603	603	String
	760	+
604	604	(string allowing any character)
605		-)))|(% style="width:312px" %)string
606		-|(% style="width:509px" %)(((
	762	+)))|string
	763	+|(((
607	607	Alpha
	765	+
608	608	(string which only allows A-z)
609		-)))|(% style="width:312px" %)string
610		-|(% style="width:509px" %)(((
	767	+)))|string
	768	+|(((
611	611	AlphaNumeric
	770	+
612	612	(string which only allows A-z and 0-9)
613		-)))|(% style="width:312px" %)string
614		-|(% style="width:509px" %)(((
	772	+)))|string
	773	+|(((
615	615	Numeric
	775	+
616	616	(string which only allows 0-9, but is not numeric so that is can having leading zeros)
617		-)))|(% style="width:312px" %)string
618		-|(% style="width:509px" %)(((
	777	+)))|string
	778	+|(((
619	619	BigInteger
	780	+
620	620	(corresponds to XML Schema xs:integer datatype; infinite set of integer values)
621		-)))|(% style="width:312px" %)integer
622		-|(% style="width:509px" %)(((
	782	+)))|integer
	783	+|(((
623	623	Integer
624		-(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647 (inclusive))
625		-)))|(% style="width:312px" %)integer
626		-|(% style="width:509px" %)(((
	785	+
	786	+(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647
	787	+
	788	+(inclusive))
	789	+)))|integer
	790	+|(((
627	627	Long
628		-(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and +9223372036854775807 (inclusive))
629		-)))|(% style="width:312px" %)integer
630		-|(% style="width:509px" %)(((
	792	+
	793	+(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and
	794	+
	795	++9223372036854775807 (inclusive))
	796	+)))|integer
	797	+|(((
631	631	Short
	799	+
632	632	(corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive))
633		-)))|(% style="width:312px" %)integer
634		-|(% 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
635		-|(% style="width:509px" %)(((
	801	+)))|integer
	802	+|Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|number
	803	+|(((
636	636	Float
	805	+
637	637	(corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type)
638		-)))|(% style="width:312px" %)number
639		-|(% style="width:509px" %)(((
	807	+)))|number
	808	+|(((
640	640	Double
	810	+
641	641	(corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type)
642		-)))|(% style="width:312px" %)number
643		-|(% style="width:509px" %)(((
	812	+)))|number
	813	+|(((
644	644	Boolean
645		-(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of binary-valued logic: {true, false})
646		-)))|(% style="width:312px" %)boolean
647	647
648		-(% style="width:822.294px" %)
649		-|(% colspan="2" style="width:507px" %)(((
	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" %)(((
650	650	URI
	823	+
651	651	(corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference)
652		-)))|(% colspan="1" style="width:311px" %)string
653		-|(% colspan="2" style="width:507px" %)(((
	825	+)))|(% colspan="2" %)string
	826	+| |(% colspan="2" %)(((
654	654	Count
	828	+
655	655	(an integer following a sequential pattern, increasing by 1 for each occurrence)
656		-)))|(% colspan="1" style="width:311px" %)integer
657		-|(% colspan="2" style="width:507px" %)(((
	830	+)))|(% colspan="2" %)integer
	831	+| |(% colspan="2" %)(((
658	658	InclusiveValueRange
	833	+
659	659	(decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
660		-)))|(% colspan="1" style="width:311px" %)number
661		-|(% colspan="2" style="width:507px" %)(((
	835	+)))|(% colspan="2" %)number
	836	+| |(% colspan="2" %)(((
662	662	ExclusiveValueRange
	838	+
663	663	(decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
664		-)))|(% colspan="1" style="width:311px" %)number
665		-|(% colspan="2" style="width:507px" %)(((
	840	+)))|(% colspan="2" %)number
	841	+| |(% colspan="2" %)(((
666	666	Incremental
	843	+
667	667	(decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation)
668		-)))|(% colspan="1" style="width:311px" %)number
669		-|(% colspan="2" style="width:507px" %)(((
	845	+)))|(% colspan="2" %)number
	846	+| |(% colspan="2" %)(((
670	670	ObservationalTimePeriod
	848	+
671	671	(superset of StandardTimePeriod and TimeRange)
672		-)))|(% colspan="1" style="width:311px" %)time
673		-|(% colspan="2" style="width:507px" %)(((
	850	+)))|(% colspan="2" %)time
	851	+| |(% colspan="2" %)(((
674	674	StandardTimePeriod
675		-(superset of BasicTimePeriod and ReportingTimePeriod)
676		-)))|(% colspan="1" style="width:311px" %)time
677		-|(% colspan="2" style="width:507px" %)(((
	853	+
	854	+(superset of BasicTimePeriod and
	855	+
	856	+ReportingTimePeriod)
	857	+)))|(% colspan="2" %)time
	858	+| |(% colspan="2" %)(((
678	678	BasicTimePeriod
	860	+
679	679	(superset of GregorianTimePeriod and DateTime)
680		-)))|(% colspan="1" style="width:311px" %)date
681		-|(% colspan="2" style="width:507px" %)(((
	862	+)))|(% colspan="2" %)date
	863	+| |(% colspan="2" %)(((
682	682	GregorianTimePeriod
	865	+
683	683	(superset of GregorianYear, GregorianYearMonth, and GregorianDay)
684		-)))|(% colspan="1" style="width:311px" %)date
685		-|(% colspan="2" style="width:507px" %)GregorianYear (YYYY)|(% colspan="1" style="width:311px" %)date
686		-|(% colspan="2" style="width:507px" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="1" style="width:311px" %)date
687		-|(% colspan="2" style="width:507px" %)GregorianDay (YYYY-MM-DD)|(% colspan="1" style="width:311px" %)date
688		-|(% colspan="2" style="width:507px" %)(((
	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" %)(((
689	689	ReportingTimePeriod
690		-(superset of RepostingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, ReportingDay)
691		-)))|(% colspan="1" style="width:311px" %)time_period
692		-|(% colspan="2" style="width:507px" %)(((
	873	+
	874	+(superset of RepostingYear, ReportingSemester,
	875	+
	876	+ReportingTrimester, ReportingQuarter,
	877	+
	878	+ReportingMonth, ReportingWeek, ReportingDay)
	879	+)))|(% colspan="2" %)time_period
	880	+| |(% colspan="2" %)(((
693	693	ReportingYear
	882	+
694	694	(YYYY-A1 – 1 year period)
695		-)))|(% colspan="1" style="width:311px" %)time_period
696		-|(% colspan="2" style="width:507px" %)(((
	884	+)))|(% colspan="2" %)time_period
	885	+| |(% colspan="2" %)(((
697	697	ReportingSemester
	887	+
698	698	(YYYY-Ss – 6 month period)
699		-)))|(% colspan="1" style="width:311px" %)time_period
700		-|(% colspan="2" style="width:507px" %)(((
	889	+)))|(% colspan="2" %)time_period
	890	+| |(% colspan="2" %)(((
701	701	ReportingTrimester
	892	+
702	702	(YYYY-Tt – 4 month period)
703		-)))|(% colspan="1" style="width:311px" %)time_period
704		-|(% colspan="2" style="width:507px" %)(((
	894	+)))|(% colspan="2" %)time_period
	895	+| |(% colspan="2" %)(((
705	705	ReportingQuarter
	897	+
706	706	(YYYY-Qq – 3 month period)
707		-)))|(% colspan="1" style="width:311px" %)time_period
708		-|(% colspan="2" style="width:507px" %)(((
	899	+)))|(% colspan="2" %)time_period
	900	+| |(% colspan="2" %)(((
709	709	ReportingMonth
	902	+
710	710	(YYYY-Mmm – 1 month period)
711		-)))|(% colspan="1" style="width:311px" %)time_period
712		-|(% colspan="2" style="width:507px" %)ReportingWeek|(% colspan="1" style="width:311px" %)time_period
713		-|(% 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" %)
714		-|(% colspan="1" style="width:507px" %)(((
	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" %)(((
715	715	ReportingDay
	911	+
716	716	(YYYY-Dddd – 1 day period)
717		-)))|(% colspan="2" style="width:312px" %)time_period
718		-|(% colspan="1" style="width:507px" %)(((
	913	+)))|(% colspan="2" %)time_period|
	914	+|(% colspan="2" %)(((
719	719	DateTime
	916	+
720	720	(YYYY-MM-DDThh:mm:ss)
721		-)))|(% colspan="2" style="width:312px" %)date
722		-|(% colspan="1" style="width:507px" %)(((
	918	+)))|(% colspan="2" %)date|
	919	+|(% colspan="2" %)(((
723	723	TimeRange
	921	+
724	724	(YYYY-MM-DD(Thh:mm:ss)?/<duration>)
725		-)))|(% colspan="2" style="width:312px" %)time
726		-|(% colspan="1" style="width:507px" %)(((
	923	+)))|(% colspan="2" %)time|
	924	+|(% colspan="2" %)(((
727	727	Month
	926	+
728	728	(~-~-MM; speicifies a month independent of a year; e.g. February is black history month in the United States)
729		-)))|(% colspan="2" style="width:312px" %)string
730		-|(% colspan="1" style="width:507px" %)(((
	928	+)))|(% colspan="2" %)string|
	929	+|(% colspan="2" %)(((
731	731	MonthDay
	931	+
732	732	(~-~-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)
733		-)))|(% colspan="2" style="width:312px" %)string
734		-|(% colspan="1" style="width:507px" %)(((
	933	+)))|(% colspan="2" %)string|
	934	+|(% colspan="2" %)(((
735	735	Day
	936	+
736	736	(~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday)
737		-)))|(% colspan="2" style="width:312px" %)string
738		-|(% colspan="1" style="width:507px" %)(((
	938	+)))|(% colspan="2" %)string|
	939	+|(% colspan="2" %)(((
739	739	Time
	941	+
740	740	(hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM)
741		-)))|(% colspan="2" style="width:312px" %)string
742		-|(% colspan="1" style="width:507px" %)(((
	943	+)))|(% colspan="2" %)string|
	944	+|(% colspan="2" %)(((
743	743	Duration
	946	+
744	744	(corresponds to XML Schema xs:duration datatype)
745		-)))|(% colspan="2" style="width:312px" %)duration
746		-|(% colspan="1" style="width:507px" %)XHTML|(% colspan="2" style="width:312px" %)Metadata type – not applicable
747		-|(% colspan="1" style="width:507px" %)KeyValues|(% colspan="2" style="width:312px" %)Metadata type – not applicable
748		-|(% colspan="1" style="width:507px" %)IdentifiableReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
749		-|(% colspan="1" style="width:507px" %)DataSetReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
	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|
750	750
751		-(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes" %)
752		-**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
	954	+==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
753	753
754	754	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).
755	755
...	...	@@ -757,32 +757,39 @@
757	757
758	758	The following table describes the default conversion from the VTL basic scalar types to the SDMX data types .
759	759
760		-(% style="width:1073.29px" %)
761		-|(% style="width:207px" %)(((
762		-**VTL basic scalar type**
763		-)))|(% style="width:462px" %)(((
764		-**Default SDMX data type (BasicComponentDataType)**
765		-)))|(% style="width:402px" %)**Default output format**
766		-|(% style="width:207px" %)String|(% style="width:462px" %)String|(% style="width:402px" %)Like XML (xs:string)
767		-|(% style="width:207px" %)Number|(% style="width:462px" %)Float|(% style="width:402px" %)Like XML (xs:float)
768		-|(% style="width:207px" %)Integer|(% style="width:462px" %)Integer|(% style="width:402px" %)Like XML (xs:int)
769		-|(% style="width:207px" %)Date|(% style="width:462px" %)DateTime|(% style="width:402px" %)YYYY-MM-DDT00:00:00Z
770		-|(% style="width:207px" %)Time|(% style="width:462px" %)StandardTimePeriod|(% style="width:402px" %)<date>/<date> (as defined above)
771		-|(% style="width:207px" %)time_period|(% style="width:462px" %)(((
	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|(((
772	772	ReportingTimePeriod
	980	+
773	773	(StandardReportingPeriod)
774		-)))|(% style="width:402px" %)(((
	982	+)))|(((
775	775	YYYY-Pppp
	984	+
776	776	(according to SDMX )
777	777	)))
778		-|(% style="width:207px" %)Duration|(% style="width:462px" %)Duration|(% style="width:402px" %)(((
	987	+|Duration|Duration|(((
779	779	Like XML (xs:duration)
	989	+
780	780	PnYnMnDTnHnMnS
781	781	)))
782		-|(% style="width:207px" %)Boolean|(% style="width:462px" %)Boolean|(% style="width:402px" %)Like XML (xs:boolean) with the values "true" or "false"
	992	+|Boolean|Boolean|Like XML (xs:boolean) with the values "true" or "false"
783	783
784		-(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes-1" %)
785		-**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
	994	+==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
786	786
787	787	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).
788	788
...	...	@@ -836,7 +836,7 @@
836	836	|N|fixed number of digits used in the preceding textual representation of the month or the day
837	837	| |
838	838
839		-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}}.
	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" %)^^42^^>>path:#sdfootnote42sym||name="sdfootnote42anc"]](%%)^^.
840	840
841	841	=== 12.4.5 Null Values ===
842	842
...	...	@@ -854,8 +854,10 @@
854	854
855	855	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).
856	856
857		-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.
	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
858	858
	1068	+TransformationScheme.
	1069	+
859	859	In case a literal is operand of a VTL Cast operation, the format specified in the Cast overrides all the possible otherwise specified formats.
860	860
861	861	{{putFootnotes/}}