Changes for page 12 Validation and Transformation Language (VTL)

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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.
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53	53
54	54	The generic structure of the URN is the following:
55	55
56		-SDMXprefix.SDMX-IM-package-name.class-name=agency-id:maintainedobject-id
	52	+SDMXprefix.SDMX-IM-package-name.class-name=agency-id:maintainedobject-id (maintainedobject-version).*container-object-id.object-id
57	57
58		-(maintainedobject-version).*container-object-id.object-id
59		-
60	60	The **SDMXprefix** is "urn:sdmx:org", always the same for all SDMX artefacts.
61	61
62	62	The SDMX-IM-package-name** **is the concatenation of the string** **"sdmx.infomodel." with the package-name, which the artefact belongs to. For example, for referencing a Dataflow the SDMX-IM-package-name is "sdmx.infomodel.datastructure", because the class Dataflow belongs to the package "datastructure".
...	...	@@ -68,10 +68,7 @@
68	68	The maintainedobject-id is the name of the maintained object which the artefact belongs to, and in case the artefact itself is maintainable{{footnote}}i.e., the artefact belongs to a maintainable class{{/footnote}}, coincides with the name of the artefact. Therefore the maintainedobject-id depends on the class of the artefact:
69	69
70	70	* if the artefact is a Dataflow, which is a maintainable class, the maintainedobject-id is the Dataflow name (dataflow-id);
71		-* if the artefact is a Dimension, Measure, TimeDimension or DataAttribute, which are not maintainable and belong to the
72		-
73		-DataStructure maintainable class, the maintainedobject-id is the name of the DataStructure (dataStructure-id) which the artefact belongs to;
74		-
	65	+* if the artefact is a Dimension, Measure, TimeDimension or DataAttribute, which are not maintainable and belong to the DataStructure maintainable class, the maintainedobject-id is the name of the DataStructure (dataStructure-id) which the artefact belongs to;
75	75	* if the artefact is a Concept, which is not maintainable and belongs to the ConceptScheme maintainable class, the maintainedobject-id is the name of the ConceptScheme (conceptScheme-id) which the artefact belongs to;
76	76	* if the artefact is a Codelist, which is a maintainable class, the maintainedobject-id is the Codelist name (codelist-id).
77	77
...	...	@@ -87,9 +87,7 @@
87	87	For example, by using the URN, the VTL Transformation that sums two SDMX Dataflows DF1 and DF2 and assigns the result to a third persistent Dataflow DFR, assuming that DF1, DF2 and DFR are the maintainedobject-id of the three Dataflows, that their version is 1.0.0 and their Agency is AG, would be written as{{footnote}}Since these references to SDMX objects include non-permitted characters as per the VTL ID notation, they need to be included between single quotes, according to the VTL rules for irregular names.{{/footnote}}:
88	88
89	89	'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DFR(1.0.0)' <-
90		-
91	91	'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF1(1.0.0)' +
92		-
93	93	'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF2(1.0.0)'
94	94
95	95	=== 12.2.3 Abbreviation of the URN ===
...	...	@@ -98,10 +98,10 @@
98	98
99	99	The URN can be abbreviated by omitting the parts that are not essential for the identification of the artefact or that can be deduced from other available information, including the context in which the invocation is made. The possible abbreviations are described below.
100	100
101		-* The SDMXprefix can be omitted for all the SDMX objects, because it is a prefixed string (urn:sdmx:org), always the same for SDMX objects. • The SDMX-IM-package-name** &nbsp;**can be omitted as well because it can be deduced from the class-name that follows it (the table of the SDMX-IM packages and classes that allows this deduction is in the SDMX 2.1 Standards - Section 5 - Registry Specifications, paragraph 6.2.3). In particular, considering the object classes of the artefacts that VTL can reference, the package is:
	90	+* The SDMXprefix can be omitted for all the SDMX objects, because it is a prefixed string (urn:sdmx:org), always the same for SDMX objects. • The SDMX-IM-package-name** **can be omitted as well because it can be deduced from the class-name that follows it (the table of the SDMX-IM packages and classes that allows this deduction is in the SDMX 2.1 Standards - Section 5 - Registry Specifications, paragraph 6.2.3). In particular, considering the object classes of the artefacts that VTL can reference, the package is:
102	102	** "datastructure" for the classes Dataflow, Dimension, TimeDimension, Measure, DataAttribute, o "conceptscheme" for the class Concept, o "codelist" for the class Codelist.
103		-* The class-name can be omitted as it can be deduced from the VTL invocation. In particular, starting from the VTL class of the invoked artefact (e.g. dataset, component, identifier, measure, attribute, variable, valuedomain), which is known given the syntax of the invoking VTL operator^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^13^^>>path:#sdfootnote13sym||name="sdfootnote13anc"]](%%)^^, the SDMX class can be deduced from the mapping rules between VTL and SDMX (see the section "Mapping between VTL and SDMX" hereinafter)^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^14^^>>path:#sdfootnote14sym||name="sdfootnote14anc"]](%%)^^.
104		-* If the agency-id is not specified, it is assumed by default equal to the agency-id of the TransformationScheme, UserDefinedOperatorScheme or RulesetScheme from which the artefact is invoked. For example, the agencyid can be omitted if it is the same as the invoking TransformationScheme and cannot be omitted if the artefact comes from another agency^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^15^^>>path:#sdfootnote15sym||name="sdfootnote15anc"]](%%)^^. Take also into account that, according to the VTL consistency rules, the agency of the result of a Transformation must be the same as its TransformationScheme, therefore the agency-id can be omitted for all the results (left part of Transformation statements).
	92	+* The class-name can be omitted as it can be deduced from the VTL invocation. In particular, starting from the VTL class of the invoked artefact (e.g. dataset, component, identifier, measure, attribute, variable, valuedomain), which is known given the syntax of the invoking VTL operator{{footnote}}For the syntax of the VTL operators see the VTL Reference Manual{{/footnote}}, the SDMX class can be deduced from the mapping rules between VTL and SDMX (see the section "Mapping between VTL and SDMX" hereinafter){{footnote}}In case the invoked artefact is a VTL component, which can be invoked only within the invocation of a VTL data set (SDMX Dataflow), the specific SDMX class-name (e.g. Dimension, TimeDimension, Measure or DataAttribute) can be deduced from the data structure of the SDMX Dataflow, which the component belongs to.{{/footnote}}.
	93	+* If the agency-id is not specified, it is assumed by default equal to the agency-id of the TransformationScheme, UserDefinedOperatorScheme or RulesetScheme from which the artefact is invoked. For example, the agencyid can be omitted if it is the same as the invoking TransformationScheme and cannot be omitted if the artefact comes from another agency{{footnote}}If the Agency is composite (for example AgencyA.Dept1.Unit2), the agency is considered different even if only part of the composite name is different (for example AgencyA.Dept1.Unit3 is a different Agency than the previous one). Moreover the agency-id cannot be omitted in part (i.e., if a TransformationScheme owned by AgencyA.Dept1.Unit2 references an artefact coming from AgencyA.Dept1.Unit3, the specification of the agency-id becomes mandatory and must be complete, without omitting the possibly equal parts like AgencyA.Dept1){{/footnote}}. Take also into account that, according to the VTL consistency rules, the agency of the result of a Transformation must be the same as its TransformationScheme, therefore the agency-id can be omitted for all the results (left part of Transformation statements).
105	105	* As for the maintainedobject-id, this is essential in some cases while in other cases it can be omitted: o if the referenced artefact is a Dataflow, which is a maintainable class, the maintainedobject-id is the dataflow-id and obviously cannot be omitted;
106	106	** if the referenced artefact is a Dimension, TimeDimension, Measure, DataAttribute, which are not maintainable and belong to the DataStructure maintainable class, the maintainedobject-id is the dataStructure-id and can be omitted, given that these components are always invoked within the invocation of a Dataflow, whose dataStructure-id can be deduced from the SDMX structural definitions;
107	107	** if the referenced artefact is a Concept, which is not maintainable and belong to the ConceptScheme maintainable class, the maintained object is the conceptScheme-id and cannot be omitted;
...	...	@@ -114,9 +114,7 @@
114	114	For example, the full formulation that uses the complete URN shown at the end of the previous paragraph:
115	115
116	116	'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DFR(1.0.0)' :=
117		-
118	118	'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF1(1.0.0)' +
119		-
120	120	'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF2(1.0.0)'
121	121
122	122	by omitting all the non-essential parts would become simply:
...	...	@@ -123,11 +123,11 @@
123	123
124	124	DFR := DF1 + DF2
125	125
126		-The references to the Codelists can be simplified similarly. For example, given the non-abbreviated reference to the Codelist AG:CL_FREQ(1.0.0), which is^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^16^^>>path:#sdfootnote16sym||name="sdfootnote16anc"]](%%)^^:
	113	+The references to the Codelists can be simplified similarly. For example, given the non-abbreviated reference to the Codelist AG:CL_FREQ(1.0.0), which is{{footnote}}Single quotes are needed because this reference is not a VTL regular name.{{/footnote}}:
127	127
128	128	'urn:sdmx:org.sdmx.infomodel.codelist.Codelist=AG:CL_FREQ(1.0.0)'
129	129
130		-if the Codelist is referenced from a RulesetScheme belonging to the agency AG, omitting all the optional parts, the abbreviated reference would become simply^^19^^:
	117	+if the Codelist is referenced from a RulesetScheme belonging to the agency AG, omitting all the optional parts, the abbreviated reference would become simply{{footnote}}Single quotes are not needed in this case because CL_FREQ is a VTL regular name.{{/footnote}}:
131	131
132	132	CL_FREQ
133	133
...	...	@@ -141,7 +141,7 @@
141	141
142	142	SECTOR
143	143
144		-For example, the Transformation for renaming the component SECTOR of the Dataflow DF1 into SEC can be written as^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^17^^>>path:#sdfootnote17sym||name="sdfootnote17anc"]](%%)^^:
	131	+For example, the Transformation for renaming the component SECTOR of the Dataflow DF1 into SEC can be written as{{footnote}}The result DFR(1.0.0) is be equal to DF1(1.0.0) save that the component SECTOR is called SEC{{/footnote}}:
145	145
146	146	'DFR(1.0.0)' := 'DF1(1.0.0)' [rename SECTOR to SEC]
147	147
...	...	@@ -173,9 +173,9 @@
173	173
174	174	The VTL Rulesets have a signature, in which the Value Domains or the Variables on which the Ruleset is defined are declared, and a body, which contains the Rules.
175	175
176		-In the signature, given the mapping between VTL and SDMX better described in the following paragraphs, a reference to a VTL Value Domain becomes a reference to a SDMX Codelist, while a reference to a VTL Represented Variable becomes a reference to a SDMX Concept, assuming for it a definite representation^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^18^^>>path:#sdfootnote18sym||name="sdfootnote18anc"]](%%)^^.
	163	+In the signature, given the mapping between VTL and SDMX better described in the following paragraphs, a reference to a VTL Value Domain becomes a reference to a SDMX Codelist, while a reference to a VTL Represented Variable becomes a reference to a SDMX Concept, assuming for it a definite representation{{footnote}}Rulesets of this kind cannot be reused when the referenced Concept has a different representation.{{/footnote}}.
177	177
178		-In general, for referencing SDMX Codelists and Concepts, the conventions described in the previous paragraphs apply. In the Ruleset syntax, the elements that reference SDMX artefacts are called "valueDomain" and "variable" for the Datapoint Rulesets and "ruleValueDomain", "ruleVariable", "condValueDomain" "condVariable" for the Hierarchical Rulesets). The syntax of the Ruleset signature allows also to define aliases of the elements above, these aliases are valid only within the specific Ruleset definition statement and cannot be mapped to SDMX.^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^19^^>>path:#sdfootnote19sym||name="sdfootnote19anc"]](%%)^^
	165	+In general, for referencing SDMX Codelists and Concepts, the conventions described in the previous paragraphs apply. In the Ruleset syntax, the elements that reference SDMX artefacts are called "valueDomain" and "variable" for the Datapoint Rulesets and "ruleValueDomain", "ruleVariable", "condValueDomain" "condVariable" for the Hierarchical Rulesets). The syntax of the Ruleset signature allows also to define aliases of the elements above, these aliases are valid only within the specific Ruleset definition statement and cannot be mapped to SDMX.{{footnote}}See also the section "VTL-DL Rulesets" in the VTL Reference Manual.{{/footnote}}
179	179
180	180	In the body of the Rulesets, the Codes and in general all the Values can be written without any other specification, because the artefact, which the Values are referred (Codelist, Concept) to can be deduced from the Ruleset signature.
181	181
...	...	@@ -187,15 +187,15 @@
187	187
188	188	Every time a SDMX object is referenced in a VTL Transformation as an input operand, there is the need to generate a VTL definition of the object, so that the VTL operations can take place. This can be made starting from the SDMX definition and applying a SDMX-VTL mapping method in the direction from SDMX to VTL. The possible mapping methods from SDMX to VTL are described in the following paragraphs and are conceived to allow the automatic deduction of the VTL definition of the object from the knowledge of the SDMX definition.
189	189
190		-In the opposite direction, every time an object calculated by means of VTL must be treated as a SDMX object (for example for exchanging it through SDMX), there is the need of a SDMX definition of the object, so that the SDMX operations can take place. The SDMX definition is needed for the VTL objects for which a SDMX use is envisaged^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^20^^>>path:#sdfootnote20sym||name="sdfootnote20anc"]](%%)^^.
	177	+In the opposite direction, every time an object calculated by means of VTL must be treated as a SDMX object (for example for exchanging it through SDMX), there is the need of a SDMX definition of the object, so that the SDMX operations can take place. The SDMX definition is needed for the VTL objects for which a SDMX use is envisaged{{footnote}}If a calculated artefact is persistent, it needs a persistent definition, i.e. a SDMX definition in a SDMX environment. In addition, possible calculated artefact that are not persistent may require a SDMX definition, for example when the result of a non-persistent calculation is disseminated through SDMX tools (like an inquiry tool).{{/footnote}}.
191	191
192	192	The mapping methods from VTL to SDMX are described in the following paragraphs as well, however they do not allow the complete SDMX definition to be automatically deduced from the VTL definition, more than all because the former typically contains additional information in respect to the latter. For example, the definition of a SDMX DSD includes also some mandatory information not available in VTL (like the concept scheme to which the SDMX components refer, the ‘usage’ and ‘attributeRelationship’ for the DataAttributes and so on). Therefore the mapping methods from VTL to SDMX provide only a general guidance for generating SDMX definitions properly starting from the information available in VTL, independently of how the SDMX definition it is actually generated (manually, automatically or part and part).
193	193
194	194	=== 12.3.2 General mapping of VTL and SDMX data structures ===
195	195
196		-This section makes reference to the VTL "Model for data and their structure"^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^21^^>>path:#sdfootnote21sym||name="sdfootnote21anc"]](%%)^^ and the correspondent SDMX "Data Structure Definition"^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^22^^>>path:#sdfootnote22sym||name="sdfootnote22anc"]](%%)^^.
	183	+This section makes reference to the VTL "Model for data and their structure"{{footnote}}See the VTL 2.0 User Manual{{/footnote}} and the correspondent SDMX "Data Structure Definition"{{footnote}}See the SDMX Standards Section 2 – Information Model{{/footnote}}.
197	197
198		-The main type of artefact that the VTL can manipulate is the VTL Data Set, which in general is mapped to the SDMX Dataflow. This means that a VTL Transformation, in the SDMX context, expresses the algorithm for calculating a derived Dataflow starting from some already existing Dataflows (either collected or derived).^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^23^^>>path:#sdfootnote23sym||name="sdfootnote23anc"]](%%)^^
	185	+The main type of artefact that the VTL can manipulate is the VTL Data Set, which in general is mapped to the SDMX Dataflow. This means that a VTL Transformation, in the SDMX context, expresses the algorithm for calculating a derived Dataflow starting from some already existing Dataflows (either collected or derived).{{footnote}}Besides the mapping between one SDMX Dataflow and one VTL Data Set, it is also possible to map distinct parts of a SDMX Dataflow to different VTL Data Set, as explained in a following paragraph.{{/footnote}}
199	199
200	200	While the VTL Transformations are defined in term of Dataflow definitions, they are assumed to be executed on instances of such Dataflows, provided at runtime to the VTL engine (the mechanism for identifying the instances to be processed are not part of the VTL specifications and depend on the implementation of the VTL-based systems). As already said, the SDMX Datasets are instances of SDMX Dataflows, therefore a VTL Transformation defined on some SDMX Dataflows can be applied on some corresponding SDMX Datasets.
201	201
...	...	@@ -211,70 +211,56 @@
211	211
212	212	=== 12.3.3 Mapping from SDMX to VTL data structures ===
213	213
214		-**12.3.3.1 Basic Mapping**
	201	+==== 12.3.3.1 Basic Mapping ====
215	215
216	216	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:
217	217
218		-|**SDMX**|**VTL**
219		-|Dimension|(Simple) Identifier
220		-|TimeDimension|(Time) Identifier
	205	+(% style="width:529.294px" %)
	206	+|(% style="width:151px" %)**SDMX**|(% style="width:375px" %)**VTL**
	207	+|(% style="width:151px" %)Dimension|(% style="width:375px" %)(Simple) Identifier
	208	+|(% style="width:151px" %)TimeDimension|(% style="width:375px" %)(Time) Identifier
	209	+|(% style="width:151px" %)Measure|(% style="width:375px" %)Measure
	210	+|(% style="width:151px" %)DataAttribute|(% style="width:375px" %)Attribute
221	221
222		-[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_59eee18f.gif||alt="Shape4" height="1" width="192"]]
223		-
224		-|Measure|Measure
225		-|DataAttribute|Attribute
226		-
227	227	The SDMX DataAttributes, in VTL they are all considered "at data point / observation level" (i.e. dependent on all the VTL Identifiers), because VTL does not have the SDMX AttributeRelationships, which defines the construct to which the DataAttribute is related (e.g. observation, dimension or set or group of dimensions, whole data set).
228	228
229		-With the Basic mapping, one SDMX observation^^27^^ generates one VTL data point.
	214	+With the Basic mapping, one SDMX observation{{footnote}}Here an SDMX observation is meant to correspond to one combination of values of the DimensionComponents.{{/footnote}} generates one VTL data point.
230	230
231		-**12.3.3.2 Pivot Mapping**
	216	+==== 12.3.3.2 Pivot Mapping ====
232	232
233	233	An alternative mapping method from SDMX to VTL is the **Pivot **mapping, which makes sense and is different from the Basic method only for the SDMX data structures that contain a Dimension that plays the role of measure dimension (like in SDMX 2.1) and just one Measure. Through this method, these structures can be mapped to multimeasure VTL data structures. Besides that, a user may choose to use any Dimension acting as a list of Measures (e.g., a Dimension with indicators), either by considering the “Measure” role of a Dimension, or at will using any coded Dimension. Of course, in SDMX 3.0, this can only work when only one Measure is defined in the DSD.
234	234
235		-In SDMX 2.1 the MeasureDimension was a subclass of DimensionComponent like Dimension and TimeDimension. In the current SDMX version, this subclass does not exist anymore, however a Dimension can have the role of measure dimension (i.e. a Dimension that contributes to the identification of the measures). In SDMX 2.1 a DataStructure could have zero or one MeasureDimensions, in the current version of the standard, from zero to many Dimension may have the role of measure dimension. Hereinafter a Dimension that plays the role of measure dimension is referenced for simplicity as “MeasureDimension“, i.e. maintaining the capital letters and the courier font even if the MeasureDimension is not anymore a class in the SDMX Information Model of the current SDMX version. For the sake of simplicity, the description below considers just one Dimension having the role of MeasureDimension (i.e., the more simple and common case). Nevertheless, it maintains its validity also if in the DataStructure there are more dimension with the role of MeasureDimensions: in this case what is said about the MeasureDimension must be applied to the combination of all the
	220	+In SDMX 2.1 the MeasureDimension was a subclass of DimensionComponent like Dimension and TimeDimension. In the current SDMX version, this subclass does not exist anymore, however a Dimension can have the role of measure dimension (i.e. a Dimension that contributes to the identification of the measures). In SDMX 2.1 a DataStructure could have zero or one MeasureDimensions, in the current version of the standard, from zero to many Dimension may have the role of measure dimension. Hereinafter a Dimension that plays the role of measure dimension is referenced for simplicity as “MeasureDimension“, i.e. maintaining the capital letters and the courier font even if the MeasureDimension is not anymore a class in the SDMX Information Model of the current SDMX version. For the sake of simplicity, the description below considers just one Dimension having the role of MeasureDimension (i.e., the more simple and common case). Nevertheless, it maintains its validity also if in the DataStructure there are more dimension with the role of MeasureDimensions: in this case what is said about the MeasureDimension must be applied to the combination of all the MeasureDimensions considered as a joint variable{{footnote}}E.g., if in the data structure there exist 3 Dimensions C,D,E having the role of MeasureDimension, they should be considered as a joint MeasureDimension Z=(C,D,E); therefore when the description says “each possible value Cj of the MeasureDimension …” it means “each possible combination of values (Cj, Dk, Ew) of the joint MeasureDimension Z=(C,D,E)”.{{/footnote}}.
236	236
237		-MeasureDimensions considered as a joint variable^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^24^^>>path:#sdfootnote24sym||name="sdfootnote24anc"]](%%)^^.
238		-
239	239	Among other things, the Pivot method provides also backward compatibility with the SDMX 2.1 data structures that contained a MeasureDimension.
240	240
241	241	If applied to SDMX structures that do not contain any MeasureDimension, this method behaves like the Basic mapping (see the previous paragraph).
242	242
243		-^^27^^ Here an SDMX observation is meant to correspond to one combination of values of the DimensionComponents.
	226	+Here an SDMX observation is meant to correspond to one combination of values of the DimensionComponents.
244	244
245	245	The SDMX structures that contain a MeasureDimension are mapped as described below (this mapping is equivalent to a pivoting operation):
246	246
247	247	* A SDMX simple dimension becomes a VTL (simple) identifier and a SDMX TimeDimension becomes a VTL (time) identifier;
248		-* 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
249		-
250		-Component;
251		-
	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;
252	252	* The SDMX MeasureDimension is not mapped to VTL (it disappears in the VTL Data Structure);
253	253	* The SDMX Measure is not mapped to VTL as well (it disappears in the VTL Data Structure);
254	254	* An SDMX DataAttribute is mapped in different ways according to its AttributeRelationship:
255		-** 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
256		-
257		-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;
258		-
259		-*
	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;
260	260	** 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).
261	261	** 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.
262	262
263	263	The summary mapping table of the "pivot" mapping from SDMX to VTL for the SDMX data structures that contain a MeasureDimension is the following:
264	264
265		-|**SDMX**|**VTL**
266		-|Dimension|(Simple) Identifier
267		-|TimeDimension|(Time) Identifier
268		-|MeasureDimension & one Measure|(((
269		-One Measure for each Code of the
270		-
271		-SDMX MeasureDimension
	241	+(% style="width:769.294px" %)
	242	+|(% style="width:401px" %)**SDMX**|(% style="width:366px" %)**VTL**
	243	+|(% style="width:401px" %)Dimension|(% style="width:366px" %)(Simple) Identifier
	244	+|(% style="width:401px" %)TimeDimension|(% style="width:366px" %)(Time) Identifier
	245	+|(% style="width:401px" %)MeasureDimension & one Measure|(% style="width:366px" %)(((
	246	+One Measure for each Code of the SDMX MeasureDimension
272	272	)))
273		-|DataAttribute not depending on the MeasureDimension|Attribute
274		-|DataAttribute depending on the MeasureDimension|(((
275		-One Attribute for each Code of the
276		-
277		-SDMX MeasureDimension
	248	+|(% style="width:401px" %)DataAttribute not depending on the MeasureDimension|(% style="width:366px" %)Attribute
	249	+|(% style="width:401px" %)DataAttribute depending on the MeasureDimension|(% style="width:366px" %)(((
	250	+One Attribute for each Code of the SDMX MeasureDimension
278	278	)))
279	279
280	280	Using this mapping method, the components of the data structure can change in the conversion from SDMX to VTL and it must be taken into account that the VTL statements can reference only the components of the resulting VTL data structure.
...	...	@@ -282,14 +282,11 @@
282	282	At observation / data point level, calling Cj (j=1, … n) the j^^th^^ Code of the MeasureDimension:
283	283
284	284	* 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;
285		-* 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)
286		-
287		-Identifiers, (time) Identifier and Attributes.
288		-
	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.
289	289	* 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
290	290	* 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
291	291
292		-**12.3.3.3 From SDMX DataAttributes to VTL Measures**
	262	+==== 12.3.3.3 From SDMX DataAttributes to VTL Measures ====
293	293
294	294	* In some cases, it may happen that the DataAttributes of the SDMX DataStructure need to be managed as Measures in VTL. Therefore, a variant of both the methods above consists in transforming all the SDMX DataAttributes in VTL Measures. When DataAttributes are converted to Measures, the two methods above are called Basic_A2M and Pivot_A2M (the suffix "A2M" stands for Attributes to Measures). Obviously, the resulting VTL data structure is, in general, multi-measure and does not contain
295	295
...	...	@@ -301,7 +301,7 @@
301	301
302	302	=== 12.3.4 Mapping from VTL to SDMX data structures ===
303	303
304		-**12.3.4.1 Basic Mapping**
	274	+==== 12.3.4.1 Basic Mapping ====
305	305
306	306	The main mapping method **from VTL to SDMX** is called **Basic **mapping as well.
307	307
...	...	@@ -311,11 +311,12 @@
311	311
312	312	Mapping table:
313	313
314		-|**VTL**|**SDMX**
315		-|(Simple) Identifier|Dimension
316		-|(Time) Identifier|TimeDimension
317		-|Measure|Measure
318		-|Attribute|DataAttribute
	284	+(% style="width:667.294px" %)
	285	+|(% style="width:272px" %)**VTL**|(% style="width:392px" %)**SDMX**
	286	+|(% style="width:272px" %)(Simple) Identifier|(% style="width:392px" %)Dimension
	287	+|(% style="width:272px" %)(Time) Identifier|(% style="width:392px" %)TimeDimension
	288	+|(% style="width:272px" %)Measure|(% style="width:392px" %)Measure
	289	+|(% style="width:272px" %)Attribute|(% style="width:392px" %)DataAttribute
319	319
320	320	If the distinction between simple identifier and time identifier is not maintained in the VTL environment, the classification between Dimension and TimeDimension exists only in SDMX, as declared in the relevant DataStructureDefinition.
321	321
...	...	@@ -325,7 +325,7 @@
325	325
326	326	As said, the resulting SDMX definitions must be compliant with the SDMX consistency rules. For example, the SDMX DSD must have the AttributeRelationship for the DataAttributes, which does not exist in VTL.
327	327
328		-**12.3.4.2 Unpivot Mapping**
	299	+==== 12.3.4.2 Unpivot Mapping ====
329	329
330	330	An alternative mapping method from VTL to SDMX is the **Unpivot **mapping.
331	331
...	...	@@ -349,11 +349,12 @@
349	349
350	350	The summary mapping table of the **unpivot** mapping method is the following:
351	351
352		-|**VTL**|**SDMX**
353		-|(Simple) Identifier|Dimension
354		-|(Time) Identifier|TimeDimension
355		-|All Measure Components|MeasureDimension (having one Code for each VTL measure component) & one Measure
356		-|Attribute|DataAttribute depending on all SDMX Dimensions including the TimeDimension and except the MeasureDimension
	323	+(% style="width:994.294px" %)
	324	+|(% style="width:306px" %)**VTL**|(% style="width:684px" %)**SDMX**
	325	+|(% style="width:306px" %)(Simple) Identifier|(% style="width:684px" %)Dimension
	326	+|(% style="width:306px" %)(Time) Identifier|(% style="width:684px" %)TimeDimension
	327	+|(% style="width:306px" %)All Measure Components|(% style="width:684px" %)MeasureDimension (having one Code for each VTL measure component) & one Measure
	328	+|(% style="width:306px" %)Attribute|(% style="width:684px" %)DataAttribute depending on all SDMX Dimensions including the TimeDimension and except the MeasureDimension
357	357
358	358	At observation / data point level:
359	359
...	...	@@ -367,7 +367,7 @@
367	367
368	368	In any case, the resulting SDMX definitions must be compliant with the SDMX consistency rules. For example, the possible Codes of the SDMX MeasureDimension need to be listed in a SDMX Codelist, with proper id, agency and version; moreover, the SDMX DSD must have the AttributeRelationship for the DataAttributes, which does not exist in VTL.
369	369
370		-**12.3.4.3 From VTL Measures to SDMX Data Attributes**
	342	+==== 12.3.4.3 From VTL Measures to SDMX Data Attributes ====
371	371
372	372	More than all for the multi-measure VTL structures (having more than one Measure Component), it may happen that the Measures of the VTL Data Structure need to be managed as DataAttributes in SDMX. Therefore, a third mapping method consists in transforming some VTL measures in a corresponding SDMX Measures and all the other VTL Measures in SDMX DataAttributes. This method is called M2A (“M2A” stands for “Measures to DataAttributes”).
373	373
...	...	@@ -375,12 +375,13 @@
375	375
376	376	The mapping table is the following:
377	377
378		-|VTL|SDMX
379		-|(Simple) Identifier|Dimension
380		-|(Time) Identifier|TimeDimension
381		-|Some Measures|Measure
382		-|Other Measures|DataAttribute
383		-|Attribute|DataAttribute
	350	+(% style="width:689.294px" %)
	351	+|(% style="width:344px" %)**VTL**|(% style="width:341px" %)**SDMX**
	352	+|(% style="width:344px" %)(Simple) Identifier|(% style="width:341px" %)Dimension
	353	+|(% style="width:344px" %)(Time) Identifier|(% style="width:341px" %)TimeDimension
	354	+|(% style="width:344px" %)Some Measures|(% style="width:341px" %)Measure
	355	+|(% style="width:344px" %)Other Measures|(% style="width:341px" %)DataAttribute
	356	+|(% style="width:344px" %)Attribute|(% style="width:341px" %)DataAttribute
384	384
385	385	Even in this case, the resulting SDMX definitions must be compliant with the SDMX consistency rules. For example, the SDMX DSD must have the attributeRelationship for the DataAttributes, which does not exist in VTL.
386	386
...	...	@@ -398,20 +398,20 @@
398	398
399	399	Until now it has been assumed to map one SMDX Dataflow to one VTL Data Set and vice-versa. This mapping one-to-one is not mandatory according to VTL because a VTL Data Set is meant to be a set of observations (data points) on a logical plane, having the same logical data structure and the same general meaning, independently of the possible physical representation or storage (see VTL 2.0 User Manual page 24), therefore a SDMX Dataflow can be seen either as a unique set of data observations (corresponding to one VTL Data Set) or as the union of many sets of data observations (each one corresponding to a distinct VTL Data Set).
400	400
401		-As a matter of fact, in some cases it can be useful to define VTL operations involving definite parts of a SDMX Dataflow instead than the whole.^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^25^^>>path:#sdfootnote25sym||name="sdfootnote25anc"]](%%)^^
	374	+As a matter of fact, in some cases it can be useful to define VTL operations involving definite parts of a SDMX Dataflow instead than the whole.{{footnote}}A typical example of this kind is the validation, and more in general the manipulation, of individual time series belonging to the same Dataflow, identifiable through the DimensionComponents of the Dataflow except the TimeDimension. The coding of these kind of operations might be simplified by mapping distinct time series (i.e. different parts of a SDMX Dataflow) to distinct VTL Data Sets.{{/footnote}}
402	402
403		-Therefore, in order to make the coding of VTL operations simpler when applied on parts of SDMX Dataflows, it is allowed to map distinct parts of a SDMX Dataflow to distinct VTL Data Sets according to the following rules and conventions. This kind of mapping is possible both from SDMX to VTL and from VTL to SDMX, as better explained below.^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^26^^>>path:#sdfootnote26sym||name="sdfootnote26anc"]](%%)^^
	376	+Therefore, in order to make the coding of VTL operations simpler when applied on parts of SDMX Dataflows, it is allowed to map distinct parts of a SDMX Dataflow to distinct VTL Data Sets according to the following rules and conventions. This kind of mapping is possible both from SDMX to VTL and from VTL to SDMX, as better explained below.{{footnote}}Please note that this kind of mapping is only an option at disposal of the definer of VTL Transformations; in fact it remains always possible to manipulate the needed parts of SDMX Dataflows by means of VTL operators (e.g. “sub”, “filter”, “calc”, “union” …), maintaining a mapping one-to-one between SDMX Dataflows and VTL Data Sets.{{/footnote}}
404	404
405	405	Given a SDMX Dataflow and some predefined Dimensions of its DataStructure, it is allowed to map the subsets of observations that have the same combination of values for such Dimensions to correspondent VTL datasets.
406	406
407	407	For example, assuming that the SDMX Dataflow DF1(1.0.0) has the Dimensions INDICATOR, TIME_PERIOD and COUNTRY, and that the user declares the Dimensions INDICATOR and COUNTRY as basis for the mapping (i.e. the mapping dimensions): the observations that have the same values for INDICATOR and COUNTRY would be mapped to the same VTL dataset (and vice-versa). In practice, this kind mapping is obtained like follows:
408	408
409		-* For a given SDMX Dataflow, the user (VTL definer) declares the DimensionComponents on which the mapping will be based, in a given order.^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^27^^>>path:#sdfootnote27sym||name="sdfootnote27anc"]](%%)^^ Following the example above, imagine that the user declares the Dimensions INDICATOR and COUNTRY.
	382	+* For a given SDMX Dataflow, the user (VTL definer) declares the DimensionComponents on which the mapping will be based, in a given order.{{footnote}}This definition is made through the ToVtlSubspace and ToVtlSpaceKey classes and/or the FromVtlSuperspace and FromVtlSpaceKey classes, depending on the direction of the mapping (“key” means “dimension”). The mapping of Dataflow subsets can be applied independently in the two directions, also according to different Dimensions. When no Dimension is declared for a given direction, it is assumed that the option of mapping different parts of a SDMX Dataflow to different VTL Data Sets is not used.{{/footnote}} Following the example above, imagine that the user declares the Dimensions INDICATOR and COUNTRY.
410	410	* The VTL Data Set is given a name using a special notation also called “ordered concatenation” and composed of the following parts:
411	411	** The reference to the SDMX Dataflow (expressed according to the rules described in the previous paragraphs, i.e. URN, abbreviated URN or another alias); for example DF(1.0.0);
412		-** a slash (“/”) as a separator; ^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^28^^>>path:#sdfootnote28sym||name="sdfootnote28anc"]](%%)^^
	385	+** a slash (“/”) as a separator;{{footnote}}As a consequence of this formalism, a slash in the name of the VTL Data Set assumes the specific meaning of separator between the name of the Dataflow and the values of some of its Dimensions.{{/footnote}}
413	413
414		-The reference to a specific part of the SDMX Dataflow above, expressed as the concatenation of the values that the SDMX DimensionComponents declared above must have, separated by dots (“.”) and written in the order in which these DimensionComponents are defined^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^29^^>>path:#sdfootnote29sym||name="sdfootnote29anc"]](%%)^^. For example POPULATION.USA would mean that such a VTL Data Set is mapped to the SDMX observations for which the dimension //INDICATOR// is equal to POPULATION and the dimension //COUNTRY// is equal to USA.
	387	+The reference to a specific part of the SDMX Dataflow above, expressed as the concatenation of the values that the SDMX DimensionComponents declared above must have, separated by dots (“.”) and written in the order in which these DimensionComponents are defined{{footnote}}This is the order in which the dimensions are defined in the ToVtlSpaceKey class or in the FromVtlSpaceKey class, depending on the direction of the mapping.{{/footnote}}. For example POPULATION.USA would mean that such a VTL Data Set is mapped to the SDMX observations for which the dimension //INDICATOR// is equal to POPULATION and the dimension //COUNTRY// is equal to USA.
415	415
416	416	In the VTL Transformations, this kind of dataset name must be referenced between single quotes because the slash (“/”) is not a regular character according to the VTL rules.
417	417
...	...	@@ -427,7 +427,7 @@
427	427
428	428	Let us now analyse the different meaning of this kind of mapping in the two mapping directions, i.e. from SDMX to VTL and from VTL to SDMX.
429	429
430		-As already said, the mapping from SDMX to VTL happens when the SDMX dataflows are operand of VTL Transformations, instead the mapping from VTL to SDMX happens when the VTL Data Sets that is result of Transformations^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^30^^>>path:#sdfootnote30sym||name="sdfootnote30anc"]](%%)^^ need to be treated as SDMX objects. This kind of mapping can be applied independently in the two directions and the Dimensions on which the mapping is based can be different in the two directions: these Dimensions are defined in the ToVtlSpaceKey and in the FromVtlSpaceKey classes respectively.
	403	+As already said, the mapping from SDMX to VTL happens when the SDMX dataflows are operand of VTL Transformations, instead the mapping from VTL to SDMX happens when the VTL Data Sets that is result of Transformations{{footnote}}It should be remembered that, according to the VTL consistency rules, a given VTL dataset cannot be the result of more than one VTL Transformation.{{/footnote}} need to be treated as SDMX objects. This kind of mapping can be applied independently in the two directions and the Dimensions on which the mapping is based can be different in the two directions: these Dimensions are defined in the ToVtlSpaceKey and in the FromVtlSpaceKey classes respectively.
431	431
432	432	First, let us see what happens in the __mapping direction from SDMX to VTL__, i.e. when parts of a SDMX Dataflow (e.g. DF1(1.0.0)) need to be mapped to distinct VTL Data Sets that are operand of some VTL Transformations.
433	433
...	...	@@ -435,27 +435,21 @@
435	435
436	436	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.
437	437
438		-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^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^31^^>>path:#sdfootnote31sym||name="sdfootnote31anc"]](%%)^^. 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 …).
439	439
440		-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.
441	441
442		-In the example above, for all the datasets of the kind
443		-
444		-‘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.
445		-
446	446	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:
447	447
448	448	‘DF1(1.0.0)/POPULATION.USA’ :=
449		-
450	450	DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA” ];
451	451
452	452	‘DF1(1.0.0)/POPULATION.CANADA’ :=
453		-
454	454	DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
455	455
456	456	… … …
457	457
458		-In fact the VTL operator “sub” has exactly the same behaviour. Therefore, mapping different parts of a SDMX Dataflow to different VTL Data Sets in the direction from SDMX to VTL through the ordered concatenation notation is equivalent to a proper use of the operator “**sub**” on such a Dataflow. ^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^32^^>>path:#sdfootnote32sym||name="sdfootnote32anc"]](%%)^^
	425	+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}}
459	459
460	460	In the direction from SDMX to VTL it is allowed to omit the value of one or more
461	461
...	...	@@ -466,7 +466,6 @@
466	466	This is equivalent to the application of the VTL “sub” operator only to the identifier //INDICATOR//:
467	467
468	468	‘DF1(1.0.0)/POPULATION.’ :=
469		-
470	470	DF1(1.0.0) [ sub INDICATOR=“POPULATION” ];
471	471
472	472	Therefore the VTL Data Set ‘DF1(1.0.0)/POPULATION.’ would have the identifiers COUNTRY and TIME_PERIOD.
...	...	@@ -483,12 +483,12 @@
483	483
484	484	Dataflow DF2(1.0.0) having the Dimensions TIME_PERIOD, INDICATOR, and COUNTRY and that such a programmer finds it convenient to calculate separately the parts of DF2(1.0.0) that have different combinations of values for INDICATOR and COUNTRY:
485	485
486		-* each part is calculated as a VTL derived Data Set, result of a dedicated VTL Transformation; ^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^33^^>>path:#sdfootnote33sym||name="sdfootnote33anc"]](%%)^^
487		-* the data structure of all these VTL Data Sets has the TIME_PERIOD identifier and does not have the INDICATOR and COUNTRY identifiers.^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^34^^>>path:#sdfootnote34sym||name="sdfootnote34anc"]](%%)^^
	452	+* each part is calculated as a VTL derived Data Set, result of a dedicated VTL Transformation;{{footnote}}If the whole DF2(1.0) is calculated by means of just one VTL Transformation, then the mapping between the SDMX Dataflow and the corresponding VTL dataset is one-to-one and this kind of mapping (one SDMX Dataflow to many VTL datasets) does not apply.{{/footnote}}
	453	+* the data structure of all these VTL Data Sets has the TIME_PERIOD identifier and does not have the INDICATOR and COUNTRY identifiers.{{footnote}}This is possible as each VTL dataset corresponds to one particular combination of values of INDICATOR and COUNTRY.{{/footnote}}
488	488
489		-Under these hypothesis, such derived VTL Data Sets can be mapped to DF2(1.0.0) by declaring the DimensionComponents INDICATOR and COUNTRY as mapping dimensions^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^35^^>>path:#sdfootnote35sym||name="sdfootnote35anc"]](%%)^^.
	455	+Under these hypothesis, such derived VTL Data Sets can be mapped to DF2(1.0.0) by declaring the DimensionComponents INDICATOR and COUNTRY as mapping dimensions{{footnote}}The mapping dimensions are defined as FromVtlSpaceKeys of the FromVtlSuperSpace of the VtlDataflowMapping relevant to DF2(1.0).{{/footnote}}.
490	490
491		-The corresponding VTL Transformations, assuming that the result needs to be persistent, would be of this kind:^^ [[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^36^^>>path:#sdfootnote36sym||name="sdfootnote36anc"]](%%)^^
	457	+The corresponding VTL Transformations, assuming that the result needs to be persistent, would be of this kind:{{footnote}}the symbol of the VTL persistent assignment is used (<-){{/footnote}}
492	492
493	493	‘DF2(1.0.0)/INDICATORvalue.COUNTRYvalue’ <- expression
494	494
...	...	@@ -497,11 +497,8 @@
497	497	‘DF2(1.0.0)/GDPPERCAPITA.USA’ <- expression11; ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ <- expression12;
498	498
499	499	… … …
500		-
501	501	‘DF2(1.0.0)/POPGROWTH.USA’ <- expression21;
502		-
503	503	‘DF2(1.0.0)/POPGROWTH.CANADA’ <- expression22;
504		-
505	505	… … …
506	506
507	507	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:
...	...	@@ -544,9 +544,9 @@
544	544
545	545	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
546	546
547		-DF2bis_GDPPERCAPITA_USA and so on) by adding the identifiers INDICATOR and COUNTRY with the desired values (//INDICATORvalue// and //COUNTRYvalue)//. Finally, all these non-persistent Data Sets are united and give the final result DF2(1.0)^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^37^^>>path:#sdfootnote37sym||name="sdfootnote37anc"]](%%)^^, which can be mapped one-to-one to the homonymous SDMX Dataflow having the dimension components TIME_PERIOD, INDICATOR and COUNTRY.
	510	+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.
548	548
549		-Therefore, mapping different VTL datasets having the same data structure to different parts of a SDMX Dataflow, i.e. in the direction from VTL to SDMX, through the ordered concatenation notation is equivalent to a proper use of the operators “calc” and “union” on such datasets. ^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^38^^>>path:#sdfootnote38sym||name="sdfootnote38anc"]](%%)[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^39^^>>path:#sdfootnote39sym||name="sdfootnote39anc"]](%%)^^
	512	+Therefore, mapping different VTL datasets having the same data structure to different parts of a SDMX Dataflow, i.e. in the direction from VTL to SDMX, through the ordered concatenation notation is equivalent to a proper use of the operators “calc” and “union” on such datasets.{{footnote}}In case the ordered concatenation notation from VTL to SDMX is used, the set of Transformations described above is implicitly performed; therefore, in order to test the overall compliance of the VTL program to the VTL consistency rules, these implicit Transformations have to be considered as part of the VTL program even if they are not explicitly coded.{{/footnote}}
550	550
551	551	It is worth noting that in the direction from VTL to SDMX it is mandatory to specify the value for every Dimension on which the mapping is based (in other word, in the name of the calculated VTL dataset is __not__ possible to omit the value of some of the Dimensions).
552	552
...	...	@@ -554,52 +554,51 @@
554	554
555	555	With reference to the VTL “model for Variables and Value domains”, the following additional mappings have to be considered:
556	556
557		-|VTL|SDMX
558		-|**Data Set Component**|Although this abstraction exists in SDMX, it does not have an explicit definition and correspond to a Component (either a DimensionComponent or a Measure or a DataAttribute) belonging to one specific Dataflow^^43^^
559		-|**Represented Variable**|(((
	520	+(% style="width:1170.29px" %)
	521	+|**VTL**|(% style="width:754px" %)**SDMX**
	522	+|**Data Set Component**|(% style="width:754px" %)Although this abstraction exists in SDMX, it does not have an explicit definition and correspond to a Component (either a DimensionComponent or a Measure or a DataAttribute) belonging to one specific Dataflow{{footnote}}Through SDMX Constraints, it is possible to specify the values that a Component of a Dataflow can assume.{{/footnote}}
	523	+|**Represented Variable**|(% style="width:754px" %)(((
560	560	**Concept** with a definite
561	561
562	562	Representation
563	563	)))
564		-|**Value Domain**|(((
	528	+|**Value Domain**|(% style="width:754px" %)(((
565	565	**Representation** (see the Structure
566	566
567	567	Pattern in the Base Package)
568	568	)))
569		-|**Enumerated Value Domain / Code List**|**Codelist**
570		-|**Code**|(((
	533	+|**Enumerated Value Domain / Code List**|(% style="width:754px" %)**Codelist**
	534	+|**Code**|(% style="width:754px" %)(((
571	571	**Code** (for enumerated
572	572
573	573	DimensionComponent, Measure, DataAttribute)
574	574	)))
575		-|**Described Value Domain**|(((
576		-non-enumerated** &nbsp;&nbsp;&nbsp;Representation**
	539	+|**Described Value Domain**|(% style="width:754px" %)(((
	540	+non-enumerated** Representation**
577	577
578	578	(having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
579	579	)))
580		-|**Value**|Although this abstraction exists in SDMX, it does not have an explicit definition and correspond to a **Code** of a Codelist (for enumerated Representations) or
581		-| |(((
582		-to a valid **value &nbsp;&nbsp;&nbsp;**(for non-enumerated** &nbsp;&nbsp;&nbsp;**
583		-
584		-Representations)
	544	+|**Value**|(% style="width:754px" %)Although this abstraction exists in SDMX, it does not have an explicit definition and correspond to a **Code** of a Codelist (for enumerated Representations) or
	545	+| |(% style="width:754px" %)(((
	546	+to a valid **value **(for non-enumerated** **Representations)
585	585	)))
586		-|**Value Domain Subset / Set**|This abstraction does not exist in SDMX
587		-|**Enumerated Value Domain Subset / Enumerated Set**|This abstraction does not exist in SDMX
588		-|**Described Value Domain Subset / Described Set**|This abstraction does not exist in SDMX
589		-|**Set list**|This abstraction does not exist in SDMX
	548	+|**Value Domain Subset / Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
	549	+|**Enumerated Value Domain Subset / Enumerated Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
	550	+|**Described Value Domain Subset / Described Set**|(% style="width:754px" %)This abstraction does not exist in SDMX
	551	+|**Set list**|(% style="width:754px" %)This abstraction does not exist in SDMX
590	590
591	591	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).
592	592
593		-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" %)^^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" %)^^41^^>>path:#sdfootnote41sym||name="sdfootnote41anc"]](%%)^^ This means that one SDMX Concept can correspond to many VTL Variables, one for each representation the Concept has.
	555	+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.
594	594
595	595	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
596	596
597		-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.
	559	+DS_c := DS_a + DS_b (where DS_a, DS_b, DS_c are VTL Data Sets)
598	598
	561	+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.
	562	+
599	599	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
600	600
601		-[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_59eee18f.gif||alt="Shape5" height="1" width="192"]]
602		-
603	603	Transformations to ensure that the VTL expressions are consistent with the actual representations of the correspondent SDMX Concepts.
604	604
605	605	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.
...	...	@@ -614,7 +614,8 @@
614	614
615	615	[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_e3df33ae.png||height="543" width="483"]]
616	616
617		-==== Figure 22 – VTL Data Types ====
	579	+(% class="wikigeneratedid" id="HFigure222013VTLDataTypes" %)
	580	+**Figure 22 – VTL Data Types**
618	618
619	619	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.
620	620
...	...	@@ -621,131 +621,12 @@
621	621	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):
622	622
623	623
	587	+**Figure 23 – VTL Basic Scalar Types**
624	624
625	625	(((
626		-//n//
627		-
628		-//a//
629		-
630		-//e//
631		-
632		-//l//
633		-
634		-//o//
635		-
636		-//o//
637		-
638		-//B//
639		-
640		-//n//
641		-
642		-//o//
643		-
644		-//i//
645		-
646		-//t//
647		-
648		-//a//
649		-
650		-//r//
651		-
652		-//u//
653		-
654		-//D//
655		-
656		-//d//
657		-
658		-//o//
659		-
660		-//i//
661		-
662		-//r//
663		-
664		-//e//
665		-
666		-//p//
667		-
668		-//_//
669		-
670		-//e//
671		-
672		-//m//
673		-
674		-//i//
675		-
676		-//T//
677		-
678		-//e//
679		-
680		-//t//
681		-
682		-//a//
683		-
684		-//D//
685		-
686		-//e//
687		-
688		-//m//
689		-
690		-//i//
691		-
692		-//T//
693		-
694		-//r//
695		-
696		-//e//
697		-
698		-//g//
699		-
700		-//e//
701		-
702		-//t//
703		-
704		-//n//
705		-
706		-//I//
707		-
708		-//r//
709		-
710		-//e//
711		-
712		-//b//
713		-
714		-//m//
715		-
716		-//u//
717		-
718		-//N//
719		-
720		-//g//
721		-
722		-//n//
723		-
724		-//i//
725		-
726		-//r//
727		-
728		-//t//
729		-
730		-//S//
731		-
732		-//r//
733		-
734		-//a//
735		-
736		-//l//
737		-
738		-//a//
739		-
740		-//c//
741		-
742		-//S//
743		-
744		-[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_82d45833.gif||alt="Shape6" height="231" width="184"]]
	590	+
745	745	)))
746	746
747		-==== Figure 23 – VTL Basic Scalar Types ====
748		-
749	749	=== 12.4.2 VTL basic scalar types and SDMX data types ===
750	750
751	751	The VTL assumes that a basic scalar type has a unique internal representation and can have more external representations.
...	...	@@ -768,204 +768,159 @@
768	768
769	769	The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types.
770	770
771		-|SDMX data type (BasicComponentDataType)|Default VTL basic scalar type
772		-|(((
	615	+(% style="width:823.294px" %)
	616	+|(% style="width:509px" %)**SDMX data type (BasicComponentDataType)**|(% style="width:312px" %)**Default VTL basic scalar type**
	617	+|(% style="width:509px" %)(((
773	773	String
774		-
775	775	(string allowing any character)
776		-)))|string
777		-|(((
	620	+)))|(% style="width:312px" %)string
	621	+|(% style="width:509px" %)(((
778	778	Alpha
779		-
780	780	(string which only allows A-z)
781		-)))|string
782		-|(((
	624	+)))|(% style="width:312px" %)string
	625	+|(% style="width:509px" %)(((
783	783	AlphaNumeric
784		-
785	785	(string which only allows A-z and 0-9)
786		-)))|string
787		-|(((
	628	+)))|(% style="width:312px" %)string
	629	+|(% style="width:509px" %)(((
788	788	Numeric
789		-
790	790	(string which only allows 0-9, but is not numeric so that is can having leading zeros)
791		-)))|string
792		-|(((
	632	+)))|(% style="width:312px" %)string
	633	+|(% style="width:509px" %)(((
793	793	BigInteger
794		-
795	795	(corresponds to XML Schema xs:integer datatype; infinite set of integer values)
796		-)))|integer
797		-|(((
	636	+)))|(% style="width:312px" %)integer
	637	+|(% style="width:509px" %)(((
798	798	Integer
799		-
800		-(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647
801		-
802		-(inclusive))
803		-)))|integer
804		-|(((
	639	+(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647 (inclusive))
	640	+)))|(% style="width:312px" %)integer
	641	+|(% style="width:509px" %)(((
805	805	Long
806		-
807		-(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and
808		-
809		-+9223372036854775807 (inclusive))
810		-)))|integer
811		-|(((
	643	+(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and +9223372036854775807 (inclusive))
	644	+)))|(% style="width:312px" %)integer
	645	+|(% style="width:509px" %)(((
812	812	Short
813		-
814	814	(corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive))
815		-)))|integer
816		-|Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|number
817		-|(((
	648	+)))|(% style="width:312px" %)integer
	649	+|(% 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
	650	+|(% style="width:509px" %)(((
818	818	Float
819		-
820	820	(corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type)
821		-)))|number
822		-|(((
	653	+)))|(% style="width:312px" %)number
	654	+|(% style="width:509px" %)(((
823	823	Double
824		-
825	825	(corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type)
826		-)))|number
827		-|(((
	657	+)))|(% style="width:312px" %)number
	658	+|(% style="width:509px" %)(((
828	828	Boolean
	660	+(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of binary-valued logic: {true, false})
	661	+)))|(% style="width:312px" %)boolean
829	829
830		-(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of
831		-
832		-binary-valued logic: {true, false})
833		-)))|boolean
834		-
835		-| |(% colspan="2" %)(((
	663	+(% style="width:822.294px" %)
	664	+|(% colspan="2" style="width:507px" %)(((
836	836	URI
837		-
838	838	(corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference)
839		-)))|(% colspan="2" %)string
840		-| |(% colspan="2" %)(((
	667	+)))|(% colspan="1" style="width:311px" %)string
	668	+|(% colspan="2" style="width:507px" %)(((
841	841	Count
842		-
843	843	(an integer following a sequential pattern, increasing by 1 for each occurrence)
844		-)))|(% colspan="2" %)integer
845		-| |(% colspan="2" %)(((
	671	+)))|(% colspan="1" style="width:311px" %)integer
	672	+|(% colspan="2" style="width:507px" %)(((
846	846	InclusiveValueRange
847		-
848	848	(decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
849		-)))|(% colspan="2" %)number
850		-| |(% colspan="2" %)(((
	675	+)))|(% colspan="1" style="width:311px" %)number
	676	+|(% colspan="2" style="width:507px" %)(((
851	851	ExclusiveValueRange
852		-
853	853	(decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
854		-)))|(% colspan="2" %)number
855		-| |(% colspan="2" %)(((
	679	+)))|(% colspan="1" style="width:311px" %)number
	680	+|(% colspan="2" style="width:507px" %)(((
856	856	Incremental
857		-
858	858	(decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation)
859		-)))|(% colspan="2" %)number
860		-| |(% colspan="2" %)(((
	683	+)))|(% colspan="1" style="width:311px" %)number
	684	+|(% colspan="2" style="width:507px" %)(((
861	861	ObservationalTimePeriod
862		-
863	863	(superset of StandardTimePeriod and TimeRange)
864		-)))|(% colspan="2" %)time
865		-| |(% colspan="2" %)(((
	687	+)))|(% colspan="1" style="width:311px" %)time
	688	+|(% colspan="2" style="width:507px" %)(((
866	866	StandardTimePeriod
867		-
868		-(superset of BasicTimePeriod and
869		-
870		-ReportingTimePeriod)
871		-)))|(% colspan="2" %)time
872		-| |(% colspan="2" %)(((
	690	+(superset of BasicTimePeriod and ReportingTimePeriod)
	691	+)))|(% colspan="1" style="width:311px" %)time
	692	+|(% colspan="2" style="width:507px" %)(((
873	873	BasicTimePeriod
874		-
875	875	(superset of GregorianTimePeriod and DateTime)
876		-)))|(% colspan="2" %)date
877		-| |(% colspan="2" %)(((
	695	+)))|(% colspan="1" style="width:311px" %)date
	696	+|(% colspan="2" style="width:507px" %)(((
878	878	GregorianTimePeriod
879		-
880	880	(superset of GregorianYear, GregorianYearMonth, and GregorianDay)
881		-)))|(% colspan="2" %)date
882		-| |(% colspan="2" %)GregorianYear (YYYY)|(% colspan="2" %)date
883		-| |(% colspan="2" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="2" %)date
884		-| |(% colspan="2" %)GregorianDay (YYYY-MM-DD)|(% colspan="2" %)date
885		-| |(% colspan="2" %)(((
	699	+)))|(% colspan="1" style="width:311px" %)date
	700	+|(% colspan="2" style="width:507px" %)GregorianYear (YYYY)|(% colspan="1" style="width:311px" %)date
	701	+|(% colspan="2" style="width:507px" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="1" style="width:311px" %)date
	702	+|(% colspan="2" style="width:507px" %)GregorianDay (YYYY-MM-DD)|(% colspan="1" style="width:311px" %)date
	703	+|(% colspan="2" style="width:507px" %)(((
886	886	ReportingTimePeriod
887		-
888		-(superset of RepostingYear, ReportingSemester,
889		-
890		-ReportingTrimester, ReportingQuarter,
891		-
892		-ReportingMonth, ReportingWeek, ReportingDay)
893		-)))|(% colspan="2" %)time_period
894		-| |(% colspan="2" %)(((
	705	+(superset of RepostingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, ReportingDay)
	706	+)))|(% colspan="1" style="width:311px" %)time_period
	707	+|(% colspan="2" style="width:507px" %)(((
895	895	ReportingYear
896		-
897	897	(YYYY-A1 – 1 year period)
898		-)))|(% colspan="2" %)time_period
899		-| |(% colspan="2" %)(((
	710	+)))|(% colspan="1" style="width:311px" %)time_period
	711	+|(% colspan="2" style="width:507px" %)(((
900	900	ReportingSemester
901		-
902	902	(YYYY-Ss – 6 month period)
903		-)))|(% colspan="2" %)time_period
904		-| |(% colspan="2" %)(((
	714	+)))|(% colspan="1" style="width:311px" %)time_period
	715	+|(% colspan="2" style="width:507px" %)(((
905	905	ReportingTrimester
906		-
907	907	(YYYY-Tt – 4 month period)
908		-)))|(% colspan="2" %)time_period
909		-| |(% colspan="2" %)(((
	718	+)))|(% colspan="1" style="width:311px" %)time_period
	719	+|(% colspan="2" style="width:507px" %)(((
910	910	ReportingQuarter
911		-
912	912	(YYYY-Qq – 3 month period)
913		-)))|(% colspan="2" %)time_period
914		-| |(% colspan="2" %)(((
	722	+)))|(% colspan="1" style="width:311px" %)time_period
	723	+|(% colspan="2" style="width:507px" %)(((
915	915	ReportingMonth
916		-
917	917	(YYYY-Mmm – 1 month period)
918		-)))|(% colspan="2" %)time_period
919		-| |(% colspan="2" %)ReportingWeek|(% colspan="2" %)time_period
920		-| |(% colspan="2" %) |(% colspan="2" %)
921		-| |(% colspan="2" %) |(% colspan="2" %)
922		-|(% colspan="2" %)(YYYY-Www – 7 day period; following ISO 8601 definition of a week in a year)|(% colspan="2" %) |
923		-|(% colspan="2" %)(((
	726	+)))|(% colspan="1" style="width:311px" %)time_period
	727	+|(% colspan="2" style="width:507px" %)ReportingWeek|(% colspan="1" style="width:311px" %)time_period
	728	+|(% 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" %)
	729	+|(% colspan="1" style="width:507px" %)(((
924	924	ReportingDay
925		-
926	926	(YYYY-Dddd – 1 day period)
927		-)))|(% colspan="2" %)time_period|
928		-|(% colspan="2" %)(((
	732	+)))|(% colspan="2" style="width:312px" %)time_period
	733	+|(% colspan="1" style="width:507px" %)(((
929	929	DateTime
930		-
931	931	(YYYY-MM-DDThh:mm:ss)
932		-)))|(% colspan="2" %)date|
933		-|(% colspan="2" %)(((
	736	+)))|(% colspan="2" style="width:312px" %)date
	737	+|(% colspan="1" style="width:507px" %)(((
934	934	TimeRange
935		-
936	936	(YYYY-MM-DD(Thh:mm:ss)?/<duration>)
937		-)))|(% colspan="2" %)time|
938		-|(% colspan="2" %)(((
	740	+)))|(% colspan="2" style="width:312px" %)time
	741	+|(% colspan="1" style="width:507px" %)(((
939	939	Month
940		-
941	941	(~-~-MM; speicifies a month independent of a year; e.g. February is black history month in the United States)
942		-)))|(% colspan="2" %)string|
943		-|(% colspan="2" %)(((
	744	+)))|(% colspan="2" style="width:312px" %)string
	745	+|(% colspan="1" style="width:507px" %)(((
944	944	MonthDay
945		-
946	946	(~-~-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)
947		-)))|(% colspan="2" %)string|
948		-|(% colspan="2" %)(((
	748	+)))|(% colspan="2" style="width:312px" %)string
	749	+|(% colspan="1" style="width:507px" %)(((
949	949	Day
950		-
951	951	(~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday)
952		-)))|(% colspan="2" %)string|
953		-|(% colspan="2" %)(((
	752	+)))|(% colspan="2" style="width:312px" %)string
	753	+|(% colspan="1" style="width:507px" %)(((
954	954	Time
955		-
956	956	(hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM)
957		-)))|(% colspan="2" %)string|
958		-|(% colspan="2" %)(((
	756	+)))|(% colspan="2" style="width:312px" %)string
	757	+|(% colspan="1" style="width:507px" %)(((
959	959	Duration
960		-
961	961	(corresponds to XML Schema xs:duration datatype)
962		-)))|(% colspan="2" %)duration|
963		-|(% colspan="2" %)XHTML|(% colspan="2" %)Metadata type – not applicable|
964		-|(% colspan="2" %)KeyValues|(% colspan="2" %)Metadata type – not applicable|
965		-|(% colspan="2" %)IdentifiableReference|(% colspan="2" %)Metadata type – not applicable|
966		-|(% colspan="2" %)DataSetReference|(% colspan="2" %)Metadata type – not applicable|
	760	+)))|(% colspan="2" style="width:312px" %)duration
	761	+|(% colspan="1" style="width:507px" %)XHTML|(% colspan="2" style="width:312px" %)Metadata type – not applicable
	762	+|(% colspan="1" style="width:507px" %)KeyValues|(% colspan="2" style="width:312px" %)Metadata type – not applicable
	763	+|(% colspan="1" style="width:507px" %)IdentifiableReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
	764	+|(% colspan="1" style="width:507px" %)DataSetReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
967	967
968		-==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
	766	+(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes" %)
	767	+**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
969	969
970	970	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).
971	971
...	...	@@ -973,39 +973,32 @@
973	973
974	974	The following table describes the default conversion from the VTL basic scalar types to the SDMX data types .
975	975
976		-|(((
977		-VTL basic
978		-
979		-scalar type
980		-)))|(((
981		-Default SDMX data type
982		-
983		-(BasicComponentDataType
984		-
985		-)
986		-)))|Default output format
987		-|String|String|Like XML (xs:string)
988		-|Number|Float|Like XML (xs:float)
989		-|Integer|Integer|Like XML (xs:int)
990		-|Date|DateTime|YYYY-MM-DDT00:00:00Z
991		-|Time|StandardTimePeriod|<date>/<date> (as defined above)
992		-|time_period|(((
	775	+(% style="width:1073.29px" %)
	776	+|(% style="width:207px" %)(((
	777	+**VTL basic scalar type**
	778	+)))|(% style="width:462px" %)(((
	779	+**Default SDMX data type (BasicComponentDataType)**
	780	+)))|(% style="width:402px" %)**Default output format**
	781	+|(% style="width:207px" %)String|(% style="width:462px" %)String|(% style="width:402px" %)Like XML (xs:string)
	782	+|(% style="width:207px" %)Number|(% style="width:462px" %)Float|(% style="width:402px" %)Like XML (xs:float)
	783	+|(% style="width:207px" %)Integer|(% style="width:462px" %)Integer|(% style="width:402px" %)Like XML (xs:int)
	784	+|(% style="width:207px" %)Date|(% style="width:462px" %)DateTime|(% style="width:402px" %)YYYY-MM-DDT00:00:00Z
	785	+|(% style="width:207px" %)Time|(% style="width:462px" %)StandardTimePeriod|(% style="width:402px" %)<date>/<date> (as defined above)
	786	+|(% style="width:207px" %)time_period|(% style="width:462px" %)(((
993	993	ReportingTimePeriod
994		-
995	995	(StandardReportingPeriod)
996		-)))|(((
	789	+)))|(% style="width:402px" %)(((
997	997	YYYY-Pppp
998		-
999	999	(according to SDMX )
1000	1000	)))
1001		-|Duration|Duration|(((
	793	+|(% style="width:207px" %)Duration|(% style="width:462px" %)Duration|(% style="width:402px" %)(((
1002	1002	Like XML (xs:duration)
1003		-
1004	1004	PnYnMnDTnHnMnS
1005	1005	)))
1006		-|Boolean|Boolean|Like XML (xs:boolean) with the values "true" or "false"
	797	+|(% style="width:207px" %)Boolean|(% style="width:462px" %)Boolean|(% style="width:402px" %)Like XML (xs:boolean) with the values "true" or "false"
1007	1007
1008		-==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
	799	+(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes-1" %)
	800	+**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
1009	1009
1010	1010	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).
1011	1011
...	...	@@ -1059,7 +1059,7 @@
1059	1059	|N|fixed number of digits used in the preceding textual representation of the month or the day
1060	1060	| |
1061	1061
1062		-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" %)^^42^^>>path:#sdfootnote42sym||name="sdfootnote42anc"]](%%)^^.
	854	+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}}.
1063	1063
1064	1064	=== 12.4.5 Null Values ===
1065	1065
...	...	@@ -1077,10 +1077,8 @@
1077	1077
1078	1078	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).
1079	1079
1080		-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
	872	+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.
1081	1081
1082		-TransformationScheme.
1083		-
1084	1084	In case a literal is operand of a VTL Cast operation, the format specified in the Cast overrides all the possible otherwise specified formats.
1085	1085
1086	1086	{{putFootnotes/}}