Changes for page 12 Validation and Transformation Language (VTL)
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... ... @@ -14,10 +14,8 @@ 14 14 15 15 The VTL language can be applied to SDMX artefacts by mapping the SDMX IM model artefacts to the model artefacts that VTL can manipulate{{footnote}}In this chapter, in order to distinguish VTL and SDMX model artefacts, the VTL ones are written in the Arial font while the SDMX ones in Courier New{{/footnote}}. Thus, the SDMX artefacts can be used in VTL as inputs and/or outputs of Transformations. It is important to be aware that the artefacts do not always have the same names in the SDMX and VTL IMs, nor do they always have the same meaning. The more evident example is given by the SDMX Dataset and the VTL "Data Set", which do not correspond one another: as a matter of fact, the VTL "Data Set" maps to the SDMX "Dataflow", while the SDMX "Dataset" has no explicit mapping to VTL (such an abstraction is not needed in the definition of VTL Transformations). A SDMX "Dataset", however, is an instance of a SDMX "Dataflow" and can be the artefact on which the VTL transformations are executed (i.e., the Transformations are defined on Dataflows and are applied to Dataflow instances that can be Datasets). 16 16 17 -The VTL programs (Transformation Schemes) are represented in SDMX through the TransformationScheme maintainable class which is composed of 17 +The VTL programs (Transformation Schemes) are represented in SDMX through the TransformationScheme maintainable class which is composed of Transformation (nameable artefact). Each Transformation assigns the outcome of the evaluation of a VTL expression to a result. 18 18 19 -Transformation (nameable artefact). Each Transformation assigns the outcome of the evaluation of a VTL expression to a result. 20 - 21 21 This section does not explain the VTL language or any of the content published in the VTL guides. Rather, this is a description of how the VTL can be used in the SDMX context and applied to SDMX artefacts. 22 22 23 23 == 12.2 References to SDMX artefacts from VTL statements == ... ... @@ -28,10 +28,8 @@ 28 28 29 29 The alias of an SDMX artefact can be its URN (Universal Resource Name), an abbreviation of its URN or another user-defined name. 30 30 31 -In any case, the aliases used in the VTL Transformations have to be mapped to the 29 +In any case, the aliases used in the VTL Transformations have to be mapped to the SDMX artefacts through the VtlMappingScheme and VtlMapping classes (see the section of the SDMX IM relevant to the VTL). A VtlMapping allows specifying the aliases to be used in the VTL Transformations, Rulesets{{footnote}}See also the section "VTL-DL Rulesets" in the VTL Reference Manual.{{/footnote}} or User Defined Operators{{footnote}}The VTLMappings are used also for User Defined Operators (UDO). Although UDOs are envisaged to be defined on generic operands, so that the specific artefacts to be manipulated are passed as parameters at their invocation, it is also possible that an UDO invokes directly some specific SDMX artefacts. These SDMX artefacts have to be mapped to the corresponding aliases used in the definition of the UDO through the VtlMappingScheme and VtlMapping classes as well.{{/footnote}} to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping. 32 32 33 -SDMX artefacts through the VtlMappingScheme and VtlMapping classes (see the section of the SDMX IM relevant to the VTL). A VtlMapping allows specifying the aliases to be used in the VTL Transformations, Rulesets{{footnote}}See also the section "VTL-DL Rulesets" in the VTL Reference Manual.{{/footnote}} or User Defined Operators{{footnote}}The VTLMappings are used also for User Defined Operators (UDO). Although UDOs are envisaged to be defined on generic operands, so that the specific artefacts to be manipulated are passed as parameters at their invocation, it is also possible that an UDO invokes directly some specific SDMX artefacts. These SDMX artefacts have to be mapped to the corresponding aliases used in the definition of the UDO through the VtlMappingScheme and VtlMapping classes as well.{{/footnote}} to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping. 34 - 35 35 The correspondence between an alias and a SDMX artefact must be one-to-one, meaning that a generic alias identifies one and just one SDMX artefact while a SDMX artefact is identified by one and just one alias. In other words, within a VtlMappingScheme an artefact can have just one alias and different artefacts cannot have the same alias. 36 36 37 37 The references through the URN and the abbreviated URN are described in the following paragraphs. ... ... @@ -82,9 +82,7 @@ 82 82 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}}: 83 83 84 84 'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DFR(1.0.0)' <- 85 - 86 86 'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF1(1.0.0)' + 87 - 88 88 'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF2(1.0.0)' 89 89 90 90 === 12.2.3 Abbreviation of the URN === ... ... @@ -109,9 +109,7 @@ 109 109 For example, the full formulation that uses the complete URN shown at the end of the previous paragraph: 110 110 111 111 'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DFR(1.0.0)' := 112 - 113 113 'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF1(1.0.0)' + 114 - 115 115 'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF2(1.0.0)' 116 116 117 117 by omitting all the non-essential parts would become simply: ... ... @@ -118,11 +118,11 @@ 118 118 119 119 DFR := DF1 + DF2 120 120 121 -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 wikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink" %)^^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}}: 122 122 123 123 'urn:sdmx:org.sdmx.infomodel.codelist.Codelist=AG:CL_FREQ(1.0.0)' 124 124 125 -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}}: 126 126 127 127 CL_FREQ 128 128 ... ... @@ -136,7 +136,7 @@ 136 136 137 137 SECTOR 138 138 139 -For example, the Transformation for renaming the component SECTOR of the Dataflow DF1 into SEC can be written as ^^[[(% class="wikiinternallink wikiinternallink wikiinternallinkwikiinternallink wikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink" %)^^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}}: 140 140 141 141 'DFR(1.0.0)' := 'DF1(1.0.0)' [rename SECTOR to SEC] 142 142 ... ... @@ -168,9 +168,9 @@ 168 168 169 169 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. 170 170 171 -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 wikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink 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}}. 172 172 173 -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 wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallink"%)^^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}} 174 174 175 175 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. 176 176 ... ... @@ -182,15 +182,15 @@ 182 182 183 183 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. 184 184 185 -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="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink"%)^^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}}. 186 186 187 187 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). 188 188 189 189 === 12.3.2 General mapping of VTL and SDMX data structures === 190 190 191 -This section makes reference to the VTL "Model for data and their structure" ^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^21^^>>path:#sdfootnote21sym||name="sdfootnote21anc"]](%%)^^and the correspondent SDMX "Data Structure Definition"^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallink wikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallink"%)^^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}}. 192 192 193 -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="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink"%)^^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}} 194 194 195 195 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. 196 196 ... ... @@ -206,70 +206,56 @@ 206 206 207 207 === 12.3.3 Mapping from SDMX to VTL data structures === 208 208 209 - **12.3.3.1 Basic Mapping**201 +==== 12.3.3.1 Basic Mapping ==== 210 210 211 211 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: 212 212 213 -|**SDMX**|**VTL** 214 -|Dimension|(Simple) Identifier 215 -|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 216 216 217 -[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_59eee18f.gif||alt="Shape4" height="1" width="192"]] 218 - 219 -|Measure|Measure 220 -|DataAttribute|Attribute 221 - 222 222 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). 223 223 224 -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. 225 225 226 - **12.3.3.2 Pivot Mapping**216 +==== 12.3.3.2 Pivot Mapping ==== 227 227 228 228 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. 229 229 230 -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}}. 231 231 232 -MeasureDimensions considered as a joint variable^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^24^^>>path:#sdfootnote24sym||name="sdfootnote24anc"]](%%)^^. 233 - 234 234 Among other things, the Pivot method provides also backward compatibility with the SDMX 2.1 data structures that contained a MeasureDimension. 235 235 236 236 If applied to SDMX structures that do not contain any MeasureDimension, this method behaves like the Basic mapping (see the previous paragraph). 237 237 238 - ^^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. 239 239 240 240 The SDMX structures that contain a MeasureDimension are mapped as described below (this mapping is equivalent to a pivoting operation): 241 241 242 242 * A SDMX simple dimension becomes a VTL (simple) identifier and a SDMX TimeDimension becomes a VTL (time) identifier; 243 -* 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 244 - 245 -Component; 246 - 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; 247 247 * The SDMX MeasureDimension is not mapped to VTL (it disappears in the VTL Data Structure); 248 248 * The SDMX Measure is not mapped to VTL as well (it disappears in the VTL Data Structure); 249 249 * An SDMX DataAttribute is mapped in different ways according to its AttributeRelationship: 250 -** 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 251 - 252 -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; 253 - 254 -* 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; 255 255 ** 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). 256 256 ** 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. 257 257 258 258 The summary mapping table of the "pivot" mapping from SDMX to VTL for the SDMX data structures that contain a MeasureDimension is the following: 259 259 260 -|**SDMX**|**VTL** 261 -|Dimension|(Simple) Identifier 262 -|TimeDimension|(Time) Identifier 263 -|MeasureDimension & one Measure|((( 264 -One Measure for each Code of the 265 - 266 -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 267 267 ))) 268 -|DataAttribute not depending on the MeasureDimension|Attribute 269 -|DataAttribute depending on the MeasureDimension|((( 270 -One Attribute for each Code of the 271 - 272 -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 273 273 ))) 274 274 275 275 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. ... ... @@ -277,14 +277,11 @@ 277 277 At observation / data point level, calling Cj (j=1, … n) the j^^th^^ Code of the MeasureDimension: 278 278 279 279 * 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; 280 -* 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) 281 - 282 -Identifiers, (time) Identifier and Attributes. 283 - 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. 284 284 * 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 285 285 * 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 286 286 287 - **12.3.3.3 From SDMX DataAttributes to VTL Measures**262 +==== 12.3.3.3 From SDMX DataAttributes to VTL Measures ==== 288 288 289 289 * 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 290 290 ... ... @@ -296,7 +296,7 @@ 296 296 297 297 === 12.3.4 Mapping from VTL to SDMX data structures === 298 298 299 - **12.3.4.1 Basic Mapping**274 +==== 12.3.4.1 Basic Mapping ==== 300 300 301 301 The main mapping method **from VTL to SDMX** is called **Basic **mapping as well. 302 302 ... ... @@ -306,11 +306,12 @@ 306 306 307 307 Mapping table: 308 308 309 -|**VTL**|**SDMX** 310 -|(Simple) Identifier|Dimension 311 -|(Time) Identifier|TimeDimension 312 -|Measure|Measure 313 -|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 314 314 315 315 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. 316 316 ... ... @@ -320,7 +320,7 @@ 320 320 321 321 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. 322 322 323 - **12.3.4.2 Unpivot Mapping**299 +==== 12.3.4.2 Unpivot Mapping ==== 324 324 325 325 An alternative mapping method from VTL to SDMX is the **Unpivot **mapping. 326 326 ... ... @@ -344,11 +344,12 @@ 344 344 345 345 The summary mapping table of the **unpivot** mapping method is the following: 346 346 347 -|**VTL**|**SDMX** 348 -|(Simple) Identifier|Dimension 349 -|(Time) Identifier|TimeDimension 350 -|All Measure Components|MeasureDimension (having one Code for each VTL measure component) & one Measure 351 -|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 352 352 353 353 At observation / data point level: 354 354 ... ... @@ -362,7 +362,7 @@ 362 362 363 363 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. 364 364 365 - **12.3.4.3 From VTL Measures to SDMX Data Attributes**342 +==== 12.3.4.3 From VTL Measures to SDMX Data Attributes ==== 366 366 367 367 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”). 368 368 ... ... @@ -370,12 +370,13 @@ 370 370 371 371 The mapping table is the following: 372 372 373 -|VTL|SDMX 374 -|(Simple) Identifier|Dimension 375 -|(Time) Identifier|TimeDimension 376 -|Some Measures|Measure 377 -|Other Measures|DataAttribute 378 -|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 379 379 380 380 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. 381 381 ... ... @@ -393,20 +393,20 @@ 393 393 394 394 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). 395 395 396 -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="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink"%)^^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}} 397 397 398 -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="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink 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}} 399 399 400 400 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. 401 401 402 402 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: 403 403 404 -* For a given SDMX Dataflow, the user (VTL definer) declares the DimensionComponents on which the mapping will be based, in a given order. ^^[[(% class="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink"%)^^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. 405 405 * The VTL Data Set is given a name using a special notation also called “ordered concatenation” and composed of the following parts: 406 406 ** 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); 407 -** a slash (“/”) as a separator; ^^[[(% class="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallink"%)^^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}} 408 408 409 -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="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink"%)^^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. 410 410 411 411 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. 412 412 ... ... @@ -422,7 +422,7 @@ 422 422 423 423 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. 424 424 425 -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="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallink wikiinternallinkwikiinternallink" %)^^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. 426 426 427 427 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. 428 428 ... ... @@ -430,7 +430,7 @@ 430 430 431 431 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. 432 432 433 -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="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallink" %)^^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. 434 434 435 435 basic, pivot …). 436 436 ... ... @@ -450,7 +450,7 @@ 450 450 451 451 … … … 452 452 453 -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="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink"%)^^32^^>>path:#sdfootnote32sym||name="sdfootnote32anc"]](%%)^^431 +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}} 454 454 455 455 In the direction from SDMX to VTL it is allowed to omit the value of one or more 456 456 ... ... @@ -478,12 +478,12 @@ 478 478 479 479 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: 480 480 481 -* each part is calculated as a VTL derived Data Set, result of a dedicated VTL Transformation; ^^[[(%class="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink"%)^^33^^>>path:#sdfootnote33sym||name="sdfootnote33anc"]](%%)^^482 -* 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 wikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink"%)^^34^^>>path:#sdfootnote34sym||name="sdfootnote34anc"]](%%)^^459 +* 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}} 460 +* 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}} 483 483 484 -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="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallink wikiinternallink"%)^^35^^>>path:#sdfootnote35sym||name="sdfootnote35anc"]](%%)^^.462 +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}}. 485 485 486 -The corresponding VTL Transformations, assuming that the result needs to be persistent, would be of this kind: ^^ [[(% class="wikiinternallink wikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallink wikiinternallinkwikiinternallink wikiinternallinkwikiinternallink"%)^^36^^>>path:#sdfootnote36sym||name="sdfootnote36anc"]](%%)^^464 +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}} 487 487 488 488 ‘DF2(1.0.0)/INDICATORvalue.COUNTRYvalue’ <- expression 489 489 ... ... @@ -539,9 +539,9 @@ 539 539 540 540 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 541 541 542 -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 wikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink 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.520 +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. 543 543 544 -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="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink"%)^^38^^>>path:#sdfootnote38sym||name="sdfootnote38anc"]](%%)[[(%class="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallink"%)^^39^^>>path:#sdfootnote39sym||name="sdfootnote39anc"]](%%)^^522 +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}} 545 545 546 546 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). 547 547 ... ... @@ -549,52 +549,51 @@ 549 549 550 550 With reference to the VTL “model for Variables and Value domains”, the following additional mappings have to be considered: 551 551 552 -|VTL|SDMX 553 -|**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^^ 554 -|**Represented Variable**|((( 530 +(% style="width:1170.29px" %) 531 +|**VTL**|(% style="width:754px" %)**SDMX** 532 +|**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}} 533 +|**Represented Variable**|(% style="width:754px" %)((( 555 555 **Concept** with a definite 556 556 557 557 Representation 558 558 ))) 559 -|**Value Domain**|((( 538 +|**Value Domain**|(% style="width:754px" %)((( 560 560 **Representation** (see the Structure 561 561 562 562 Pattern in the Base Package) 563 563 ))) 564 -|**Enumerated Value Domain / Code List**|**Codelist** 565 -|**Code**|((( 543 +|**Enumerated Value Domain / Code List**|(% style="width:754px" %)**Codelist** 544 +|**Code**|(% style="width:754px" %)((( 566 566 **Code** (for enumerated 567 567 568 568 DimensionComponent, Measure, DataAttribute) 569 569 ))) 570 -|**Described Value Domain**|((( 571 -non-enumerated** Representation**549 +|**Described Value Domain**|(% style="width:754px" %)((( 550 +non-enumerated** Representation** 572 572 573 573 (having Facets / ExtendedFacets, see the Structure Pattern in the Base Package) 574 574 ))) 575 -|**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 576 -| |((( 577 -to a valid **value **(for non-enumerated** ** 578 - 579 -Representations) 554 +|**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 555 +| |(% style="width:754px" %)((( 556 +to a valid **value **(for non-enumerated** **Representations) 580 580 ))) 581 -|**Value Domain Subset / Set**|This abstraction does not exist in SDMX 582 -|**Enumerated Value Domain Subset / Enumerated Set**|This abstraction does not exist in SDMX 583 -|**Described Value Domain Subset / Described Set**|This abstraction does not exist in SDMX 584 -|**Set list**|This abstraction does not exist in SDMX 558 +|**Value Domain Subset / Set**|(% style="width:754px" %)This abstraction does not exist in SDMX 559 +|**Enumerated Value Domain Subset / Enumerated Set**|(% style="width:754px" %)This abstraction does not exist in SDMX 560 +|**Described Value Domain Subset / Described Set**|(% style="width:754px" %)This abstraction does not exist in SDMX 561 +|**Set list**|(% style="width:754px" %)This abstraction does not exist in SDMX 585 585 586 586 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). 587 587 588 -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="wikiinternallinkwikiinternallink wikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallink"%)^^40^^>>path:#sdfootnote40sym||name="sdfootnote40anc"]](%%)^^, while the SDMX Concepts can have different Representations in different DataStructures.^^[[(% class="wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallink wikiinternallinkwikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink"%)^^41^^>>path:#sdfootnote41sym||name="sdfootnote41anc"]](%%)^^This means that one SDMX Concept can correspond to many VTL Variables, one for each representation the Concept has.565 +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. 589 589 590 590 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 591 591 592 -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.569 +DS_c := DS_a + DS_b (where DS_a, DS_b, DS_c are VTL Data Sets) 593 593 571 +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. 572 + 594 594 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 595 595 596 -[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_59eee18f.gif||alt="Shape5" height="1" width="192"]] 597 - 598 598 Transformations to ensure that the VTL expressions are consistent with the actual representations of the correspondent SDMX Concepts. 599 599 600 600 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. ... ... @@ -609,7 +609,8 @@ 609 609 610 610 [[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_e3df33ae.png||height="543" width="483"]] 611 611 612 -==== Figure 22 – VTL Data Types ==== 589 +(% class="wikigeneratedid" id="HFigure222013VTLDataTypes" %) 590 +**Figure 22 – VTL Data Types** 613 613 614 614 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. 615 615 ... ... @@ -616,131 +616,12 @@ 616 616 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): 617 617 618 618 597 +**Figure 23 – VTL Basic Scalar Types** 619 619 620 620 ((( 621 -//n// 622 - 623 -//a// 624 - 625 -//e// 626 - 627 -//l// 628 - 629 -//o// 630 - 631 -//o// 632 - 633 -//B// 634 - 635 -//n// 636 - 637 -//o// 638 - 639 -//i// 640 - 641 -//t// 642 - 643 -//a// 644 - 645 -//r// 646 - 647 -//u// 648 - 649 -//D// 650 - 651 -//d// 652 - 653 -//o// 654 - 655 -//i// 656 - 657 -//r// 658 - 659 -//e// 660 - 661 -//p// 662 - 663 -//_// 664 - 665 -//e// 666 - 667 -//m// 668 - 669 -//i// 670 - 671 -//T// 672 - 673 -//e// 674 - 675 -//t// 676 - 677 -//a// 678 - 679 -//D// 680 - 681 -//e// 682 - 683 -//m// 684 - 685 -//i// 686 - 687 -//T// 688 - 689 -//r// 690 - 691 -//e// 692 - 693 -//g// 694 - 695 -//e// 696 - 697 -//t// 698 - 699 -//n// 700 - 701 -//I// 702 - 703 -//r// 704 - 705 -//e// 706 - 707 -//b// 708 - 709 -//m// 710 - 711 -//u// 712 - 713 -//N// 714 - 715 -//g// 716 - 717 -//n// 718 - 719 -//i// 720 - 721 -//r// 722 - 723 -//t// 724 - 725 -//S// 726 - 727 -//r// 728 - 729 -//a// 730 - 731 -//l// 732 - 733 -//a// 734 - 735 -//c// 736 - 737 -//S// 738 - 739 -[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_82d45833.gif||alt="Shape6" height="231" width="184"]] 600 + 740 740 ))) 741 741 742 -==== Figure 23 – VTL Basic Scalar Types ==== 743 - 744 744 === 12.4.2 VTL basic scalar types and SDMX data types === 745 745 746 746 The VTL assumes that a basic scalar type has a unique internal representation and can have more external representations. ... ... @@ -763,204 +763,159 @@ 763 763 764 764 The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types. 765 765 766 -|SDMX data type (BasicComponentDataType)|Default VTL basic scalar type 767 -|((( 625 +(% style="width:823.294px" %) 626 +|(% style="width:509px" %)**SDMX data type (BasicComponentDataType)**|(% style="width:312px" %)**Default VTL basic scalar type** 627 +|(% style="width:509px" %)((( 768 768 String 769 - 770 770 (string allowing any character) 771 -)))|string 772 -|((( 630 +)))|(% style="width:312px" %)string 631 +|(% style="width:509px" %)((( 773 773 Alpha 774 - 775 775 (string which only allows A-z) 776 -)))|string 777 -|((( 634 +)))|(% style="width:312px" %)string 635 +|(% style="width:509px" %)((( 778 778 AlphaNumeric 779 - 780 780 (string which only allows A-z and 0-9) 781 -)))|string 782 -|((( 638 +)))|(% style="width:312px" %)string 639 +|(% style="width:509px" %)((( 783 783 Numeric 784 - 785 785 (string which only allows 0-9, but is not numeric so that is can having leading zeros) 786 -)))|string 787 -|((( 642 +)))|(% style="width:312px" %)string 643 +|(% style="width:509px" %)((( 788 788 BigInteger 789 - 790 790 (corresponds to XML Schema xs:integer datatype; infinite set of integer values) 791 -)))|integer 792 -|((( 646 +)))|(% style="width:312px" %)integer 647 +|(% style="width:509px" %)((( 793 793 Integer 794 - 795 -(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647 796 - 797 -(inclusive)) 798 -)))|integer 799 -|((( 649 +(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647 (inclusive)) 650 +)))|(% style="width:312px" %)integer 651 +|(% style="width:509px" %)((( 800 800 Long 801 - 802 -(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and 803 - 804 -+9223372036854775807 (inclusive)) 805 -)))|integer 806 -|((( 653 +(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and +9223372036854775807 (inclusive)) 654 +)))|(% style="width:312px" %)integer 655 +|(% style="width:509px" %)((( 807 807 Short 808 - 809 809 (corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive)) 810 -)))|integer 811 -|Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|number 812 -|((( 658 +)))|(% style="width:312px" %)integer 659 +|(% 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 660 +|(% style="width:509px" %)((( 813 813 Float 814 - 815 815 (corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type) 816 -)))|number 817 -|((( 663 +)))|(% style="width:312px" %)number 664 +|(% style="width:509px" %)((( 818 818 Double 819 - 820 820 (corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type) 821 -)))|number 822 -|((( 667 +)))|(% style="width:312px" %)number 668 +|(% style="width:509px" %)((( 823 823 Boolean 670 +(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of binary-valued logic: {true, false}) 671 +)))|(% style="width:312px" %)boolean 824 824 825 -(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of 826 - 827 -binary-valued logic: {true, false}) 828 -)))|boolean 829 - 830 -| |(% colspan="2" %)((( 673 +(% style="width:822.294px" %) 674 +|(% colspan="2" style="width:507px" %)((( 831 831 URI 832 - 833 833 (corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference) 834 -)))|(% colspan=" 2" %)string835 -| |(% colspan="2" %)(((677 +)))|(% colspan="1" style="width:311px" %)string 678 +|(% colspan="2" style="width:507px" %)((( 836 836 Count 837 - 838 838 (an integer following a sequential pattern, increasing by 1 for each occurrence) 839 -)))|(% colspan=" 2" %)integer840 -| |(% colspan="2" %)(((681 +)))|(% colspan="1" style="width:311px" %)integer 682 +|(% colspan="2" style="width:507px" %)((( 841 841 InclusiveValueRange 842 - 843 843 (decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue) 844 -)))|(% colspan=" 2" %)number845 -| |(% colspan="2" %)(((685 +)))|(% colspan="1" style="width:311px" %)number 686 +|(% colspan="2" style="width:507px" %)((( 846 846 ExclusiveValueRange 847 - 848 848 (decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue) 849 -)))|(% colspan=" 2" %)number850 -| |(% colspan="2" %)(((689 +)))|(% colspan="1" style="width:311px" %)number 690 +|(% colspan="2" style="width:507px" %)((( 851 851 Incremental 852 - 853 853 (decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation) 854 -)))|(% colspan=" 2" %)number855 -| |(% colspan="2" %)(((693 +)))|(% colspan="1" style="width:311px" %)number 694 +|(% colspan="2" style="width:507px" %)((( 856 856 ObservationalTimePeriod 857 - 858 858 (superset of StandardTimePeriod and TimeRange) 859 -)))|(% colspan=" 2" %)time860 -| |(% colspan="2" %)(((697 +)))|(% colspan="1" style="width:311px" %)time 698 +|(% colspan="2" style="width:507px" %)((( 861 861 StandardTimePeriod 862 - 863 -(superset of BasicTimePeriod and 864 - 865 -ReportingTimePeriod) 866 -)))|(% colspan="2" %)time 867 -| |(% colspan="2" %)((( 700 +(superset of BasicTimePeriod and ReportingTimePeriod) 701 +)))|(% colspan="1" style="width:311px" %)time 702 +|(% colspan="2" style="width:507px" %)((( 868 868 BasicTimePeriod 869 - 870 870 (superset of GregorianTimePeriod and DateTime) 871 -)))|(% colspan=" 2" %)date872 -| |(% colspan="2" %)(((705 +)))|(% colspan="1" style="width:311px" %)date 706 +|(% colspan="2" style="width:507px" %)((( 873 873 GregorianTimePeriod 874 - 875 875 (superset of GregorianYear, GregorianYearMonth, and GregorianDay) 876 -)))|(% colspan=" 2" %)date877 -| |(% colspan="2" %)GregorianYear (YYYY)|(% colspan="2" %)date878 -| |(% colspan="2" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="2" %)date879 -| |(% colspan="2" %)GregorianDay (YYYY-MM-DD)|(% colspan="2" %)date880 -| |(% colspan="2" %)(((709 +)))|(% colspan="1" style="width:311px" %)date 710 +|(% colspan="2" style="width:507px" %)GregorianYear (YYYY)|(% colspan="1" style="width:311px" %)date 711 +|(% colspan="2" style="width:507px" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="1" style="width:311px" %)date 712 +|(% colspan="2" style="width:507px" %)GregorianDay (YYYY-MM-DD)|(% colspan="1" style="width:311px" %)date 713 +|(% colspan="2" style="width:507px" %)((( 881 881 ReportingTimePeriod 882 - 883 -(superset of RepostingYear, ReportingSemester, 884 - 885 -ReportingTrimester, ReportingQuarter, 886 - 887 -ReportingMonth, ReportingWeek, ReportingDay) 888 -)))|(% colspan="2" %)time_period 889 -| |(% colspan="2" %)((( 715 +(superset of RepostingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, ReportingDay) 716 +)))|(% colspan="1" style="width:311px" %)time_period 717 +|(% colspan="2" style="width:507px" %)((( 890 890 ReportingYear 891 - 892 892 (YYYY-A1 – 1 year period) 893 -)))|(% colspan=" 2" %)time_period894 -| |(% colspan="2" %)(((720 +)))|(% colspan="1" style="width:311px" %)time_period 721 +|(% colspan="2" style="width:507px" %)((( 895 895 ReportingSemester 896 - 897 897 (YYYY-Ss – 6 month period) 898 -)))|(% colspan=" 2" %)time_period899 -| |(% colspan="2" %)(((724 +)))|(% colspan="1" style="width:311px" %)time_period 725 +|(% colspan="2" style="width:507px" %)((( 900 900 ReportingTrimester 901 - 902 902 (YYYY-Tt – 4 month period) 903 -)))|(% colspan=" 2" %)time_period904 -| |(% colspan="2" %)(((728 +)))|(% colspan="1" style="width:311px" %)time_period 729 +|(% colspan="2" style="width:507px" %)((( 905 905 ReportingQuarter 906 - 907 907 (YYYY-Qq – 3 month period) 908 -)))|(% colspan=" 2" %)time_period909 -| |(% colspan="2" %)(((732 +)))|(% colspan="1" style="width:311px" %)time_period 733 +|(% colspan="2" style="width:507px" %)((( 910 910 ReportingMonth 911 - 912 912 (YYYY-Mmm – 1 month period) 913 -)))|(% colspan="2" %)time_period 914 -| |(% colspan="2" %)ReportingWeek|(% colspan="2" %)time_period 915 -| |(% colspan="2" %) |(% colspan="2" %) 916 -| |(% colspan="2" %) |(% colspan="2" %) 917 -|(% colspan="2" %)(YYYY-Www – 7 day period; following ISO 8601 definition of a week in a year)|(% colspan="2" %) | 918 -|(% colspan="2" %)((( 736 +)))|(% colspan="1" style="width:311px" %)time_period 737 +|(% colspan="2" style="width:507px" %)ReportingWeek|(% colspan="1" style="width:311px" %)time_period 738 +|(% 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" %) 739 +|(% colspan="1" style="width:507px" %)((( 919 919 ReportingDay 920 - 921 921 (YYYY-Dddd – 1 day period) 922 -)))|(% colspan="2" %)time_period |923 -|(% colspan=" 2" %)(((742 +)))|(% colspan="2" style="width:312px" %)time_period 743 +|(% colspan="1" style="width:507px" %)((( 924 924 DateTime 925 - 926 926 (YYYY-MM-DDThh:mm:ss) 927 -)))|(% colspan="2" %)date |928 -|(% colspan=" 2" %)(((746 +)))|(% colspan="2" style="width:312px" %)date 747 +|(% colspan="1" style="width:507px" %)((( 929 929 TimeRange 930 - 931 931 (YYYY-MM-DD(Thh:mm:ss)?/<duration>) 932 -)))|(% colspan="2" %)time |933 -|(% colspan=" 2" %)(((750 +)))|(% colspan="2" style="width:312px" %)time 751 +|(% colspan="1" style="width:507px" %)((( 934 934 Month 935 - 936 936 (~-~-MM; speicifies a month independent of a year; e.g. February is black history month in the United States) 937 -)))|(% colspan="2" %)string |938 -|(% colspan=" 2" %)(((754 +)))|(% colspan="2" style="width:312px" %)string 755 +|(% colspan="1" style="width:507px" %)((( 939 939 MonthDay 940 - 941 941 (~-~-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) 942 -)))|(% colspan="2" %)string |943 -|(% colspan=" 2" %)(((758 +)))|(% colspan="2" style="width:312px" %)string 759 +|(% colspan="1" style="width:507px" %)((( 944 944 Day 945 - 946 946 (~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday) 947 -)))|(% colspan="2" %)string |948 -|(% colspan=" 2" %)(((762 +)))|(% colspan="2" style="width:312px" %)string 763 +|(% colspan="1" style="width:507px" %)((( 949 949 Time 950 - 951 951 (hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM) 952 -)))|(% colspan="2" %)string |953 -|(% colspan=" 2" %)(((766 +)))|(% colspan="2" style="width:312px" %)string 767 +|(% colspan="1" style="width:507px" %)((( 954 954 Duration 955 - 956 956 (corresponds to XML Schema xs:duration datatype) 957 -)))|(% colspan="2" %)duration |958 -|(% colspan=" 2" %)XHTML|(% colspan="2" %)Metadata type – not applicable|959 -|(% colspan=" 2" %)KeyValues|(% colspan="2" %)Metadata type – not applicable|960 -|(% colspan=" 2" %)IdentifiableReference|(% colspan="2" %)Metadata type – not applicable|961 -|(% colspan=" 2" %)DataSetReference|(% colspan="2" %)Metadata type – not applicable|770 +)))|(% colspan="2" style="width:312px" %)duration 771 +|(% colspan="1" style="width:507px" %)XHTML|(% colspan="2" style="width:312px" %)Metadata type – not applicable 772 +|(% colspan="1" style="width:507px" %)KeyValues|(% colspan="2" style="width:312px" %)Metadata type – not applicable 773 +|(% colspan="1" style="width:507px" %)IdentifiableReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable 774 +|(% colspan="1" style="width:507px" %)DataSetReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable 962 962 963 -==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ==== 776 +(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes" %) 777 +**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types** 964 964 965 965 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). 966 966 ... ... @@ -968,39 +968,32 @@ 968 968 969 969 The following table describes the default conversion from the VTL basic scalar types to the SDMX data types . 970 970 971 -|((( 972 -VTL basic 973 - 974 -scalar type 975 -)))|((( 976 -Default SDMX data type 977 - 978 -(BasicComponentDataType 979 - 980 -) 981 -)))|Default output format 982 -|String|String|Like XML (xs:string) 983 -|Number|Float|Like XML (xs:float) 984 -|Integer|Integer|Like XML (xs:int) 985 -|Date|DateTime|YYYY-MM-DDT00:00:00Z 986 -|Time|StandardTimePeriod|<date>/<date> (as defined above) 987 -|time_period|((( 785 +(% style="width:1073.29px" %) 786 +|(% style="width:207px" %)((( 787 +**VTL basic scalar type** 788 +)))|(% style="width:462px" %)((( 789 +**Default SDMX data type (BasicComponentDataType)** 790 +)))|(% style="width:402px" %)**Default output format** 791 +|(% style="width:207px" %)String|(% style="width:462px" %)String|(% style="width:402px" %)Like XML (xs:string) 792 +|(% style="width:207px" %)Number|(% style="width:462px" %)Float|(% style="width:402px" %)Like XML (xs:float) 793 +|(% style="width:207px" %)Integer|(% style="width:462px" %)Integer|(% style="width:402px" %)Like XML (xs:int) 794 +|(% style="width:207px" %)Date|(% style="width:462px" %)DateTime|(% style="width:402px" %)YYYY-MM-DDT00:00:00Z 795 +|(% style="width:207px" %)Time|(% style="width:462px" %)StandardTimePeriod|(% style="width:402px" %)<date>/<date> (as defined above) 796 +|(% style="width:207px" %)time_period|(% style="width:462px" %)((( 988 988 ReportingTimePeriod 989 - 990 990 (StandardReportingPeriod) 991 -)))|((( 799 +)))|(% style="width:402px" %)((( 992 992 YYYY-Pppp 993 - 994 994 (according to SDMX ) 995 995 ))) 996 -|Duration|Duration|((( 803 +|(% style="width:207px" %)Duration|(% style="width:462px" %)Duration|(% style="width:402px" %)((( 997 997 Like XML (xs:duration) 998 - 999 999 PnYnMnDTnHnMnS 1000 1000 ))) 1001 -|Boolean|Boolean|Like XML (xs:boolean) with the values "true" or "false" 807 +|(% style="width:207px" %)Boolean|(% style="width:462px" %)Boolean|(% style="width:402px" %)Like XML (xs:boolean) with the values "true" or "false" 1002 1002 1003 -==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ==== 809 +(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes-1" %) 810 +**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types** 1004 1004 1005 1005 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). 1006 1006 ... ... @@ -1054,7 +1054,7 @@ 1054 1054 |N|fixed number of digits used in the preceding textual representation of the month or the day 1055 1055 | | 1056 1056 1057 -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 wikiinternallinkwikiinternallink wikiinternallink wikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallinkwikiinternallink"%)^^42^^>>path:#sdfootnote42sym||name="sdfootnote42anc"]](%%)^^.864 +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}}. 1058 1058 1059 1059 === 12.4.5 Null Values === 1060 1060 ... ... @@ -1072,10 +1072,8 @@ 1072 1072 1073 1073 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). 1074 1074 1075 -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 882 +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. 1076 1076 1077 -TransformationScheme. 1078 - 1079 1079 In case a literal is operand of a VTL Cast operation, the format specified in the Cast overrides all the possible otherwise specified formats. 1080 1080 1081 1081 {{putFootnotes/}}