Changes for page 12 Validation and Transformation Language (VTL)
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... ... @@ -19,7 +19,6 @@ 19 19 This section does not explain the VTL language or any of the content published in the VTL guides. Rather, this is a description of how the VTL can be used in the SDMX context and applied to SDMX artefacts. 20 20 21 21 == 12.2 References to SDMX artefacts from VTL statements == 22 - 23 23 === 12.2.1 Introduction === 24 24 25 25 The VTL can manipulate SDMX artefacts (or objects) by referencing them through predefined conventional names (aliases). ... ... @@ -49,8 +49,10 @@ 49 49 50 50 The generic structure of the URN is the following: 51 51 52 -SDMXprefix.SDMX-IM-package-name.class-name=agency-id:maintainedobject-id (maintainedobject-version).*container-object-id.object-id51 +SDMXprefix.SDMX-IM-package-name.class-name=agency-id:maintainedobject-id 53 53 53 +(maintainedobject-version).*container-object-id.object-id 54 + 54 54 The **SDMXprefix** is "urn:sdmx:org", always the same for all SDMX artefacts. 55 55 56 56 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". ... ... @@ -71,19 +71,24 @@ 71 71 72 72 The maintainedobject-version is the version, according to the SDMX versioning rules, of the maintained object which the artefact belongs to (for example, possible versions might be 1.0, 2.3, 1.0.0, 2.1.0 or 3.1.2). 73 73 74 -The container-object-id does not apply to the classes that can be referenced in VTL Transformations, therefore is not present in their URN .75 +The container-object-id does not apply to the classes that can be referenced in VTL Transformations, therefore is not present in their URN 75 75 76 76 The object-id is the name of the non-maintainable artefact (when the artefact is maintainable its name is already specified as the maintainedobject-id, see above), in particular it has to be specified: 77 77 78 -* if the artefact is a Dimension, TimeDimension, Measure or DataAttribute (the object-id is the name of one of the artefacts above, which are data structure components) 79 +* if the artefact is a Dimension, TimeDimension, Measure or 80 + 81 +DataAttribute (the object-id is the name of one of the artefacts above, which are data structure components) 82 + 79 79 * if the artefact is a Concept (the object-id is the name of the Concept) 80 80 81 81 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}}: 82 82 83 ->(% style="font-size:16px" %) 'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DFR(1.0.0)' <- 84 ->(% style="font-size:16px" %) 'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF1(1.0.0)' + 85 ->(% style="font-size:16px" %) 'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF2(1.0.0)' 87 +'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DFR(1.0.0)' <- 86 86 89 +'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF1(1.0.0)' + 90 + 91 +'urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF2(1.0.0)' 92 + 87 87 === 12.2.3 Abbreviation of the URN === 88 88 89 89 The complete formulation of the URN described above is exhaustive but verbose, even for very simple statements. In order to reduce the verbosity through a simplified identifier and make the work of transformation definers easier, proper abbreviations of the URN are possible. Using this approach, the referenced artefacts remain intelligible in the VTL code by a human reader. ... ... @@ -92,9 +92,7 @@ 92 92 93 93 * 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. 94 94 * 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: 95 -** "datastructure" for the classes Dataflow, Dimension, TimeDimension, Measure, DataAttribute, 96 -** "conceptscheme" for the class Concept, 97 -** "codelist" for the class Codelist. 101 +** "datastructure" for the classes Dataflow, Dimension, TimeDimension, Measure, DataAttribute, o "conceptscheme" for the class Concept, o "codelist" for the class Codelist. 98 98 * 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}}. 99 99 * 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). 100 100 * 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; ... ... @@ -176,7 +176,6 @@ 176 176 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. 177 177 178 178 == 12.3 Mapping between SDMX and VTL artefacts == 179 - 180 180 === 12.3.1. When the mapping occurs === 181 181 182 182 The mapping methods between the VTL and SDMX object classes allow transforming a SDMX definition in a VTL one and vice-versa for the artefacts to be manipulated. It should be remembered that VTL programs (i.e. Transformation Schemes) are represented in SDMX through the TransformationScheme maintainable class which is composed of Transformations (nameable artefacts). Each Transformation assigns the outcome of the evaluation of a VTL expression to a result: the input operands of the expression and the result can be SDMX artefacts. 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. ... ... @@ -219,7 +219,7 @@ 219 219 220 220 With the Basic mapping, one SDMX observation^^27^^ generates one VTL data point. 221 221 222 - ====12.3.3.2 Pivot Mapping====225 +**12.3.3.2 Pivot Mapping** 223 223 224 224 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. 225 225 ... ... @@ -250,6 +250,7 @@ 250 250 |DataAttribute not depending on the MeasureDimension|Attribute 251 251 |DataAttribute depending on the MeasureDimension|((( 252 252 One Attribute for each Code of the 256 + 253 253 SDMX MeasureDimension 254 254 ))) 255 255 ... ... @@ -262,10 +262,13 @@ 262 262 263 263 Identifiers, (time) Identifier and Attributes. 264 264 265 -* 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 269 +* 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 270 + 271 +Cj 272 + 266 266 * 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 267 267 268 - ====12.3.3.3 From SDMX DataAttributes to VTL Measures====275 +**12.3.3.3 From SDMX DataAttributes to VTL Measures** 269 269 270 270 * 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 271 271 ... ... @@ -275,9 +275,11 @@ 275 275 276 276 Proper VTL features allow changing the role of specific attributes even after the SDMX to VTL mapping: they can be useful when only some of the DataAttributes need to be managed as VTL Measures. 277 277 278 -=== 12.3.4 Mapping from VTL to SDMX data structures === 285 +1. 286 +11. 287 +111. Mapping from VTL to SDMX data structures 279 279 280 - ====12.3.4.1 Basic Mapping====289 +**12.3.4.1 Basic Mapping** 281 281 282 282 The main mapping method **from VTL to SDMX** is called **Basic **mapping as well. 283 283 ... ... @@ -301,7 +301,7 @@ 301 301 302 302 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. 303 303 304 - ====12.3.4.2 Unpivot Mapping====313 +**12.3.4.2 Unpivot Mapping** 305 305 306 306 An alternative mapping method from VTL to SDMX is the **Unpivot **mapping. 307 307 ... ... @@ -337,7 +337,7 @@ 337 337 338 338 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. 339 339 340 - ====12.3.4.3 From VTL Measures to SDMX Data Attributes====349 +**12.3.4.3 From VTL Measures to SDMX Data Attributes** 341 341 342 342 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”). 343 343 ... ... @@ -354,7 +354,9 @@ 354 354 355 355 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. 356 356 357 -=== 12.3.5 Declaration of the mapping methods between data structures === 366 +1. 367 +11. 368 +111. Declaration of the mapping methods between data structures 358 358 359 359 In order to define and understand properly VTL Transformations, the applied mapping methods must be specified in the SDMX structural metadata. If the default mapping method (Basic) is applied, no specification is needed. 360 360 ... ... @@ -364,10 +364,14 @@ 364 364 365 365 The VtlMappingScheme is a container for zero or more VtlDataflowMapping (it may contain also mappings towards artefacts other than dataflows). 366 366 367 -=== 12.3.6 Mapping dataflow subsets to distinct VTL Data Sets === 378 +1. 379 +11. 380 +111. Mapping dataflow subsets to distinct VTL Data Sets 368 368 369 -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).382 +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 370 370 384 +(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). 385 + 371 371 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}} 372 372 373 373 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}} ... ... @@ -460,10 +460,13 @@ 460 460 Some examples follow, for some specific values of INDICATOR and COUNTRY: 461 461 462 462 ‘DF2(1.0.0)/GDPPERCAPITA.USA’ <- expression11; ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ <- expression12; 478 + 463 463 … … … 464 464 465 465 ‘DF2(1.0.0)/POPGROWTH.USA’ <- expression21; 482 + 466 466 ‘DF2(1.0.0)/POPGROWTH.CANADA’ <- expression22; 484 + 467 467 … … … 468 468 469 469 As said, it is assumed that these VTL derived Data Sets have the TIME_PERIOD as the only identifier. In the mapping from VTL to SMDX, the Dimensions INDICATOR and COUNTRY are added to the VTL data structure on order to obtain the SDMX one, with the following values respectively: ... ... @@ -470,9 +470,13 @@ 470 470 471 471 VTL dataset INDICATOR value COUNTRY value 472 472 491 + 473 473 ‘DF2(1.0.0)/GDPPERCAPITA.USA’ GDPPERCAPITA USA 493 + 474 474 ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ GDPPERCAPITA CANADA … … … 495 + 475 475 ‘DF2(1.0.0)/POPGROWTH.USA’ POPGROWTH USA 497 + 476 476 ‘DF2(1.0.0)/POPGROWTH.CANADA’ POPGROWTH CANADA 477 477 478 478 … … … ... ... @@ -480,15 +480,25 @@ 480 480 It should be noted that the application of this many-to-one mapping from VTL to SDMX is equivalent to an appropriate sequence of VTL Transformations. These use the VTL operator “calc” to add the proper VTL identifiers (in the example, INDICATOR and COUNTRY) and to assign to them the proper values and the operator “union” in order to obtain the final VTL dataset (in the example DF2(1.0.0)), that can be mapped oneto-one to the homonymous SDMX Dataflow. Following the same example, these VTL Transformations would be: 481 481 482 482 DF2bis_GDPPERCAPITA_USA := ‘DF2(1.0.0)/GDPPERCAPITA.USA’ [calc identifier INDICATOR := ”GDPPERCAPITA”, identifier COUNTRY := ”USA”]; 505 + 483 483 DF2bis_GDPPERCAPITA_CANADA := ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ [calc identifier INDICATOR:=”GDPPERCAPITA”, identifier COUNTRY:=”CANADA”]; … … … 507 + 484 484 DF2bis_POPGROWTH_USA := ‘DF2(1.0.0)/POPGROWTH.USA’ 509 + 485 485 [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”USA”]; 511 + 486 486 DF2bis_POPGROWTH_CANADA’ := ‘DF2(1.0.0)/POPGROWTH.CANADA’ [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”CANADA”]; … … … 513 + 487 487 DF2(1.0) <- UNION (DF2bis_GDPPERCAPITA_USA’, 515 + 488 488 DF2bis_GDPPERCAPITA_CANADA’, 517 + 489 489 … , 519 + 490 490 DF2bis_POPGROWTH_USA’, 521 + 491 491 DF2bis_POPGROWTH_CANADA’ 523 + 492 492 …); 493 493 494 494 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 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. ... ... @@ -497,7 +497,9 @@ 497 497 498 498 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). 499 499 500 -=== 12.3.7 Mapping variables and value domains between VTL and SDMX === 532 +1. 533 +11. 534 +111. Mapping variables and value domains between VTL and SDMX 501 501 502 502 With reference to the VTL “model for Variables and Value domains”, the following additional mappings have to be considered: 503 503 ... ... @@ -506,6 +506,7 @@ 506 506 |**Represented Variable**|**Concept** with a definite Representation 507 507 |**Value Domain**|((( 508 508 **Representation** (see the Structure 543 + 509 509 Pattern in the Base Package) 510 510 ))) 511 511 |**Enumerated Value Domain / Code List**|**Codelist** ... ... @@ -512,6 +512,7 @@ 512 512 |**Code**|**Code** (for enumerated DimensionComponent, Measure, DataAttribute) 513 513 |**Described Value Domain**|((( 514 514 non-enumerated** Representation** 550 + 515 515 (having Facets / ExtendedFacets, see the Structure Pattern in the Base Package) 516 516 ))) 517 517 |**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 ... ... @@ -535,10 +535,10 @@ 535 535 536 536 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. 537 537 538 -== 12.4 Mapping between SDMX and VTL Data Types == 574 +1. 575 +11. Mapping between SDMX and VTL Data Types 576 +111. VTL Data types 539 539 540 -=== 12.4.1 VTL Data types === 541 - 542 542 According to the VTL User Guide the possible operations in VTL depend on the data types of the artefacts. For example, numbers can be multiplied but text strings cannot. In the VTL Transformations, the compliance between the operators and the data types of their operands is statically checked, i.e., violations result in compile-time errors. 543 543 544 544 The VTL data types are sub-divided in scalar types (like integers, strings, etc.), which are the types of the scalar values, and compound types (like Data Sets, Components, Rulesets, etc.), which are the types of the compound structures. See below the diagram of the VTL data types, taken from the VTL User Manual: ... ... @@ -545,15 +545,17 @@ 545 545 546 546 [[image:1750067055028-964.png]] 547 547 548 - **Figure 22 – VTL Data Types**584 +==== Figure 22 – VTL Data Types ==== 549 549 550 550 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. 551 551 552 552 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): 553 553 554 - **Figure 23 – VTL Basic Scalar Types**590 +==== Figure 23 – VTL Basic Scalar Types ==== 555 555 556 -=== 12.4.2 VTL basic scalar types and SDMX data types === 592 +1. 593 +11. 594 +111. VTL basic scalar types and SDMX data types 557 557 558 558 The VTL assumes that a basic scalar type has a unique internal representation and can have more external representations. 559 559 ... ... @@ -571,7 +571,9 @@ 571 571 572 572 The opposite conversion, i.e. from VTL to SDMX, happens when a VTL result, i.e. a VTL Data Set output of a Transformation, must become a SDMX artefact (or part of it). The values of the VTL result must be converted into the desired (SDMX) external representations (data types) of the SDMX artefact. 573 573 574 -=== 12.4.3 Mapping SDMX data types to VTL basic scalar types === 612 +1. 613 +11. 614 +111. Mapping SDMX data types to VTL basic scalar types 575 575 576 576 The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types. 577 577 ... ... @@ -578,6 +578,7 @@ 578 578 |SDMX data type (BasicComponentDataType)|Default VTL basic scalar type 579 579 |((( 580 580 String 621 + 581 581 (string allowing any character) 582 582 )))|string 583 583 |((( ... ... @@ -587,6 +587,7 @@ 587 587 )))|string 588 588 |((( 589 589 AlphaNumeric 631 + 590 590 (string which only allows A-z and 0-9) 591 591 )))|string 592 592 |((( ... ... @@ -596,70 +596,89 @@ 596 596 )))|string 597 597 |((( 598 598 BigInteger 641 + 599 599 (corresponds to XML Schema xs:integer datatype; infinite set of integer values) 600 600 )))|integer 601 601 |((( 602 602 Integer 646 + 603 603 (corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647 648 + 604 604 (inclusive)) 605 605 )))|integer 606 606 |((( 607 607 Long 653 + 608 608 (corresponds to XML Schema xs:long datatype; between -9223372036854775808 and 655 + 609 609 +9223372036854775807 (inclusive)) 610 610 )))|integer 611 611 |((( 612 612 Short 660 + 613 613 (corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive)) 614 614 )))|integer 615 615 |Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|number 616 616 |((( 617 617 Float 666 + 618 618 (corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type) 619 619 )))|number 620 620 |((( 621 621 Double 671 + 622 622 (corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type) 623 623 )))|number 624 624 |((( 625 625 Boolean 676 + 626 626 (corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of 678 + 627 627 binary-valued logic: {true, false}) 628 628 )))|boolean 629 629 |((( 630 630 URI 683 + 631 631 (corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference) 632 632 )))|string 633 633 |((( 634 634 Count 688 + 635 635 (an integer following a sequential pattern, increasing by 1 for each occurrence) 636 636 )))|integer 637 637 |((( 638 638 InclusiveValueRange 693 + 639 639 (decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue) 640 640 )))|number 641 641 |((( 642 642 ExclusiveValueRange 698 + 643 643 (decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue) 644 644 )))|number 645 645 |((( 646 646 Incremental 703 + 647 647 (decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation) 648 648 )))|number 649 649 |((( 650 650 ObservationalTimePeriod 708 + 651 651 (superset of StandardTimePeriod and TimeRange) 652 652 )))|time 653 653 |((( 654 654 StandardTimePeriod 713 + 655 655 (superset of BasicTimePeriod and ReportingTimePeriod) 656 656 )))|time 657 657 |((( 658 658 BasicTimePeriod 718 + 659 659 (superset of GregorianTimePeriod and DateTime) 660 660 )))|date 661 661 |((( 662 662 GregorianTimePeriod 723 + 663 663 (superset of GregorianYear, GregorianYearMonth, and GregorianDay) 664 664 )))|date 665 665 |GregorianYear (YYYY)|date ... ... @@ -667,26 +667,32 @@ 667 667 |GregorianDay (YYYY-MM-DD)|date 668 668 |((( 669 669 ReportingTimePeriod 731 + 670 670 (superset of RepostingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, ReportingDay) 671 671 )))|time_period 672 672 |((( 673 673 ReportingYear 736 + 674 674 (YYYY-A1 – 1 year period) 675 675 )))|time_period 676 676 |((( 677 677 ReportingSemester 741 + 678 678 (YYYY-Ss – 6 month period) 679 679 )))|time_period 680 680 |((( 681 681 ReportingTrimester 746 + 682 682 (YYYY-Tt – 4 month period) 683 683 )))|time_period 684 684 |((( 685 685 ReportingQuarter 751 + 686 686 (YYYY-Qq – 3 month period) 687 687 )))|time_period 688 688 |((( 689 689 ReportingMonth 756 + 690 690 (YYYY-Mmm – 1 month period) 691 691 )))|time_period 692 692 |ReportingWeek|time_period ... ... @@ -693,34 +693,42 @@ 693 693 | (YYYY-Www – 7 day period; following ISO 8601 definition of a week in a year)| 694 694 |((( 695 695 ReportingDay 763 + 696 696 (YYYY-Dddd – 1 day period) 697 697 )))|time_period 698 698 |((( 699 699 DateTime 768 + 700 700 (YYYY-MM-DDThh:mm:ss) 701 701 )))|date 702 702 |((( 703 703 TimeRange 773 + 704 704 (YYYY-MM-DD(Thh:mm:ss)?/<duration>) 705 705 )))|time 706 706 |((( 707 707 Month 778 + 708 708 (~-~-MM; speicifies a month independent of a year; e.g. February is black history month in the United States) 709 709 )))|string 710 710 |((( 711 711 MonthDay 783 + 712 712 (~-~-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) 713 713 )))|string 714 714 |((( 715 715 Day 788 + 716 716 (~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday) 717 717 )))|string 718 718 |((( 719 719 Time 793 + 720 720 (hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM) 721 721 )))|string 722 722 |((( 723 723 Duration 798 + 724 724 (corresponds to XML Schema xs:duration datatype) 725 725 )))|duration 726 726 |XHTML|Metadata type – not applicable ... ... @@ -728,20 +728,27 @@ 728 728 |IdentifiableReference|Metadata type – not applicable 729 729 |DataSetReference|Metadata type – not applicable 730 730 731 - **Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**806 +додол 732 732 808 +==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ==== 809 + 733 733 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). 734 734 735 -=== 12.4.4 Mapping VTL basic scalar types to SDMX data types === 812 +1. 813 +11. 814 +111. Mapping VTL basic scalar types to SDMX data types 736 736 737 737 The following table describes the default conversion from the VTL basic scalar types to the SDMX data types . 738 738 739 739 |((( 740 740 VTL basic 820 + 741 741 scalar type 742 742 )))|((( 743 743 Default SDMX data type 824 + 744 744 (BasicComponentDataType 826 + 745 745 ) 746 746 )))|Default output format 747 747 |String|String|Like XML (xs:string) ... ... @@ -751,15 +751,17 @@ 751 751 |Time|StandardTimePeriod|<date>/<date> (as defined above) 752 752 |time_period|((( 753 753 ReportingTimePeriod 836 + 754 754 (StandardReportingPeriod) 755 755 )))|((( 756 756 YYYY-Pppp 840 + 757 757 (according to SDMX ) 758 758 ))) 759 759 |Duration|Duration|Like XML (xs:duration) PnYnMnDTnHnMnS 760 760 |Boolean|Boolean|Like XML (xs:boolean) with the values "true" or "false" 761 761 762 - **Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**846 +==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ==== 763 763 764 764 In case a different default conversion is desired, it can be achieved through the CustomTypeScheme and CustomType artefacts (see also the section 765 765 ... ... @@ -817,13 +817,17 @@ 817 817 818 818 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}}. 819 819 820 -=== 12.4.3 Null Values === 904 +1. 905 +11. 906 +111. Null Values 821 821 822 822 In the conversions from SDMX to VTL it is assumed by default that a missing value in SDMX becomes a NULL in VTL. After the conversion, the NULLs can be manipulated through the proper VTL operators. 823 823 824 824 On the other side, the VTL programs can produce in output NULL values for Measures and Attributes (Null values are not allowed in the Identifiers). In the conversion from VTL to SDMX, it is assumed that a NULL in VTL becomes a missing value in SDMX. In the conversion from VTL to SDMX, the default assumption can be overridden, separately for each VTL basic scalar type, by specifying which the value that represents the NULL in SDMX is. This can be specified in the attribute "nullValue" of the CustomType artefact (see also the section Transformations and Expressions of the SDMX information model). A CustomType belongs to a CustomTypeScheme, which can be referenced by one or more TransformationScheme (i.e. VTL programs). The overriding assumption is applied for all the SDMX Dataflows calculated in the TransformationScheme. 825 825 826 -=== 12.4.5 Format of the literals used in VTL Transformations === 912 +1. 913 +11. 914 +111. Format of the literals used in VTL Transformations 827 827 828 828 The VTL programs can contain literals, i.e. specific values of certain data types written directly in the VTL definitions or expressions. The VTL does not prescribe a specific format for the literals and leave the specific VTL systems and the definers of VTL Transformations free of using their preferred formats. 829 829 ... ... @@ -837,6 +837,7 @@ 837 837 838 838 In case a literal is operand of a VTL Cast operation, the format specified in the Cast overrides all the possible otherwise specified formats. 839 839 928 + 840 840 ---- 841 841 842 842 {{putFootnotes/}}