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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.
... ... @@ -202,7 +202,7 @@
202 202  
203 203  === 12.3.3 Mapping from SDMX to VTL data structures ===
204 204  
205 -**12.3.3.1 Basic Mapping**
201 +==== 12.3.3.1 Basic Mapping ====
206 206  
207 207  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:
208 208  
... ... @@ -232,18 +232,11 @@
232 232  The SDMX structures that contain a MeasureDimension are mapped as described below (this mapping is equivalent to a pivoting operation):
233 233  
234 234  * A SDMX simple dimension becomes a VTL (simple) identifier and a SDMX TimeDimension becomes a VTL (time) identifier;
235 -* Each possible Code Cj of the SDMX MeasureDimension is mapped to a VTL Measure, having the same name as the SDMX Code (i.e. Cj); the VTL Measure Cj is a new VTL component even if the SDMX data structure has not such a
236 -
237 -Component;
238 -
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;
239 239  * The SDMX MeasureDimension is not mapped to VTL (it disappears in the VTL Data Structure);
240 240  * The SDMX Measure is not mapped to VTL as well (it disappears in the VTL Data Structure);
241 241  * An SDMX DataAttribute is mapped in different ways according to its AttributeRelationship:
242 -** If, according to the SDMX AttributeRelationship, the values of the DataAttribute do not depend on the values of the MeasureDimension, the SDMX DataAttribute becomes a VTL Attribute having the same name. This happens if the
243 -
244 -AttributeRelationship is not specified (i.e. the DataAttribute does not depend on any DimensionComponent and therefore is at data set level), or if it refers to a set (or a group) of dimensions which does not include the MeasureDimension;
245 -
246 -*
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;
247 247  ** 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).
248 248  ** 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.
249 249  
... ... @@ -266,10 +266,7 @@
266 266  At observation / data point level, calling Cj (j=1, … n) the j^^th^^ Code of the MeasureDimension:
267 267  
268 268  * 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;
269 -* The values of the SDMX simple Dimensions, TimeDimension and DataAttributes not depending on the MeasureDimension (these components by definition have always the same values for all the observations of the set above) become the values of the corresponding VTL (simple)
270 -
271 -Identifiers, (time) Identifier and Attributes.
272 -
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.
273 273  * 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
274 274  * 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
275 275  
... ... @@ -362,7 +362,7 @@
362 362  The mapping table is the following:
363 363  
364 364  (% style="width:689.294px" %)
365 -|(% style="width:344px" %)VTL|(% style="width:341px" %)SDMX
351 +|(% style="width:344px" %)**VTL**|(% style="width:341px" %)**SDMX**
366 366  |(% style="width:344px" %)(Simple) Identifier|(% style="width:341px" %)Dimension
367 367  |(% style="width:344px" %)(Time) Identifier|(% style="width:341px" %)TimeDimension
368 368  |(% style="width:344px" %)Some Measures|(% style="width:341px" %)Measure
... ... @@ -422,24 +422,17 @@
422 422  
423 423  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.
424 424  
425 -In order to obtain the data structure of these VTL Data Sets from the SDMX one, it is assumed that the SDMX DimensionComponents on which the mapping is based are dropped, i.e. not maintained in the VTL data structure; this is possible because their values are fixed for each one of the invoked VTL Data Sets{{footnote}}If these DimensionComponents would not be dropped, the various VTL Data Sets resulting from this kind of mapping would have non-matching values for the Identifiers corresponding to the mapping Dimensions (e.g. POPULATION and COUNTRY). As a consequence, taking into account that the typical binary VTL operations at dataset level (+, -, *, / and so on) are executed on the observations having matching values for the identifiers, it would not be possible to compose the resulting VTL datasets one another (e.g. it would not be possible to calculate the population ratio between USA and CANADA).{{/footnote}}. After that, the mapping method from SDMX to VTL specified for the Dataflow DF1(1.0.0) is applied (i.e.
411 +In order to obtain the data structure of these VTL Data Sets from the SDMX one, it is assumed that the SDMX DimensionComponents on which the mapping is based are dropped, i.e. not maintained in the VTL data structure; this is possible because their values are fixed for each one of the invoked VTL Data Sets{{footnote}}If these DimensionComponents would not be dropped, the various VTL Data Sets resulting from this kind of mapping would have non-matching values for the Identifiers corresponding to the mapping Dimensions (e.g. POPULATION and COUNTRY). As a consequence, taking into account that the typical binary VTL operations at dataset level (+, -, *, / and so on) are executed on the observations having matching values for the identifiers, it would not be possible to compose the resulting VTL datasets one another (e.g. it would not be possible to calculate the population ratio between USA and CANADA).{{/footnote}}. After that, the mapping method from SDMX to VTL specified for the Dataflow DF1(1.0.0) is applied (i.e. basic, pivot …).
426 426  
427 -basic, pivot …).
413 +In the example above, for all the datasets of the kind ‘DF1(1.0.0)///INDICATORvalue//.//COUNTRYvalue//’, the dimensions INDICATOR and COUNTRY would be dropped so that the data structure of all the resulting VTL Data Sets would have the identifier TIME_PERIOD only.
428 428  
429 -In the example above, for all the datasets of the kind
430 -
431 -‘DF1(1.0.0)///INDICATORvalue//.//COUNTRYvalue//’, the dimensions INDICATOR and COUNTRY would be dropped so that the data structure of all the resulting VTL Data Sets would have the identifier TIME_PERIOD only.
432 -
433 433  It should be noted that the desired VTL Data Sets (i.e. of the kind ‘DF1(1.0.0)/// INDICATORvalue//.//COUNTRYvalue//’) can be obtained also by applying the VTL operator “**sub**” (subspace) to the Dataflow DF1(1.0.0), like in the following VTL expression:
434 434  
435 435  ‘DF1(1.0.0)/POPULATION.USA’ :=
436 -
437 437  DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“USA” ];
438 438  
439 439  ‘DF1(1.0.0)/POPULATION.CANADA’ :=
440 -
441 441  DF1(1.0.0) [ sub INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
442 -
443 443  … … …
444 444  
445 445  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}}
... ... @@ -453,7 +453,6 @@
453 453  This is equivalent to the application of the VTL “sub” operator only to the identifier //INDICATOR//:
454 454  
455 455  ‘DF1(1.0.0)/POPULATION.’ :=
456 -
457 457  DF1(1.0.0) [ sub INDICATOR=“POPULATION” ];
458 458  
459 459  Therefore the VTL Data Set ‘DF1(1.0.0)/POPULATION.’ would have the identifiers COUNTRY and TIME_PERIOD.
... ... @@ -484,11 +484,8 @@
484 484  ‘DF2(1.0.0)/GDPPERCAPITA.USA’ <- expression11; ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ <- expression12;
485 485  
486 486  … … …
487 -
488 488  ‘DF2(1.0.0)/POPGROWTH.USA’ <- expression21;
489 -
490 490  ‘DF2(1.0.0)/POPGROWTH.CANADA’ <- expression22;
491 -
492 492  … … …
493 493  
494 494  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:
... ... @@ -496,13 +496,9 @@
496 496  VTL dataset INDICATOR value COUNTRY value
497 497  
498 498  ‘DF2(1.0.0)/GDPPERCAPITA.USA’ GDPPERCAPITA USA
499 -
500 500  ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ GDPPERCAPITA CANADA … … …
501 -
502 502  ‘DF2(1.0.0)/POPGROWTH.USA’ POPGROWTH USA
503 -
504 504  ‘DF2(1.0.0)/POPGROWTH.CANADA’ POPGROWTH CANADA
505 -
506 506  … … …
507 507  
508 508  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:
... ... @@ -600,7 +600,8 @@
600 600  
601 601  [[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_e3df33ae.png||height="543" width="483"]]
602 602  
603 -==== Figure 22 – VTL Data Types ====
574 +(% class="wikigeneratedid" id="HFigure222013VTLDataTypes" %)
575 +**Figure 22 – VTL Data Types**
604 604  
605 605  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.
606 606  
... ... @@ -607,131 +607,12 @@
607 607  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):
608 608  
609 609  
582 +**Figure 23 – VTL Basic Scalar Types**
610 610  
611 611  (((
612 -//n//
613 -
614 -//a//
615 -
616 -//e//
617 -
618 -//l//
619 -
620 -//o//
621 -
622 -//o//
623 -
624 -//B//
625 -
626 -//n//
627 -
628 -//o//
629 -
630 -//i//
631 -
632 -//t//
633 -
634 -//a//
635 -
636 -//r//
637 -
638 -//u//
639 -
640 -//D//
641 -
642 -//d//
643 -
644 -//o//
645 -
646 -//i//
647 -
648 -//r//
649 -
650 -//e//
651 -
652 -//p//
653 -
654 -//_//
655 -
656 -//e//
657 -
658 -//m//
659 -
660 -//i//
661 -
662 -//T//
663 -
664 -//e//
665 -
666 -//t//
667 -
668 -//a//
669 -
670 -//D//
671 -
672 -//e//
673 -
674 -//m//
675 -
676 -//i//
677 -
678 -//T//
679 -
680 -//r//
681 -
682 -//e//
683 -
684 -//g//
685 -
686 -//e//
687 -
688 -//t//
689 -
690 -//n//
691 -
692 -//I//
693 -
694 -//r//
695 -
696 -//e//
697 -
698 -//b//
699 -
700 -//m//
701 -
702 -//u//
703 -
704 -//N//
705 -
706 -//g//
707 -
708 -//n//
709 -
710 -//i//
711 -
712 -//r//
713 -
714 -//t//
715 -
716 -//S//
717 -
718 -//r//
719 -
720 -//a//
721 -
722 -//l//
723 -
724 -//a//
725 -
726 -//c//
727 -
728 -//S//
729 -
730 -[[image:SDMX 3-0-0 SECTION 6 FINAL-1.0_en_82d45833.gif||alt="Shape6" height="231" width="184"]]
585 +
731 731  )))
732 732  
733 -==== Figure 23 – VTL Basic Scalar Types ====
734 -
735 735  === 12.4.2 VTL basic scalar types and SDMX data types ===
736 736  
737 737  The VTL assumes that a basic scalar type has a unique internal representation and can have more external representations.
... ... @@ -754,204 +754,159 @@
754 754  
755 755  The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types.
756 756  
757 -|SDMX data type (BasicComponentDataType)|Default VTL basic scalar type
758 -|(((
610 +(% style="width:823.294px" %)
611 +|(% style="width:509px" %)**SDMX data type (BasicComponentDataType)**|(% style="width:312px" %)**Default VTL basic scalar type**
612 +|(% style="width:509px" %)(((
759 759  String
760 -
761 761  (string allowing any character)
762 -)))|string
763 -|(((
615 +)))|(% style="width:312px" %)string
616 +|(% style="width:509px" %)(((
764 764  Alpha
765 -
766 766  (string which only allows A-z)
767 -)))|string
768 -|(((
619 +)))|(% style="width:312px" %)string
620 +|(% style="width:509px" %)(((
769 769  AlphaNumeric
770 -
771 771  (string which only allows A-z and 0-9)
772 -)))|string
773 -|(((
623 +)))|(% style="width:312px" %)string
624 +|(% style="width:509px" %)(((
774 774  Numeric
775 -
776 776  (string which only allows 0-9, but is not numeric so that is can having leading zeros)
777 -)))|string
778 -|(((
627 +)))|(% style="width:312px" %)string
628 +|(% style="width:509px" %)(((
779 779  BigInteger
780 -
781 781  (corresponds to XML Schema xs:integer datatype; infinite set of integer values)
782 -)))|integer
783 -|(((
631 +)))|(% style="width:312px" %)integer
632 +|(% style="width:509px" %)(((
784 784  Integer
785 -
786 -(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647
787 -
788 -(inclusive))
789 -)))|integer
790 -|(((
634 +(corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647 (inclusive))
635 +)))|(% style="width:312px" %)integer
636 +|(% style="width:509px" %)(((
791 791  Long
792 -
793 -(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and
794 -
795 -+9223372036854775807 (inclusive))
796 -)))|integer
797 -|(((
638 +(corresponds to XML Schema xs:long datatype; between -9223372036854775808 and +9223372036854775807 (inclusive))
639 +)))|(% style="width:312px" %)integer
640 +|(% style="width:509px" %)(((
798 798  Short
799 -
800 800  (corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive))
801 -)))|integer
802 -|Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|number
803 -|(((
643 +)))|(% style="width:312px" %)integer
644 +|(% 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
645 +|(% style="width:509px" %)(((
804 804  Float
805 -
806 806  (corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type)
807 -)))|number
808 -|(((
648 +)))|(% style="width:312px" %)number
649 +|(% style="width:509px" %)(((
809 809  Double
810 -
811 811  (corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type)
812 -)))|number
813 -|(((
652 +)))|(% style="width:312px" %)number
653 +|(% style="width:509px" %)(((
814 814  Boolean
655 +(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of binary-valued logic: {true, false})
656 +)))|(% style="width:312px" %)boolean
815 815  
816 -(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of
817 -
818 -binary-valued logic: {true, false})
819 -)))|boolean
820 -
821 -| |(% colspan="2" %)(((
658 +(% style="width:822.294px" %)
659 +|(% colspan="2" style="width:507px" %)(((
822 822  URI
823 -
824 824  (corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference)
825 -)))|(% colspan="2" %)string
826 -| |(% colspan="2" %)(((
662 +)))|(% colspan="1" style="width:311px" %)string
663 +|(% colspan="2" style="width:507px" %)(((
827 827  Count
828 -
829 829  (an integer following a sequential pattern, increasing by 1 for each occurrence)
830 -)))|(% colspan="2" %)integer
831 -| |(% colspan="2" %)(((
666 +)))|(% colspan="1" style="width:311px" %)integer
667 +|(% colspan="2" style="width:507px" %)(((
832 832  InclusiveValueRange
833 -
834 834  (decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
835 -)))|(% colspan="2" %)number
836 -| |(% colspan="2" %)(((
670 +)))|(% colspan="1" style="width:311px" %)number
671 +|(% colspan="2" style="width:507px" %)(((
837 837  ExclusiveValueRange
838 -
839 839  (decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
840 -)))|(% colspan="2" %)number
841 -| |(% colspan="2" %)(((
674 +)))|(% colspan="1" style="width:311px" %)number
675 +|(% colspan="2" style="width:507px" %)(((
842 842  Incremental
843 -
844 844  (decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation)
845 -)))|(% colspan="2" %)number
846 -| |(% colspan="2" %)(((
678 +)))|(% colspan="1" style="width:311px" %)number
679 +|(% colspan="2" style="width:507px" %)(((
847 847  ObservationalTimePeriod
848 -
849 849  (superset of StandardTimePeriod and TimeRange)
850 -)))|(% colspan="2" %)time
851 -| |(% colspan="2" %)(((
682 +)))|(% colspan="1" style="width:311px" %)time
683 +|(% colspan="2" style="width:507px" %)(((
852 852  StandardTimePeriod
853 -
854 -(superset of BasicTimePeriod and
855 -
856 -ReportingTimePeriod)
857 -)))|(% colspan="2" %)time
858 -| |(% colspan="2" %)(((
685 +(superset of BasicTimePeriod and ReportingTimePeriod)
686 +)))|(% colspan="1" style="width:311px" %)time
687 +|(% colspan="2" style="width:507px" %)(((
859 859  BasicTimePeriod
860 -
861 861  (superset of GregorianTimePeriod and DateTime)
862 -)))|(% colspan="2" %)date
863 -| |(% colspan="2" %)(((
690 +)))|(% colspan="1" style="width:311px" %)date
691 +|(% colspan="2" style="width:507px" %)(((
864 864  GregorianTimePeriod
865 -
866 866  (superset of GregorianYear, GregorianYearMonth, and GregorianDay)
867 -)))|(% colspan="2" %)date
868 -| |(% colspan="2" %)GregorianYear (YYYY)|(% colspan="2" %)date
869 -| |(% colspan="2" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="2" %)date
870 -| |(% colspan="2" %)GregorianDay (YYYY-MM-DD)|(% colspan="2" %)date
871 -| |(% colspan="2" %)(((
694 +)))|(% colspan="1" style="width:311px" %)date
695 +|(% colspan="2" style="width:507px" %)GregorianYear (YYYY)|(% colspan="1" style="width:311px" %)date
696 +|(% colspan="2" style="width:507px" %)GregorianYearMonth / GregorianMonth (YYYY-MM)|(% colspan="1" style="width:311px" %)date
697 +|(% colspan="2" style="width:507px" %)GregorianDay (YYYY-MM-DD)|(% colspan="1" style="width:311px" %)date
698 +|(% colspan="2" style="width:507px" %)(((
872 872  ReportingTimePeriod
873 -
874 -(superset of RepostingYear, ReportingSemester,
875 -
876 -ReportingTrimester, ReportingQuarter,
877 -
878 -ReportingMonth, ReportingWeek, ReportingDay)
879 -)))|(% colspan="2" %)time_period
880 -| |(% colspan="2" %)(((
700 +(superset of RepostingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, ReportingDay)
701 +)))|(% colspan="1" style="width:311px" %)time_period
702 +|(% colspan="2" style="width:507px" %)(((
881 881  ReportingYear
882 -
883 883  (YYYY-A1 – 1 year period)
884 -)))|(% colspan="2" %)time_period
885 -| |(% colspan="2" %)(((
705 +)))|(% colspan="1" style="width:311px" %)time_period
706 +|(% colspan="2" style="width:507px" %)(((
886 886  ReportingSemester
887 -
888 888  (YYYY-Ss – 6 month period)
889 -)))|(% colspan="2" %)time_period
890 -| |(% colspan="2" %)(((
709 +)))|(% colspan="1" style="width:311px" %)time_period
710 +|(% colspan="2" style="width:507px" %)(((
891 891  ReportingTrimester
892 -
893 893  (YYYY-Tt – 4 month period)
894 -)))|(% colspan="2" %)time_period
895 -| |(% colspan="2" %)(((
713 +)))|(% colspan="1" style="width:311px" %)time_period
714 +|(% colspan="2" style="width:507px" %)(((
896 896  ReportingQuarter
897 -
898 898  (YYYY-Qq – 3 month period)
899 -)))|(% colspan="2" %)time_period
900 -| |(% colspan="2" %)(((
717 +)))|(% colspan="1" style="width:311px" %)time_period
718 +|(% colspan="2" style="width:507px" %)(((
901 901  ReportingMonth
902 -
903 903  (YYYY-Mmm – 1 month period)
904 -)))|(% colspan="2" %)time_period
905 -| |(% colspan="2" %)ReportingWeek|(% colspan="2" %)time_period
906 -| |(% colspan="2" %) |(% colspan="2" %)
907 -| |(% colspan="2" %) |(% colspan="2" %)
908 -|(% colspan="2" %)(YYYY-Www – 7 day period; following ISO 8601 definition of a week in a year)|(% colspan="2" %) |
909 -|(% colspan="2" %)(((
721 +)))|(% colspan="1" style="width:311px" %)time_period
722 +|(% colspan="2" style="width:507px" %)ReportingWeek|(% colspan="1" style="width:311px" %)time_period
723 +|(% 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" %)
724 +|(% colspan="1" style="width:507px" %)(((
910 910  ReportingDay
911 -
912 912  (YYYY-Dddd – 1 day period)
913 -)))|(% colspan="2" %)time_period|
914 -|(% colspan="2" %)(((
727 +)))|(% colspan="2" style="width:312px" %)time_period
728 +|(% colspan="1" style="width:507px" %)(((
915 915  DateTime
916 -
917 917  (YYYY-MM-DDThh:mm:ss)
918 -)))|(% colspan="2" %)date|
919 -|(% colspan="2" %)(((
731 +)))|(% colspan="2" style="width:312px" %)date
732 +|(% colspan="1" style="width:507px" %)(((
920 920  TimeRange
921 -
922 922  (YYYY-MM-DD(Thh:mm:ss)?/<duration>)
923 -)))|(% colspan="2" %)time|
924 -|(% colspan="2" %)(((
735 +)))|(% colspan="2" style="width:312px" %)time
736 +|(% colspan="1" style="width:507px" %)(((
925 925  Month
926 -
927 927  (~-~-MM; speicifies a month independent of a year; e.g. February is black history month in the United States)
928 -)))|(% colspan="2" %)string|
929 -|(% colspan="2" %)(((
739 +)))|(% colspan="2" style="width:312px" %)string
740 +|(% colspan="1" style="width:507px" %)(((
930 930  MonthDay
931 -
932 932  (~-~-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)
933 -)))|(% colspan="2" %)string|
934 -|(% colspan="2" %)(((
743 +)))|(% colspan="2" style="width:312px" %)string
744 +|(% colspan="1" style="width:507px" %)(((
935 935  Day
936 -
937 937  (~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday)
938 -)))|(% colspan="2" %)string|
939 -|(% colspan="2" %)(((
747 +)))|(% colspan="2" style="width:312px" %)string
748 +|(% colspan="1" style="width:507px" %)(((
940 940  Time
941 -
942 942  (hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM)
943 -)))|(% colspan="2" %)string|
944 -|(% colspan="2" %)(((
751 +)))|(% colspan="2" style="width:312px" %)string
752 +|(% colspan="1" style="width:507px" %)(((
945 945  Duration
946 -
947 947  (corresponds to XML Schema xs:duration datatype)
948 -)))|(% colspan="2" %)duration|
949 -|(% colspan="2" %)XHTML|(% colspan="2" %)Metadata type – not applicable|
950 -|(% colspan="2" %)KeyValues|(% colspan="2" %)Metadata type – not applicable|
951 -|(% colspan="2" %)IdentifiableReference|(% colspan="2" %)Metadata type – not applicable|
952 -|(% colspan="2" %)DataSetReference|(% colspan="2" %)Metadata type – not applicable|
755 +)))|(% colspan="2" style="width:312px" %)duration
756 +|(% colspan="1" style="width:507px" %)XHTML|(% colspan="2" style="width:312px" %)Metadata type – not applicable
757 +|(% colspan="1" style="width:507px" %)KeyValues|(% colspan="2" style="width:312px" %)Metadata type – not applicable
758 +|(% colspan="1" style="width:507px" %)IdentifiableReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
759 +|(% colspan="1" style="width:507px" %)DataSetReference|(% colspan="2" style="width:312px" %)Metadata type – not applicable
953 953  
954 -==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
761 +(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes" %)
762 +**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
955 955  
956 956  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).
957 957  
... ... @@ -959,39 +959,32 @@
959 959  
960 960  The following table describes the default conversion from the VTL basic scalar types to the SDMX data types .
961 961  
962 -|(((
963 -VTL basic
964 -
965 -scalar type
966 -)))|(((
967 -Default SDMX data type
968 -
969 -(BasicComponentDataType
970 -
971 -)
972 -)))|Default output format
973 -|String|String|Like XML (xs:string)
974 -|Number|Float|Like XML (xs:float)
975 -|Integer|Integer|Like XML (xs:int)
976 -|Date|DateTime|YYYY-MM-DDT00:00:00Z
977 -|Time|StandardTimePeriod|<date>/<date> (as defined above)
978 -|time_period|(((
770 +(% style="width:1073.29px" %)
771 +|(% style="width:207px" %)(((
772 +**VTL basic scalar type**
773 +)))|(% style="width:462px" %)(((
774 +**Default SDMX data type (BasicComponentDataType)**
775 +)))|(% style="width:402px" %)**Default output format**
776 +|(% style="width:207px" %)String|(% style="width:462px" %)String|(% style="width:402px" %)Like XML (xs:string)
777 +|(% style="width:207px" %)Number|(% style="width:462px" %)Float|(% style="width:402px" %)Like XML (xs:float)
778 +|(% style="width:207px" %)Integer|(% style="width:462px" %)Integer|(% style="width:402px" %)Like XML (xs:int)
779 +|(% style="width:207px" %)Date|(% style="width:462px" %)DateTime|(% style="width:402px" %)YYYY-MM-DDT00:00:00Z
780 +|(% style="width:207px" %)Time|(% style="width:462px" %)StandardTimePeriod|(% style="width:402px" %)<date>/<date> (as defined above)
781 +|(% style="width:207px" %)time_period|(% style="width:462px" %)(((
979 979  ReportingTimePeriod
980 -
981 981  (StandardReportingPeriod)
982 -)))|(((
784 +)))|(% style="width:402px" %)(((
983 983  YYYY-Pppp
984 -
985 985  (according to SDMX )
986 986  )))
987 -|Duration|Duration|(((
788 +|(% style="width:207px" %)Duration|(% style="width:462px" %)Duration|(% style="width:402px" %)(((
988 988  Like XML (xs:duration)
989 -
990 990  PnYnMnDTnHnMnS
991 991  )))
992 -|Boolean|Boolean|Like XML (xs:boolean) with the values "true" or "false"
792 +|(% style="width:207px" %)Boolean|(% style="width:462px" %)Boolean|(% style="width:402px" %)Like XML (xs:boolean) with the values "true" or "false"
993 993  
994 -==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
794 +(% class="wikigeneratedid" id="HFigure142013MappingsfromSDMXdatatypestoVTLBasicScalarTypes-1" %)
795 +**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
995 995  
996 996  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).
997 997  
... ... @@ -1045,7 +1045,7 @@
1045 1045  |N|fixed number of digits used in the preceding textual representation of the month or the day
1046 1046  | |
1047 1047  
1048 -The default conversion, either standard or customized, can be used to deduce automatically the representation of the components of the result of a VTL Transformation. In alternative, the representation of the resulting SDMX Dataflow can be given explicitly by providing its DataStructureDefinition. In other words, the representation specified in the DSD, if available, overrides any default conversion^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^42^^>>path:#sdfootnote42sym||name="sdfootnote42anc"]](%%)^^.
849 +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}}.
1049 1049  
1050 1050  === 12.4.5 Null Values ===
1051 1051  
... ... @@ -1063,10 +1063,8 @@
1063 1063  
1064 1064  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).
1065 1065  
1066 -Like in the case of the conversion of NULLs described in the previous paragraph, the overriding assumption is applied, for a certain VTL basic scalar type, if a value is found for the vtlLiteralFormat attribute of the CustomType of such VTL basic scalar type. The overriding assumption is applied for all the literals of a related VTL
867 +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.
1067 1067  
1068 -TransformationScheme.
1069 -
1070 1070  In case a literal is operand of a VTL Cast operation, the format specified in the Cast overrides all the possible otherwise specified formats.
1071 1071  
1072 1072  {{putFootnotes/}}