Changes for page 12 Validation and Transformation Language (VTL)

Last modified by Artur on 2025/09/10 11:19

From 1.11 to 1.10 From 1.15 to 1.14

From version 1.14

edited by Helena
on 2025/06/16 13:14

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To version 1.11

edited by Helena
on 2025/06/16 13:08

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@@ -222,7 +222,7 @@
  With the Basic mapping, one SDMX observation^^27^^ generates one VTL data point.
--==== 12.3.3.2 Pivot Mapping ====
++**12.3.3.2 Pivot Mapping**
  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.
@@ -253,6 +253,7 @@
  |DataAttribute not depending on the MeasureDimension|Attribute
  |DataAttribute depending on the MeasureDimension|(((
  One Attribute for each Code of the
++
  SDMX MeasureDimension
  )))
@@ -265,10 +265,13 @@
  Identifiers, (time) Identifier and Attributes.
--* 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
++* 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
++
  * 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
--==== 12.3.3.3 From SDMX DataAttributes to VTL Measures ====
++**12.3.3.3 From SDMX DataAttributes to VTL Measures**
  * 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
@@ -278,9 +278,11 @@
  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.
--=== 12.3.4 Mapping from VTL to SDMX data structures ===
++1.
++11.
++111. Mapping from VTL to SDMX data structures
--==== 12.3.4.1 Basic Mapping ====
++**12.3.4.1 Basic Mapping**
  The main mapping method **from VTL to SDMX** is called **Basic **mapping as well.
@@ -304,7 +304,7 @@
  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.
--==== 12.3.4.2 Unpivot Mapping ====
++**12.3.4.2 Unpivot Mapping**
  An alternative mapping method from VTL to SDMX is the **Unpivot **mapping.
@@ -340,7 +340,7 @@
  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.
--==== 12.3.4.3 From VTL Measures to SDMX Data Attributes ====
++**12.3.4.3 From VTL Measures to SDMX Data Attributes**
  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”).
@@ -357,7 +357,9 @@
  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.
--=== 12.3.5 Declaration of the mapping methods between data structures ===
++1.
++11.
++111. Declaration of the mapping methods between data structures
  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.
@@ -367,10 +367,14 @@
  The VtlMappingScheme is a container for zero or more VtlDataflowMapping (it may contain also mappings towards artefacts other than dataflows).
--=== 12.3.6 Mapping dataflow subsets to distinct VTL Data Sets ===
++1.
++11.
++111. Mapping dataflow subsets to distinct VTL Data Sets
--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).
++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).
++
  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}}
  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}}
@@ -463,10 +463,13 @@
  Some examples follow, for some specific values of INDICATOR and COUNTRY:
  ‘DF2(1.0.0)/GDPPERCAPITA.USA’ <- expression11; ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ <- expression12;
++
  … … …
  ‘DF2(1.0.0)/POPGROWTH.USA’  <- expression21;
++
  ‘DF2(1.0.0)/POPGROWTH.CANADA’ <- expression22;
++
  … … …
  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:
@@ -473,9 +473,13 @@
  VTL dataset   INDICATOR value COUNTRY value
++
  ‘DF2(1.0.0)/GDPPERCAPITA.USA’  GDPPERCAPITA USA
++
  ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ GDPPERCAPITA CANADA … … …
++
  ‘DF2(1.0.0)/POPGROWTH.USA’  POPGROWTH USA
++
  ‘DF2(1.0.0)/POPGROWTH.CANADA’  POPGROWTH CANADA
  … … …
@@ -483,15 +483,25 @@
  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:
  DF2bis_GDPPERCAPITA_USA := ‘DF2(1.0.0)/GDPPERCAPITA.USA’ [calc identifier INDICATOR := ”GDPPERCAPITA”, identifier COUNTRY := ”USA”];
++
  DF2bis_GDPPERCAPITA_CANADA := ‘DF2(1.0.0)/GDPPERCAPITA.CANADA’ [calc identifier INDICATOR:=”GDPPERCAPITA”, identifier COUNTRY:=”CANADA”]; … … …
++
  DF2bis_POPGROWTH_USA  := ‘DF2(1.0.0)/POPGROWTH.USA’
++
  [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”USA”];
++
  DF2bis_POPGROWTH_CANADA’ := ‘DF2(1.0.0)/POPGROWTH.CANADA’ [calc identifier INDICATOR := ”POPGROWTH”, identifier COUNTRY := ”CANADA”]; … … …
++
  DF2(1.0) <- UNION   (DF2bis_GDPPERCAPITA_USA’,
++
  DF2bis_GDPPERCAPITA_CANADA’,
++
  … ,
++
  DF2bis_POPGROWTH_USA’,
++
  DF2bis_POPGROWTH_CANADA’
++
  …);
  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.
@@ -500,7 +500,9 @@
  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).
--=== 12.3.7 Mapping variables and value domains between VTL and SDMX ===
++1.
++11.
++111. Mapping variables and value domains between VTL and SDMX
  With reference to the VTL “model for Variables and Value domains”, the following additional mappings have to be considered:
@@ -509,6 +509,7 @@
  |**Represented Variable**|**Concept** with a definite Representation
  |**Value Domain**|(((
  **Representation** (see the Structure
++
  Pattern in the Base Package)
  )))
  |**Enumerated Value Domain / Code List**|**Codelist**
@@ -515,6 +515,7 @@
  |**Code**|**Code** (for enumerated DimensionComponent, Measure, DataAttribute)
  |**Described Value Domain**|(((
  non-enumerated** Representation**
++
  (having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
  )))
  |**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
@@ -538,8 +538,9 @@
  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.
--== 12.4 Mapping between SDMX and VTL Data Types ==
--=== 12.4.1 VTL Data types ===
++1.
++11. Mapping between SDMX and VTL Data Types
++111. VTL Data types
  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.
@@ -547,15 +547,17 @@
  [[image:1750067055028-964.png]]
--**Figure 22 – VTL Data Types**
++==== Figure 22 – VTL Data Types ====
  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.
  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):
--**Figure 23 – VTL Basic Scalar Types**
++==== Figure 23 – VTL Basic Scalar Types ====
--=== 12.4.2 VTL basic scalar types and SDMX data types ===
++1.
++11.
++111. VTL basic scalar types and SDMX data types
  The VTL assumes that a basic scalar type has a unique internal representation and can have more external representations.
@@ -573,7 +573,9 @@
  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.
--=== 12.4.3 Mapping SDMX data types to VTL basic scalar types ===
++1.
++11.
++111. Mapping SDMX data types to VTL basic scalar types
  The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types.
@@ -580,6 +580,7 @@
  |SDMX data type (BasicComponentDataType)|Default VTL basic scalar type
  |(((
  String
++
  (string allowing any character)
  )))|string
  |(((
@@ -589,6 +589,7 @@
  )))|string
  |(((
  AlphaNumeric
++
  (string which only allows A-z and 0-9)
  )))|string
  |(((
@@ -598,70 +598,89 @@
  )))|string
  |(((
  BigInteger
++
  (corresponds to XML Schema xs:integer datatype; infinite set of integer values)
  )))|integer
  |(((
  Integer
++
  (corresponds to XML Schema xs:int datatype; between -2147483648 and +2147483647
++
  (inclusive))
  )))|integer
  |(((
  Long
++
  (corresponds to XML Schema xs:long datatype; between -9223372036854775808 and
++
  +9223372036854775807 (inclusive))
  )))|integer
  |(((
  Short
++
  (corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive))
  )))|integer
  |Decimal (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)|number
  |(((
  Float
++
  (corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type)
  )))|number
  |(((
  Double
++
  (corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type)
  )))|number
  |(((
  Boolean
++
  (corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of
++
  binary-valued logic: {true, false})
  )))|boolean
  |(((
  URI
++
  (corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference)
  )))|string
  |(((
  Count
++
  (an integer following a sequential pattern, increasing by 1 for each occurrence)
  )))|integer
  |(((
  InclusiveValueRange
++
  (decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
  )))|number
  |(((
  ExclusiveValueRange
++
  (decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
  )))|number
  |(((
  Incremental
++
  (decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation)
  )))|number
  |(((
  ObservationalTimePeriod
++
  (superset of StandardTimePeriod and TimeRange)
  )))|time
  |(((
  StandardTimePeriod
++
  (superset of BasicTimePeriod and ReportingTimePeriod)
  )))|time
  |(((
  BasicTimePeriod
++
  (superset of GregorianTimePeriod and DateTime)
  )))|date
  |(((
  GregorianTimePeriod
++
  (superset of GregorianYear, GregorianYearMonth, and GregorianDay)
  )))|date
  |GregorianYear  (YYYY)|date
@@ -669,26 +669,32 @@
  |GregorianDay (YYYY-MM-DD)|date
  |(((
  ReportingTimePeriod
++
  (superset of RepostingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, ReportingDay)
  )))|time_period
  |(((
  ReportingYear
++
  (YYYY-A1 – 1 year period)
  )))|time_period
  |(((
  ReportingSemester
++
  (YYYY-Ss – 6 month period)
  )))|time_period
  |(((
  ReportingTrimester
++
  (YYYY-Tt – 4 month period)
  )))|time_period
  |(((
  ReportingQuarter
++
  (YYYY-Qq – 3 month period)
  )))|time_period
  |(((
  ReportingMonth
++
  (YYYY-Mmm – 1 month period)
  )))|time_period
  |ReportingWeek|time_period
@@ -695,34 +695,42 @@
  | (YYYY-Www – 7 day period; following ISO 8601 definition of a week in a year)|
  |(((
  ReportingDay
++
  (YYYY-Dddd – 1 day period)
  )))|time_period
  |(((
  DateTime
++
  (YYYY-MM-DDThh:mm:ss)
  )))|date
  |(((
  TimeRange
++
  (YYYY-MM-DD(Thh:mm:ss)?/<duration>)
  )))|time
  |(((
  Month
++
  (~-~-MM; speicifies a month independent of a year; e.g. February is black history month in the United States)
  )))|string
  |(((
  MonthDay
++
  (~-~-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)
  )))|string
  |(((
  Day
++
  (~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday)
  )))|string
  |(((
  Time
++
  (hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM)
  )))|string
  |(((
  Duration
++
  (corresponds to XML Schema xs:duration datatype)
  )))|duration
  |XHTML|Metadata type – not applicable
@@ -730,20 +730,27 @@
  |IdentifiableReference|Metadata type – not applicable
  |DataSetReference|Metadata type – not applicable
--**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
++додол
++==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
++
  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).
--=== 12.4.4 Mapping VTL basic scalar types to SDMX data types ===
++1.
++11.
++111. Mapping VTL basic scalar types to SDMX data types
  The following table describes the default conversion from the VTL basic scalar types to the SDMX data types .
  |(((
  VTL          basic
++
  scalar type
  )))|(((
  Default SDMX data type
++
  (BasicComponentDataType
++
  )
  )))|Default output format
  |String|String|Like XML (xs:string)
@@ -753,15 +753,17 @@
  |Time|StandardTimePeriod|<date>/<date> (as defined above)
  |time_period|(((
  ReportingTimePeriod
++
  (StandardReportingPeriod)
  )))|(((
   YYYY-Pppp
++
  (according to SDMX )
  )))
  |Duration|Duration|Like     XML    (xs:duration) PnYnMnDTnHnMnS
  |Boolean|Boolean|Like XML (xs:boolean) with the values "true" or "false"
--**Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
++==== Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types ====
  In case a different default conversion is desired, it can be achieved through the CustomTypeScheme and CustomType artefacts (see also the section
@@ -819,13 +819,17 @@
  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}}.
--=== 12.4.3 Null Values ===
++1.
++11.
++111. Null Values
  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.
  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.
--=== 12.4.5 Format of the literals used in VTL Transformations ===
++1.
++11.
++111. Format of the literals used in VTL Transformations
  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.
@@ -839,6 +839,7 @@
  In case a literal is operand of a VTL Cast operation, the format specified in the Cast overrides all the possible otherwise specified formats.
++
  ----
  {{putFootnotes/}}