Changes for page SDMX 2.1 Standards. Section 6. Technical Notes

Last modified by Artur on 2025/08/19 10:43

From 2.1 to 1.1 From 5.4 to 5.3

From version 5.3

edited by Helena
on 2025/05/21 21:43

Change comment: There is no comment for this version

To version 2.1

edited by Helena
on 2025/05/21 17:13

Change comment: There is no comment for this version

Raw
Rendered

Summary

Page properties (1 modified, 0 added, 0 removed)

Details

Page properties

Content

@@ -1,9 +1,5 @@
--{{box title="**Contents**"}}
--{{toc/}}
--{{/box}}
++Revision History
--**Revision History**
--
  |**Revision**|**Date**|**Contents**
  | |April 2011|Initial release
  |1.0|April 2013|Added section 9 - Transforming between versions of SDMX
@@ -13,8 +13,10 @@
  == 1.1 Purpose ==
--The intention of this document is to document certain aspects of SDMX that are important to understand and will aid implementation decisions. The explanations here supplement the information documented in the SDMX XML schema and the Information Model.
++The intention of this document is to document certain aspects of SDMX that are important to understand and will aid implementation decisions. The explanations here supplement the information documented in the SDMX XML schema and the
++Information Model.
++
  == 1.2 Structure ==
  This document is organized into the following major parts:
@@ -39,7 +39,7 @@
  == 3.2 SDMX Information Model for Format Implementers ==
--=== 3.2.1 Introduction ===
++=== 3.2.1       Introduction ===
  The purpose of this sub-section is to provide an introduction to the SDMX-IM relating to Data Structure Definitions and Data Sets for those whose primary interest is in the use of the XML or EDI formats.  For those wishing to have a deeper understanding of the Information Model, the full SDMX-IM document, and other sections in this guide provide a more in-depth view, along with UML diagrams and supporting explanation. For those who are unfamiliar with DSDs, an appendix to the SDMX-IM provides a tutorial which may serve as a useful introduction.
@@ -47,12 +47,16 @@
  The Data Structure Definition and Data Set parts of the information model are consistent with the GESMES/TS version 3.0 Data Model (called SDMX-EDI in the SDMX standard), with these exceptions:
--* the “sibling group” construct has been generalized to permit any dimension or dimensions to be wildcarded, and not just frequency, as in GESMES/TS. It has been renamed a “group” to distinguish it from the “sibling group” where only frequency is wildcarded. The set of allowable partial “group” keys must be declared in the DSD, and attributes may be attached to any of these group keys;
--* furthermore, whilst the “group” has been retained for compatibility with version 2.0 and with SDMX-EDI, it has, at version 2.1, been replaced by the “Attribute Relationship” definition which is explained later
--* the section on data representation is now a convention, to support interoperability with EDIFACT-syntax implementations ( see section 3.3.2);
++the “sibling group” construct has been generalized to permit any dimension or dimensions to be wildcarded, and not just frequency, as in GESMES/TS. It has been renamed a “group” to distinguish it from the “sibling group” where only frequency is wildcarded. The set of allowable partial “group” keys must be declared in the DSD, and attributes may be attached to any of these group keys;
--DSD-specific data formats are derived from the model, and some supporting features for declaring multiple measures have been added to the structural metadata descriptions Clearly, this is not a coincidence. The GESMES/TS Data Model provides the foundation for the EDIFACT messages in SDMX-EDI, and also is the starting point for the development of SDMX-ML.
++furthermore, whilst the “group” has been retained for compatibility with version 2.0 and with SDMX-EDI, it has, at version 2.1, been replaced by the “Attribute Relationship” definition which is explained later
++the section on data representation is now a convention, to support interoperability with EDIFACT-syntax implementations ( see section 3.3.2);
++
++DSD-specific data formats are derived from the model, and some supporting features for declaring multiple measures have been added to the structural metadata descriptions
++
++Clearly, this is not a coincidence. The GESMES/TS Data Model provides the foundation for the EDIFACT messages in SDMX-EDI, and also is the starting point for the development of SDMX-ML.
++
  Note that in the descriptions below, text in courier and italicised are the names used in the information model (e.g. //DataSet//).
  == 3.3 SDMX-ML and SDMX-EDI: Comparison of Expressive Capabilities and Function ==
@@ -59,16 +59,22 @@
  SDMX offers several equivalent formats for describing data and structural metadata, optimized for use in different applications. Although all of these formats are derived directly from the SDM-IM, and are thus equivalent, the syntaxes used to express the model place some restrictions on their use. Also, different optimizations provide different capabilities. This section describes these differences, and provides some rules for applications which may need to support more than one SDMX format or syntax. This section is constrained to the Data Structure Definitionand the Date Set.
--=== 3.3.1 Format Optimizations and Differences ===
++=== 3.3.1        Format Optimizations and Differences ===
  The following section provides a brief overview of the differences between the various SDMX formats.
--Version 2.0 was characterised by 4 data messages, each with a distinct format: Generic, Compact, Cross-Sectional and Utility. Because of the design, data in some formats could not always be related to another format. In version 2.1, this issue has been addressed by merging some formats and eliminating others. As a result, in SDMX 2.1 there are just two types of data formats: //GenericData// and //StructureSpecificData// (i.e. specific to one Data Structure Definition).
++Version 2.0 was characterised by 4 data messages, each with a distinct format: Generic, Compact, Cross-Sectional and Utility. Because of the design, data in some formats could not always be related to another format. In version 2.1, this issue has been addressed by merging some formats and eliminating others. As a result, in
++SDMX 2.1 there are just two types of data formats: //GenericData// and
++
++//StructureSpecificData// (i.e. specific to one Data Structure Definition).
++
  Both of these formats are now flexible enough to allow for data to be oriented in series with any dimension used to disambiguate the observations (as opposed to only time or a cross sectional measure in version 2.0). The formats have also been expanded to allow for ungrouped observations.
--To allow for applications which only understand time series data, variations of these formats have been introduced in the form of two data messages; //GenericTimeSeriesData// and //StructureSpecificTimeSeriesData//. It is important to note that these variations are built on the same root structure and can be processed in the same manner as the base format so that they do NOT introduce additional processing requirements.
++To allow for applications which only understand time series data, variations of these formats have been introduced in the form of two data messages;
++//GenericTimeSeriesData// and //StructureSpecificTimeSeriesData//. It is important to note that these variations are built on the same root structure and can be processed in the same manner as the base format so that they do NOT introduce additional processing requirements.
++
  === //Structure Definition// ===
  The SDMX-ML Structure Message supports the use of annotations to the structure, which is not supported by the SDMX-EDI syntax.
@@ -77,8 +77,10 @@
  === //Validation// ===
--SDMX-EDI – as is typical of EDIFACT syntax messages – leaves validation to dedicated applications (“validation” being the checking of syntax, data typing, and adherence of the data message to the structure as described in the structural definition.)
++SDMX-EDI – as is typical of EDIFACT syntax messages – leaves validation to dedicated applications (“validation” being the checking of syntax, data typing, and adherence of the data message to the structure as described in the structural
++definition.)
++
  The SDMX-ML Generic Data Message also leaves validation above the XML syntax level to the application.
  The SDMX-ML DSD-specific messages will allow validation of XML syntax and datatyping to be performed with a generic XML parser, and enforce agreement between the structural definition and the data to a moderate degree with the same tool.
@@ -89,13 +89,17 @@
  === //Character Encodings// ===
--All SDMX-ML messages use the UTF-8 encoding, while SDMX-EDI uses the ISO 8879-1 character encoding. There is a greater capacity with UTF-8 to express some character sets (see the “APPENDIX: MAP OF ISO 8859-1 (UNOC) CHARACTER SET (LATIN 1 OR “WESTERN”) in the document “SYNTAX AND DOCUMENTATION VERSION 2.0”.) Many transformation tools are available which allow XML instances with UTF-8 encodings to be expressed as ISO 8879-1-encoded characters, and to transform UTF-8 into ISO 8879-1. Such tools should be used when transforming SDMX-ML messages into SDMX-EDI messages and vice-versa.
++All SDMX-ML messages use the UTF-8 encoding, while SDMX-EDI uses the ISO 8879-1 character encoding. There is a greater capacity with UTF-8 to express some character sets (see the “APPENDIX: MAP OF ISO 8859-1 (UNOC) CHARACTER
++SET      (LATIN     1     OR     “WESTERN”)     in     the     document     “SYNTAX     AND
++
++DOCUMENTATION VERSION 2.0”.) Many transformation tools are available which allow XML instances with UTF-8 encodings to be expressed as ISO 8879-1-encoded characters, and to transform UTF-8 into ISO 8879-1. Such tools should be used when transforming SDMX-ML messages into SDMX-EDI messages and vice-versa.
++
  === //Data Typing// ===
  The XML syntax and EDIFACT syntax have different data-typing mechanisms. The section below provides a set of conventions to be observed when support for messages in both syntaxes is required. For more information on the SDMX-ML representations of data, see below.
--==== 3.3.2 Data Types ====
++==== 3.3.2        Data Types ====
  The XML syntax has a very different mechanism for data-typing than the EDIFACT syntax, and this difference may create some difficulties for applications which support both EDIFACT-based and XML-based SDMX data formats. This section provides a set of conventions for the expression in data in all formats, to allow for clean interoperability between them.
@@ -111,8 +111,7 @@
 *. Maximum 70 characters.
 *. From ISO 8859-1 character set (including accented characters)
 . **Descriptions **are:
--1*. Maximum 350 characters;
--1*. From ISO 8859-1 character set.
++1*. Maximum 350 characters;             From ISO 8859-1 character set.
 . **Code values** are:
 *. Maximum 18 characters;
 *. Any of A..Z (upper case alphabetic), 0..9 (numeric), _ (underscore), / (solidus, slash), = (equal sign), - (hyphen);
@@ -121,43 +121,37 @@
  A..Z (upper case alphabetic), 0..9 (numeric), _ (underscore)
--**5. Observation values** are:
++1. **Observation values** are:
++1*. Decimal numerics (signed only if they are negative);
++1*. The maximum number of significant figures is:
++1*. 15 for a positive number
++1*. 14 for a positive decimal or a negative integer
++1*. 13 for a negative decimal
++1*. Scientific notation may be used.
++1. **Uncoded statistical concept** text values are:
++1*.
++1**. Maximum 1050 characters;
++1**. From ISO 8859-1 character set.
++1. **Time series keys**:
--* Decimal numerics (signed only if they are negative);
--* The maximum number of significant figures is:
--* 15 for a positive number
--* 14 for a positive decimal or a negative integer
--* 13 for a negative decimal
--* Scientific notation may be used.
++In principle, the maximum permissible length of time series keys used in a data exchange does not need to be restricted. However, for working purposes, an effort is made to limit the maximum length to 35 characters; in this length, also (for SDMXEDI) one (separator) position is included between all successive dimension values; this means that the maximum length allowed for a pure series key (concatenation of dimension values) can be less than 35 characters.  The separator character is a colon (“:”) by conventional usage.
--**6. Uncoded statistical concept** text values are:
--
--* Maximum 1050 characters;
--* From ISO 8859-1 character set.
--
--**7. Time series keys**:
--
--In principle, the maximum permissible length of time series keys used in a data exchange does not need to be restricted. However, for working purposes, an effort is made to limit the maximum length to 35 characters; in this length, also (for SDMXEDI) one (separator) position is included between all successive dimension values; this means that the maximum length allowed for a pure series key (concatenation of dimension values) can be less than 35 characters. The separator character is a colon (“:”) by conventional usage.
--
  == 3.4 SDMX-ML and SDMX-EDI Best Practices ==
--=== 3.4.1 Reporting and Dissemination Guidelines ===
++=== 3.4.1        Reporting and Dissemination Guidelines ===
--==== 3.4.1.1 Central Institutions and Their Role in Statistical Data Exchanges ====
++**3.4.1.1 Central Institutions and Their Role in Statistical Data Exchanges **Central institutions are the organisations to which other partner institutions "report" statistics. These statistics are used by central institutions either to compile aggregates and/or they are put together and made available in a uniform manner (e.g. on-line or on a CD-ROM or through file transfers). Therefore, central institutions receive data from other institutions and, usually, they also "disseminate" data to individual and/or institutions for end-use.  Within a country, a NSI or a national central bank (NCB) plays, of course, a central institution role as it collects data from other entities and it disseminates statistical information to end users. In SDMX the role of central institution is very important: every statistical message is based on underlying structural definitions (statistical concepts, code lists, DSDs) which have been devised by a particular agency, usually a central institution. Such an institution plays the role of the reference "structural definitions maintenance agency" for the corresponding messages which are exchanged. Of course, two institutions could exchange data using/referring to structural information devised by a third institution.
--Central institutions are the organisations to which other partner institutions "report" statistics. These statistics are used by central institutions either to compile aggregates and/or they are put together and made available in a uniform manner (e.g. on-line or on a CD-ROM or through file transfers). Therefore, central institutions receive data from other institutions and, usually, they also "disseminate" data to individual and/or institutions for end-use.  Within a country, a NSI or a national central bank (NCB) plays, of course, a central institution role as it collects data from other entities and it disseminates statistical information to end users. In SDMX the role of central institution is very important: every statistical message is based on underlying structural definitions (statistical concepts, code lists, DSDs) which have been devised by a particular agency, usually a central institution. Such an institution plays the role of the reference "structural definitions maintenance agency" for the corresponding messages which are exchanged. Of course, two institutions could exchange data using/referring to structural information devised by a third institution.
--
  Central institutions can play a double role:
  * collecting and further disseminating statistics;
  * devising structural definitions for use in data exchanges.
--==== 3.4.1.2 Defining Data Structure Definitions (DSDs) ====
++**3.4.1.2 Defining Data Structure Definitions (DSDs)**
  The following guidelines are suggested for building a DSD. However, it is expected that these guidelines will be considered by central institutions when devising new DSDs.
--(% class="wikigeneratedid" id="HDimensions2CAttributesandCodeLists" %)
--__Dimensions, Attributes and Code Lists__
++=== Dimensions, Attributes and Code Lists ===
  **//Avoid dimensions that are not appropriate for all the series in the data structure definition.//**  If some dimensions are not applicable (this is evident from the need to have a code in a code list which is marked as “not applicable”, “not relevant” or “total”) for some series then consider moving these series to a new data structure definition in which these dimensions are dropped from the key structure. This is a judgement call as it is sometimes difficult to achieve this without increasing considerably the number of DSDs.
@@ -187,8 +187,7 @@
  The same code list can be used for several statistical concepts, within a data structure definition or across DSDs. Note that SDMX has recognised that these classifications are often quite large and the usage of codes in any one DSD is only a small extract of the full code list. In this version of the standard it is possible to exchange and disseminate a **partial code list** which is extracted from the full code list and which supports the dimension values valid for a particular DSD.
--(% class="wikigeneratedid" id="HDataStructureDefinitionStructure" %)
--__Data Structure Definition Structure__
++=== Data Structure Definition Structure   ===
  The following items have to be specified by a structural definitions maintenance agency when defining a new data structure definition:
@@ -218,7 +218,7 @@
  * code list name
  * code values and descriptions
--Definition of data flow definitions. Two (or more) partners performing data exchanges in a certain context need to agree on:
++Definition of data flow definitions.  Two (or more) partners performing data exchanges in a certain context need to agree on:
  * the list of data set identifiers they will be using;
  * for each data flow:
@@ -225,12 +225,10 @@
  * its content and description
  * the relevant DSD that defines the structure of the data reported or disseminated according the the dataflow definition
--==== 3.4.1.3 Exchanging Attributes ====
++**3.4.1.3 Exchanging Attributes**
--===== //3.4.1.3.1 Attributes on series, sibling and data set level // =====
++**//3.4.1.3.1 Attributes on series, sibling and data set level //**//Static properties//.
--//Static properties//.
--
  * Upon creation of a series the sender has to provide to the receiver values for all mandatory attributes. In case they are available, values for conditional attributes  should also be provided. Whereas initially this information may be provided by means other than SDMX-ML or SDMX-EDI messages (e.g. paper, telephone) it is expected that partner institutions will be in a position to provide this information in SDMX-ML or SDMX-EDI format over time.
  * A centre may agree with its data exchange partners special procedures for authorising the setting of attributes' initial values.
  * Attribute values at a data set level are set and maintained exclusively by the centre administrating the exchanged data set.
@@ -247,21 +247,21 @@
  * If the “observation status” changes and the observation remains unchanged, both components would have to be reported.
  * For Data Structure Definitions having also the observation level attributes “observation confidentiality” and "observation pre-break" defined, this rule applies to these attribute as well: if an institution receives from another institution an observation with an observation status attribute only attached, this means that the associated observation confidentiality and prebreak observation attributes either never existed or from now they do not have a value for this observation.
--=== 3.4.2 Best Practices for Batch Data Exchange ===
++==== 3.4.2        Best Practices for Batch Data Exchange ====
--==== 3.4.2.1 Introduction ====
++**3.4.2.1 Introduction**
  Batch data exchange is the exchange and maintenance of entire databases between counterparties. It is an activity that often employs SDMX-EDI formats, and might also use the SDMX-ML DSD-specific data set. The following points apply equally to both formats.
--==== 3.4.2.2 Positioning of the Dimension "Frequency" ====
++**3.4.2.2 Positioning of the Dimension "Frequency"**
  The position of the “frequency” dimension is unambiguously identified in the data structure definition. Moreover, most central institutions devising structural definitions have decided to assign to this dimension the first position in the key structure. This facilitates the easy identification of this dimension, something that it is necessary to frequency's crucial role in several database systems and in attaching attributes at the “sibling” group level.
--==== 3.4.2.3 Identification of Data Structure Definitions (DSDs) ====
++**3.4.2.3 Identification of Data Structure Definitions (DSDs)**
  In order to facilitate the easy and immediate recognition of the structural definition maintenance agency that defined a data structure definition, most central institutions devising structural definitions use the first characters of the data structure definition identifiers to identify their institution: e.g. BIS_EER, EUROSTAT_BOP_01, ECB_BOP1, etc.
--==== 3.4.2.4 Identification of the Data Flows ====
++**3.4.2.4 Identification of the Data Flows**
  In order to facilitate the easy and immediate recognition of the institution administrating a data flow definitions, many central institutions prefer to use the first characters of the data flow definition identifiers to identify their institution: e.g. BIS_EER, ECB_BOP1, ECB_BOP1, etc. Note that in GESMES/TS the Data Set plays the role of the data flow definition (see //DataSet //in the SDMX-IM//)//.
@@ -269,7 +269,7 @@
  Note that the role of the Data Flow (called //DataflowDefintion// in the model) and Data Set is very specific in the model, and the terminology used may not be the same as used in all organisations, and specifically the term Data Set is used differently in SDMX than in GESMES/TS. Essentially the GESMES/TS term "Data Set" is, in SDMX, the "Dataflow Definition" whist the term "Data Set" in SDMX is used to describe the "container" for an instance of the data.
--==== 3.4.2.5 Special Issues ====
++**3.4.2.5 Special Issues**
  ===== 3.4.2.5.1 "Frequency" related issues =====
@@ -280,6 +280,7 @@
  **//Tick data.//** The issue of data collected at irregular intervals at a higher than daily frequency (e.g. tick-by-tick data) is not discussed here either. However, for data exchange purposes, such series can already be exchanged in the SDMX-EDI format by using the option to send observations with the associated time stamp.
++
  = 4 General Notes for Implementers =
  This section discusses a number of topics other than the exchange of data sets in SDMX-ML and SDMX-EDI. Supported only in SDMX-ML, these topics include the use of the reference metadata mechanism in SDMX, the use of Structure Sets and Reporting Taxonomies, the use of Processes, a discussion of time and data-typing, and some of the conventional mechanisms within the SDMX-ML Structure message  regarding versioning and external referencing.
@@ -290,31 +290,39 @@
  There are several different representations in SDMX-ML, taken from XML Schemas and common programming languages. The table below describes the various representations which are found in SDMX-ML, and their equivalents.
--(% style="width:912.294px" %)
--|(% style="width:172px" %)**SDMX-ML Data Type**|(% style="width:204px" %)**XML Schema Data Type**|(% style="width:189px" %)**.NET Framework Type**|(% style="width:342px" %)(((
--**Java Data Type **
++|**SDMX-ML Data Type**|**XML Schema Data Type**|**.NET Framework Type**|(((
++**Java Data Type**
++
++**~ **
  )))
--|(% style="width:172px" %)String|(% style="width:204px" %)xsd:string|(% style="width:189px" %)System.String|(% style="width:342px" %)java.lang.String
--|(% style="width:172px" %)Big Integer|(% style="width:204px" %)xsd:integer|(% style="width:189px" %)System.Decimal|(% style="width:342px" %)java.math.BigInteg er
--|(% style="width:172px" %)Integer|(% style="width:204px" %)xsd:int|(% style="width:189px" %)System.Int32|(% style="width:342px" %)int
--|(% style="width:172px" %)Long|(% style="width:204px" %)xsd.long|(% style="width:189px" %)System.Int64|(% style="width:342px" %)long
--|(% style="width:172px" %)Short|(% style="width:204px" %)xsd:short|(% style="width:189px" %)System.Int16|(% style="width:342px" %)short
--|(% style="width:172px" %)Decimal|(% style="width:204px" %)xsd:decimal|(% style="width:189px" %)System.Decimal|(% style="width:342px" %)java.math.BigDecim al
--|(% style="width:172px" %)Float|(% style="width:204px" %)xsd:float|(% style="width:189px" %)System.Single|(% style="width:342px" %)float
--|(% style="width:172px" %)Double|(% style="width:204px" %)xsd:double|(% style="width:189px" %)System.Double|(% style="width:342px" %)double
--|(% style="width:172px" %)Boolean|(% style="width:204px" %)xsd:boolean|(% style="width:189px" %)System.Boolean|(% style="width:342px" %)boolean
--|(% style="width:172px" %)URI|(% style="width:204px" %)xsd:anyURI|(% style="width:189px" %)System.Uri|(% style="width:342px" %)Java.net.URI or java.lang.String
--|(% style="width:172px" %)DateTime|(% style="width:204px" %)xsd:dateTime|(% style="width:189px" %)System.DateTime|(% style="width:342px" %)javax.xml.datatype .XMLGregorianCalen dar
--|(% style="width:172px" %)Time|(% style="width:204px" %)xsd:time|(% style="width:189px" %)System.DateTime|(% style="width:342px" %)javax.xml.datatype .XMLGregorianCalen dar
--|(% style="width:172px" %)GregorianYear|(% style="width:204px" %)xsd:gYear|(% style="width:189px" %)System.DateTime|(% style="width:342px" %)javax.xml.datatype .XMLGregorianCalen dar
--|(% style="width:172px" %)GregorianMonth|(% style="width:204px" %)xsd:gYearMonth|(% style="width:189px" %)System.DateTime|(% style="width:342px" %)javax.xml.datatype .XMLGregorianCalen dar
--|(% style="width:172px" %)GregorianDay|(% style="width:204px" %)xsd:date|(% style="width:189px" %)System.DateTime|(% style="width:342px" %)javax.xml.datatype .XMLGregorianCalen dar
--|(% style="width:172px" %)(((
--Day, MonthDay, Month
--)))|(% style="width:204px" %)xsd:g*|(% style="width:189px" %)System.DateTime|(% style="width:342px" %)javax.xml.datatype .XMLGregorianCalen dar
--|(% style="width:172px" %)Duration|(% style="width:204px" %)xsd:duration |(% style="width:189px" %)System.TimeSpa|(% style="width:342px" %)javax.xml.datatype
--|(% style="width:172px" %) |(% style="width:204px" %) |(% style="width:189px" %)n|(% style="width:342px" %).Duration
++|String|xsd:string|System.String|java.lang.String
++|Big Integer|xsd:integer|System.Decimal|java.math.BigInteg er
++|Integer|xsd:int|System.Int32|int
++|Long|xsd.long|System.Int64|long
++|Short|xsd:short|System.Int16|short
++|Decimal|xsd:decimal|System.Decimal|java.math.BigDecim al
++|Float|xsd:float|System.Single|float
++|Double|xsd:double|System.Double|double
++|Boolean|xsd:boolean|System.Boolean|boolean
++|URI|xsd:anyURI|System.Uri|Java.net.URI or java.lang.String
++|DateTime|xsd:dateTime|System.DateTim e|javax.xml.datatype .XMLGregorianCalen dar
++|Time|xsd:time|System.DateTim e|javax.xml.datatype .XMLGregorianCalen dar
++|GregorianYear|xsd:gYear|System.DateTim e|javax.xml.datatype .XMLGregorianCalen dar
++|GregorianMont h|xsd:gYearMont h|System.DateTim e|javax.xml.datatype .XMLGregorianCalen dar
++|GregorianDay|xsd:date|System.DateTim e|javax.xml.datatype .XMLGregorianCalen dar
++|(((
++Day,
++MonthDay, Month
++)))|xsd:g*|System.DateTim e|javax.xml.datatype .XMLGregorianCalen dar
++|Duration|xsd:duration |System.TimeSpa|javax.xml.datatype
++|**SDMX-ML Data Type**|**XML Schema Data Type**|**.NET Framework Type**|(((
++**Java Data Type**
++
++**~ **
++)))
++| | |n|.Duration
++
  There are also a number of SDMX-ML data types which do not have these direct correspondences, often because they are composite representations or restrictions of a broader data type. For most of these, there are simple types which can be referenced from the SDMX schemas, for others a derived simple type will be necessary:
  * AlphaNumeric (common:AlphaNumericType, string which only allows A-z and 0-9)
@@ -340,8 +340,10 @@
  * KeyValues (common:DataKeyType)
  * IdentifiableReference (types for each identifiable object)
  * DataSetReference (common:DataSetReferenceType)
--* AttachmentConstraintReference (common:AttachmentConstraintReferenceType)
++* AttachmentConstraintReference
++(common:AttachmentConstraintReferenceType)
++
  Data types also have a set of facets:
  * isSequence = true | false (indicates a sequentially increasing value)
@@ -363,7 +363,7 @@
  == 4.2 Time and Time Format ==
--=== 4.2.1 Introduction ===
++==== 4.2.1 Introduction  ====
  First, it is important to recognize that most observation times are a period. SDMX specifies precisely how Time is handled.
@@ -371,14 +371,15 @@
  The hierarchy of time formats is as follows (**bold** indicates a category which is made up of multiple formats, //italic// indicates a distinct format):
--* **Observational Time Period**
--** **Standard Time Period**
--*** **Basic Time Period**
--**** **Gregorian Time Period**
--**** //Date Time//
--*** **Reporting Time Period**
--** //Time Range//
++* **Observational Time Period **o **Standard Time Period**
++                                                        §    **Basic Time Period**
++
++* **Gregorian Time Period**
++* //Date Time//
++
++§   **Reporting Time Period **o //Time Range//
++
  The details of these time period categories and of the distinct formats which make them up are detailed in the sections to follow.
  ==== 4.2.2 Observational Time Period ====
@@ -411,7 +411,7 @@
  This is used to unambiguously state that a date-time represents an observation at a single point in time. Therefore, if one wants to use SDMX for data which is measured at a distinct point in time rather than being reported over a period, the date-time representation can be used.
--Representation: xs:dateTime (YYYY-MM-DDThh:mm:ss)[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[1~]^^>>path:#_ftn1]]
++Representation: xs:dateTime (YYYY-MM-DDThh:mm:ss)[[^^~[1~]^^>>path:#_ftn1]]
  ==== 4.2.6 Standard Reporting Period ====
@@ -479,7 +479,7 @@
  Representation: common:ReportingWeekType (YYYY-Www, e.g. 2000-W53)
--Notes: There are either 52 or 53 weeks in a reporting year. This is based on the ISO 8601 definition of a week (Monday - Saturday), where the first week of a reporting year is defined as the week with the first Thursday on or after the reporting year start day.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[2~]^^>>path:#_ftn2]](%%) The reporting week is always represented as two digits, therefore 1-9 are 0 padded (e.g. 01). This allows the values to be sorted chronologically using textual sorting methods.
++Notes: There are either 52 or 53 weeks in a reporting year. This is based on the ISO 8601 definition of a week (Monday - Saturday), where the first week of a reporting year is defined as the week with the first Thursday on or after the reporting year start day.[[^^~[2~]^^>>path:#_ftn2]] The reporting week is always represented as two digits, therefore 1-9 are 0 padded (e.g. 01). This allows the values to be sorted chronologically using textual sorting methods.
  **Reporting Day**:
@@ -507,16 +507,16 @@
  **a) If the [PERIOD_INDICATOR] is W:**
--1.
--11.
--111.
++1.
++11.
++111.
 . **If [REPORTING_YEAR_START_DATE] is a Friday, Saturday, or Sunday:**
  Add^^3^^ (P3D, P2D, or P1D respectively) to the [REPORTING_YEAR_START_DATE]. The result is the [REPORTING_YEAR_BASE].
--1.
--11.
--111.
++1.
++11.
++111.
 . **If [REPORTING_YEAR_START_DATE] is a Monday, Tuesday, Wednesday, or Thursday:**
  Add^^3^^ (P0D, -P1D, -P2D, or -P3D respectively) to the [REPORTING_YEAR_START_DATE]. The result is the [REPORTING_YEAR_BASE].
@@ -526,7 +526,7 @@
  The [REPORTING_YEAR_START_DATE] is the [REPORTING_YEAR_BASE].
 . **Determine [PERIOD_DURATION]:**
--11.
++11.
 . If the [PERIOD_INDICATOR] is A, the [PERIOD_DURATION] is P1Y.
 . If the [PERIOD_INDICATOR] is S, the [PERIOD_DURATION] is P6M.
 . If the [PERIOD_INDICATOR] is T, the [PERIOD_DURATION] is P4M.
@@ -536,7 +536,7 @@
 . If the [PERIOD_INDICATOR] is D, the [PERIOD_DURATION] is P1D.
 . **Determine [PERIOD_START]:**
--Subtract one from the [PERIOD_VALUE] and multiply this by the [PERIOD_DURATION]. Add[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[3~]^^>>path:#_ftn3]](%%) this to the [REPORTING_YEAR_BASE]. The result is the [PERIOD_START].
++Subtract one from the [PERIOD_VALUE] and multiply this by the [PERIOD_DURATION]. Add[[^^~[3~]^^>>path:#_ftn3]] this to the [REPORTING_YEAR_BASE]. The result is the [PERIOD_START].
 . **Determine the [PERIOD_END]:**
@@ -635,7 +635,7 @@
  When converting data from SDMX-EDI to SDMX-ML, the source time format is only necessary to determine the format of the target time value. For example, a source time format of will result in a target time in the format YYYY-Ss whereas a source format of will result in a target time value in the format YYYY-Qq.
--==== 4.2.10    Time Zones ====
++====                 4.2.10    Time Zones ====
  In alignment with ISO 8601, SDMX allows the specification of a time zone on all time periods and on the reporting year start day. If a time zone is provided on a reporting year start day, then the same time zone (or none) should be reported for each reporting time period. If the reporting year start day and the reporting period time zone differ, the time zone of the reporting period will take precedence. Examples of each format with time zones are as follows (time zone indicated in bold):
@@ -656,7 +656,7 @@
  According to ISO 8601, a date without a time-zone is considered "local time". SDMX assumes that local time is that of the sender of the message. In this version of SDMX, an optional field is added to the sender definition in the header for specifying a time zone. This field has a default value of 'Z' (UTC). This determination of local time applies for all dates in a message.
--==== 4.2.11    Representing Time Spans Elsewhere ====
++====                 4.2.11    Representing Time Spans Elsewhere ====
  It has been possible since SDMX 2.0 for a Component to specify a representation of a time span. Depending on the format of the data message, this resulted in either an element with 2 XML attributes for holding the start time and the duration or two separate XML attributes based on the underlying Component identifier. For example if REF_PERIOD were given a representation of time span, then in the Compact data format, it would be represented by two XML attributes; REF_PERIODStartTime (holding the start) and REF_PERIOD (holding the duration). If a new simple type is introduced in the SDMX schemas that can hold ISO 8601 time intervals, then this will no longer be necessary. What was represented as this:
@@ -666,11 +666,11 @@
  <Series REF_PERIOD="2000-01-01T00:00:00/P2M"/>
--==== 4.2.12    Notes on Formats ====
++====                 4.2.12    Notes on Formats ====
  There is no ambiguity in these formats so that for any given value of time, the category of the period (and thus the intended time period range) is always clear. It should also be noted that by utilizing the ISO 8601 format, and a format loosely based on it for the report periods, the values of time can easily be sorted chronologically without additional parsing.
--==== 4.2.13    Effect on Time Ranges ====
++====                 4.2.13    Effect on Time Ranges ====
  All SDMX-ML data messages are capable of functioning in a manner similar to SDMX-EDI if the Dimension at the observation level is time: the time period for the first observation can be stated and the rest of the observations can omit the time value as it can be derived from the start time and the frequency. Since the frequency can be determined based on the actual format of the time value for everything but distinct points in time and time ranges, this makes is even simpler to process as the interval between time ranges is known directly from the time value.
@@ -749,7 +749,7 @@
   2010-D185 or later (reporting year start day ~-~-07-01)
--== 4.3 Structural Metadata Querying Best Practices ==
++== 4.3 Structural Metadata Querying Best Practices  ==
  When querying for structural metadata, the ability to state how references should be resolved is quite powerful. However, this mechanism is not always necessary and can create an undue burden on the systems processing the queries if it is not used properly.
@@ -757,7 +757,7 @@
  When the referenced object is not known, then the reference resolution mechanism could be used. For example, suppose one wanted to find all category schemes and the related categorisations for a given maintenance agency. In this case, one could query for the category scheme by the maintenance agency and specify that parent and sibling references should be resolved. This would result in the categorisations which reference the categories in the matched schemes to be returned, as well as the object which they categorise.
--== 4.4 Versioning and External Referencing ==
++== 4.4 Versioning and External Referencing  ==
  Within the SDMX-ML Structure Message, there is a pattern for versioning and external referencing which should be pointed out. The identifiers are qualified by their version numbers – that is, an object with an Agency of “A”, and ID of “X” and a version of “1.0” is a different object than one with an Agency of “A’, an ID of “X”, and a version of “1.1”.
@@ -1172,12 +1172,12 @@
 . Restricts the code list for the CAS Dimension to codes TOT and NAP.
 . Inherits the AGE constraint applied at the level of the DSD.
--=== Provision Agreements CENSUS_CUBE1_IT ===
++=== Provision Agreements CENSUS_CUBE1_IT  ===
 . Restricts the codes for the GEO Dimension to IT and its children.
 . Inherits the constraints from Dataflow CENSUS_CUBE1  for the AGE and CAS Dimensions.
--=== Provision Agreements CENSUS_CUBE2_IT ===
++=== Provision Agreements CENSUS_CUBE2_IT  ===
 . Restricts the codes for the GEO Dimension to IT and its children.
 . Inherits the constraints from Dataflow CENSUS_CUBE2 for the CAS Dimension.
@@ -1207,7 +1207,7 @@
  == 9.2 Groups and Dimension Groups ==
--=== 9.2.1      Issue ===
++===                    9.2.1      Issue ===
  Version 2.1 introduces a more granular mechanism for specifying the relationship between a Data Attribute and the Dimensions to which the attribute applies. The technical construct for this is the Dimension Group. This Dimension Group has no direct equivalent in versions 2.0 and 1.0 and so the application transforming data from a version 2.1 data set to a version 2.0 or version 1.0 data set must decide to which construct the attribute value, whose Attribute is declared in a Dimension Group, should be attached. The closest construct is the “Series” attachment level and in many cases this is the correct construct to use.
@@ -1232,7 +1232,7 @@
  == 10.1 Introduction ==
--The Validation and Transformation Language (VTL) supports the definition of Transformations, which are algorithms to calculate new data starting from already existing ones[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[4~]^^>>path:#_ftn4]](%%). The purpose of the VTL in the SDMX context is to enable the:
++The Validation and Transformation Language (VTL) supports the definition of Transformations, which are algorithms to calculate new data starting from already existing ones[[^^~[4~]^^>>path:#_ftn4]]. The purpose of the VTL in the SDMX context is to enable the:
  * definition of validation and transformation algorithms, in order to specify how to calculate new data  from existing ones;
  * exchange of the definition of VTL algorithms, also together the definition of the data structures of the involved data (for example, exchange the data structures of a reporting framework together with the validation rules to be applied, exchange the input and output data structures of a calculation task together with the VTL Transformations describing the calculation algorithms);
@@ -1246,7 +1246,7 @@
  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.
--== 10.2 References to SDMX artefacts from VTL statements ==
++== 10.2 References to SDMX artefacts from VTL statements  ==
  === 10.2.1 Introduction ===
@@ -1256,7 +1256,7 @@
  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[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[5~]^^>>path:#_ftn5]](%%) or user defined operators[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[6~]^^>>path:#_ftn6]](%%)  to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping.
++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[[^^~[5~]^^>>path:#_ftn5]] or user defined operators[[^^~[6~]^^>>path:#_ftn6]]  to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping.
  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.
@@ -1266,15 +1266,15 @@
  This approach has the advantage that in the VTL code the URN of the referenced artefacts is directly intelligible by a human reader but has the drawback that the references are verbose.
--The SDMX URN[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[7~]^^>>path:#_ftn7]](%%) is the concatenation of the following parts, separated by special symbols like dot, equal, asterisk, comma, and parenthesis:^^ ^^
++The SDMX URN[[^^~[7~]^^>>path:#_ftn7]] is the concatenation of the following parts, separated by special symbols like dot, equal, asterisk, comma, and parenthesis:^^ ^^
--* SDMXprefix
--* SDMX-IM-package-name
--* class-name
--* agency-id
++* SDMXprefix
++* SDMX-IM-package-name
++* class-name
++* agency-id
  * maintainedobject-id
  * maintainedobject-version
--* container-object-id [[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[8~]^^>>path:#_ftn8]]
++* container-object-id [[^^~[8~]^^>>path:#_ftn8]]
  * object-id
  The generic structure of the URN is the following:
@@ -1293,7 +1293,7 @@
  The **agency-id** is the acronym of the agency that owns the definition of the artefact, for example for the Eurostat artefacts the agency-id is “ESTAT”). The agency-id can be composite (for example AgencyA.Dept1.Unit2).
--The **maintainedobject-id** is the name of the maintained object which the artefact belongs to, and in case the artefact itself is maintainable[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[9~]^^>>path:#_ftn9]](%%), coincides with the name of the artefact. Therefore the maintainedobject-id depends on the class of the artefact:
++The **maintainedobject-id** is the name of the maintained object which the artefact belongs to, and in case the artefact itself is maintainable[[^^~[9~]^^>>path:#_ftn9]], coincides with the name of the artefact. Therefore the maintainedobject-id depends on the class of the artefact:
  * if the artefact is a ,,Dataflow,,, which is a maintainable class,  the maintainedobject-id is the Dataflow name (dataflow-id);
  * if the artefact is a Dimension, MeasureDimension, TimeDimension, PrimaryMeasure or DataAttribute, which are not maintainable and belong to the ,,DataStructure,, maintainable class, the maintainedobject-id is the name of the DataStructure (dataStructure-id) which the artefact belongs to;
@@ -1313,7 +1313,7 @@
  * if the artefact is a ,,Concept ,,(the object-id is the name of the ,,Concept,,)
--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 and their Agency is AG, would be written as[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[10~]^^>>path:#_ftn10]](%%):
++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 and their Agency is AG, would be written as[[^^~[10~]^^>>path:#_ftn10]]:
  ‘urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DFR(1.0)’  <-
@@ -1329,21 +1329,21 @@
  * The **SDMXPrefix** can be omitted for all the SDMX objects, because it is a prefixed string (urn:sdmx:org), always the same for SDMX objects.
  * The **SDMX-IM-package-name **can be omitted as well because it can be deduced from the class-name that follows it (the table of the SDMX-IM packages and classes that allows this deduction is in the SDMX 2.1 Standards - Section 5 -  Registry Specifications, paragraph 6.2.3). In particular, considering the object classes of the artefacts that VTL can reference, the package is:
--** “datastructure” for the classes Dataflow, Dimension, MeasureDimension, TimeDimension, PrimaryMeasure, DataAttribute,
++** “datastructure” for the classes Dataflow, Dimension, MeasureDimension, TimeDimension, PrimaryMeasure, DataAttribute,
  ** “conceptscheme” for the classes  Concept and ConceptScheme o “codelist” for the class Codelist.
--* The **class-name** can be omitted as it can be deduced from the VTL invocation.  In particular, starting from the VTL class of the invoked artefact (e.g. dataset, component, identifier, measure, attribute, variable, valuedomain),  which is known given the syntax of the invoking VTL operator[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[11~]^^>>path:#_ftn11]](%%), the SDMX class can be deduced from the mapping rules between VTL and SDMX (see the section “Mapping between VTL and SDMX” hereinafter)[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[12~]^^>>path:#_ftn12]](%%).
--* 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 agency-id can be omitted if it is the same as the invoking T,,ransformationScheme,, and cannot be omitted if the artefact comes from another agency.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[13~]^^>>path:#_ftn13]](%%)  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).
++* 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[[^^~[11~]^^>>path:#_ftn11]], the SDMX class can be deduced from the mapping rules between VTL and SDMX (see the section “Mapping between VTL and SDMX” hereinafter)[[^^~[12~]^^>>path:#_ftn12]].
++* 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 agency-id can be omitted if it is the same as the invoking T,,ransformationScheme,, and cannot be omitted if the artefact comes from another agency.[[^^~[13~]^^>>path:#_ftn13]]  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).
  * 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;
  ** if the referenced artefact is a Dimension, MeasureDimension, TimeDimension, PrimaryMeasure, DataAttribute, which are not maintainable and belong to the ,,DataStructure,, maintainable class, the maintainedobject-id is the dataStructure-id and can be omitted, given that these components are always invoked within the invocation of a ,,Dataflow,,, whose dataStructure-id can be deduced from the
  SDMX structural definitions;  o if the referenced artefact is a ,,Concept, ,,which is not maintainable and belong to the ,,ConceptScheme ,,maintainable class,,, ,,the maintained object is the conceptScheme-id and cannot be omitted;
--*
++*
  ** if the referenced artefact is a ,,ConceptScheme, ,,which is a,, ,,maintainable class,,, ,,the maintained object is the ,,conceptScheme-id,, and obviously cannot be omitted;
  ** if the referenced artefact is a ,,Codelist, ,,which is a maintainable class, the maintainedobject-id is the ,,codelist-id,, and obviously cannot be omitted.
  * When the maintainedobject-id is omitted, the **maintainedobject-version** is omitted too. When the maintainedobject-id is not omitted and the maintainedobject-version is omitted, the version 1.0 is assumed by default.,, ,,
  * As said, the **container-object-id** does not apply to the classes that can be referenced in VTL transformations, therefore is not present in their URN
--* The **object-id** does not exist for the artefacts belonging to the ,,Dataflow, ConceptScheme,, and ,,Codelist,, classes, while it exists and cannot be omitted for the artefacts belonging to the classes Dimension, MeasureDimension, TimeDimension, PrimaryMeasure, DataAttribute and Concept, as for
++* The **object-id** does not exist for the artefacts belonging to the ,,Dataflow,,,,, ConceptScheme,, and ,,Codelist,, classes, while it exists and cannot be omitted for the artefacts belonging to the classes Dimension, MeasureDimension, TimeDimension, PrimaryMeasure, DataAttribute and Concept, as for
  them the object-id is the main identifier of the artefact
@@ -1355,15 +1355,15 @@
  ‘urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF2(1.0)’
--by omitting all the non-essential parts would become simply:
++by omitting all the non-essential parts would become simply:
  DFR  :=  DF1 + DF2
--The references to the ,,Codelists,, can be simplified similarly. For example, given the non-abbreviated reference to the ,,Codelist,,  AG:CL_FREQ(1.0), which is[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[14~]^^>>path:#_ftn14]](%%):
++The references to the ,,Codelists,, can be simplified similarly. For example, given the non-abbreviated reference to the ,,Codelist,,  AG:CL_FREQ(1.0), which is[[^^~[14~]^^>>path:#_ftn14]]:
  ‘urn:sdmx:org.sdmx.infomodel.codelist.Codelist=AG:CL_FREQ(1.0)’
--if the ,,Codelist,, is referenced from a ruleset scheme belonging to the agency AG, omitting all the optional parts, the abbreviated reference would become simply[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[15~]^^>>path:#_ftn15]](%%):
++if the ,,Codelist,, is referenced from a ruleset scheme belonging to the agency AG, omitting all the optional parts, the abbreviated reference would become simply[[^^~[15~]^^>>path:#_ftn15]]:
  CL_FREQ
@@ -1373,7 +1373,7 @@
  SECTOR
--For example, the transformation for renaming the component SECTOR of the dataflow DF1 into SEC can be written as[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[16~]^^>>path:#_ftn16]](%%):
++For example, the transformation for renaming the component SECTOR of the dataflow DF1 into SEC can be written as[[^^~[16~]^^>>path:#_ftn16]]:
  ‘DFR(1.0)’ := ‘DF1(1.0)’ [rename SECTOR to SEC]
@@ -1407,9 +1407,9 @@
  The VTL Rulesets have a signature, in which the Value Domains or the Variables on which the Ruleset is defined are declared, and a body, which contains the rules.
--In the signature, given the mapping between VTL and SDMX better described in the following paragraphs, a reference to a VTL Value Domain becomes a reference to a SDMX Codelist or to a SDMX ConceptScheme (for SDMX measure dimensions), while a reference to a VTL Represented Variable becomes a reference to a SDMX Concept, assuming for it a definite representation[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[17~]^^>>path:#_ftn17]](%%).
++In the signature, given the mapping between VTL and SDMX better described in the following paragraphs, a reference to a VTL Value Domain becomes a reference to a SDMX Codelist or to a SDMX ConceptScheme (for SDMX measure dimensions), while a reference to a VTL Represented Variable becomes a reference to a SDMX Concept, assuming for it a definite representation[[^^~[17~]^^>>path:#_ftn17]].
--In general, for referencing SDMX Codelists and Concepts, the conventions described in the previous paragraphs apply. In the Ruleset syntax, the elements that reference SDMX artefacts are called “valueDomain” and “variable” for the Datapoint Rulesets and “ruleValueDomain”, “ruleVariable”, “condValueDomain” “condVariable” for the Hierarchical Rulesets). The syntax of the Ruleset signature allows also to define aliases of the elements above, these aliases are valid only within the specific ruleset definition statement and cannot be mapped to SDMX.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[18~]^^>>path:#_ftn18]](%%)
++In general, for referencing SDMX Codelists and Concepts, the conventions described in the previous paragraphs apply. In the Ruleset syntax, the elements that reference SDMX artefacts are called “valueDomain” and “variable” for the Datapoint Rulesets and “ruleValueDomain”, “ruleVariable”, “condValueDomain” “condVariable” for the Hierarchical Rulesets). The syntax of the Ruleset signature allows also to define aliases of the elements above, these aliases are valid only within the specific ruleset definition statement and cannot be mapped to SDMX.[[^^~[18~]^^>>path:#_ftn18]]
  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, ConceptScheme, Concept) to can be deduced from the Ruleset signature.
@@ -1423,15 +1423,15 @@
  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.
--In the opposite direction, every time an object calculated by means of VTL must be treated as a SDMX object (for example for exchanging it through SDMX), there is the need of a SDMX definition of the object, so that the SDMX operations can take place.  The SDMX definition is needed for the VTL objects for which a SDMX use is envisaged[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[19~]^^>>path:#_ftn19]](%%).
++In the opposite direction, every time an object calculated by means of VTL must be treated as a SDMX object (for example for exchanging it through SDMX), there is the need of a SDMX definition of the object, so that the SDMX operations can take place.  The SDMX definition is needed for the VTL objects for which a SDMX use is envisaged[[^^~[19~]^^>>path:#_ftn19]].
  The mapping methods from VTL to SDMX are described in the following paragraphs as well, however they do not allow the complete SDMX definition to be automatically deduced from the VTL definition,  more than all because the former typically contains additional information in respect to the latter. For example, the definition of a SDMX DSD includes also some mandatory information not available in VTL (like the concept scheme to which the SDMX components refer, the assignmentStatus and attributeRelationship for the DataAttributes and so on). Therefore the mapping methods from VTL to SDMX provide only a general guidance for generating SDMX definitions properly starting from the information available in VTL, independently of how the SDMX definition it is actually generated (manually, automatically or part and part).
  === 10.3.2 General mapping of VTL and SDMX data structures ===
--This section makes reference to the VTL “Model for data and their structure”[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[20~]^^>>path:#_ftn20]](%%) and the correspondent SDMX “Data Structure Definition”[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[21~]^^>>path:#_ftn21]](%%).
++This section makes reference to the VTL “Model for data and their structure”[[^^~[20~]^^>>path:#_ftn20]] and the correspondent SDMX “Data Structure Definition”[[^^~[21~]^^>>path:#_ftn21]].
--The main type of artefact that the VTL can manipulate is the VTL Data Set, which in general is mapped to the SDMX Dataflow. This means that a VTL Transformation, in the SDMX context, expresses the algorithm for calculating a derived Dataflow starting from some already existing Dataflows (either collected or derived).[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[22~]^^>>path:#_ftn22]](%%)
++The main type of artefact that the VTL can manipulate is the VTL Data Set, which in general is mapped to the SDMX Dataflow. This means that a VTL Transformation, in the SDMX context, expresses the algorithm for calculating a derived Dataflow starting from some already existing Dataflows (either collected or derived).[[^^~[22~]^^>>path:#_ftn22]]
  While the VTL Transformations are defined in term of Dataflow definitions, they are assumed to be executed on instances of such Dataflows, provided at runtime to the VTL engine (the mechanism for identifying the instances to be processed are not part of the VTL specifications and depend on the implementation of the VTL-based systems).  As already said, the SDMX Datasets are instances of SDMX Dataflows, therefore a VTL Transformation defined on some SDMX Dataflows can be applied on some corresponding SDMX Datasets.
@@ -1441,7 +1441,7 @@
  SDMX DimensionComponent can be a Dimension, a TimeDimension or a MeasureDimension. Correspondingly, in the SDMX implementation of the VTL, the VTL Identifiers can be (optionally) distinguished in three sub-classes (Simple Identifier, Time Identifier, Measure Identifier) even if such a distinction is not evidenced in the VTL IM.
--However, a VTL Data Structure can have any number of Identifiers, Measures and Attributes, while a SDMX 2.1 DataStructureDefinition can have any number of Dimensions and DataAttributes but just one PrimaryMeasure[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[23~]^^>>path:#_ftn23]](%%). This is due to a difference between SDMX 2.1 and VTL in the possible representation methods of the data that contain more measures.
++However, a VTL Data Structure can have any number of Identifiers, Measures and Attributes, while a SDMX 2.1 DataStructureDefinition can have any number of Dimensions and DataAttributes but just one PrimaryMeasure[[^^~[23~]^^>>path:#_ftn23]]. This is due to a difference between SDMX 2.1 and VTL in the possible representation methods of the data that contain more measures.
  As for SDMX, because the data structure cannot contain more than one measure component (i.e., the primaryMeasure), the representation of data having more measures is possible only by means of a particular dimension, called MeasureDimension, which is aimed at containing the name of the measure concepts, so that for each observation the value contained in the PrimaryMeasure component is the value of the measure concept reported in the MeasureDimension component.
@@ -1505,7 +1505,7 @@
   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 Concept Cj of the SDMX MeasureDimension;
--*
++*
  ** 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.
  ** The value of the PrimaryMeasure 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
@@ -1512,7 +1512,7 @@
  **10.3.3.3 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 Attributes.
  The Basic_A2M and Pivot_A2M behaves respectively like the Basic and Pivot methods, except that the final VTL components, which according to the Basic and Pivot methods would have had the role of Attribute, assume instead the role of Measure.
@@ -1531,7 +1531,7 @@
  This mapping method cannot be applied for SDMX 2.1 if the VTL data structure has more than one measure component, given that the SDMX 2.1 DataStructureDefinition allows just one measure component (the
--PrimaryMeasure). In this case it becomes mandatory to specify a different 1958 mapping method through the VtlMappingScheme and VtlDataflowMapping 1959 classes.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[24~]^^>>path:#_ftn24]](%%)
++PrimaryMeasure). In this case it becomes mandatory to specify a different 1958 mapping method through the VtlMappingScheme and VtlDataflowMapping 1959 classes.[[^^~[24~]^^>>path:#_ftn24]]
 Please note that the VTL measures can have any name while in SDMX 2.1 the 1961 MeasureComponent has the mandatory name “obs_value”, therefore the name of the VTL measure name must become “obs_value” in SDMX 2.1.
@@ -1598,7 +1598,7 @@
   the values of the VTL identifiers become the values of the corresponding SDMX Dimensions, for all the observations of the set above
--*
++*
  ** the name of the j^^th^^ VTL measure (e.g. “Cj”) becomes the value of the SDMX MeasureDimension of the j^^th^^ observation of the set (i.e. the Concept Cj)
  ** the value of the j^^th^^ VTL measure becomes the value of the SDMX PrimaryMeasure of the j^^th^^ observation of the set
  ** the values of the VTL Attributes become the values of the corresponding SDMX DataAttributes (in principle for all the observations of the set above)
@@ -1648,15 +1648,15 @@
   The VtlMappingScheme is a container for zero or more VtlDataflowMapping (besides possible mappings to artefacts other than dataflows).
--=== 10.3.6 Mapping dataflow subsets to distinct VTL data sets[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^**~[25~]**^^>>path:#_ftn25]](%%) ===
++=== 10.3.6 Mapping dataflow subsets to distinct VTL data sets[[^^**~[25~]**^^>>path:#_ftn25]]  ===
  Until now it as been assumed to map one SMDX Dataflow to one VTL dataset 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
 ), 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.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[26~]^^>>path:#_ftn26]](%%)
++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.[[^^~[26~]^^>>path:#_ftn26]]
--Therefore, in order to make the coding of  VTL operations simpler when applied on parts of SDMX Dataflows, it is allowed to map distinct parts of a SDMX Dataflow to distinct VTL data sets according to the following rules and conventions. This kind of mapping is possible both from SDMX to VTL and from VTL to SDMX, as better explained below.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[27~]^^>>path:#_ftn27]](%%)
++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.[[^^~[27~]^^>>path:#_ftn27]]
                     Given    a      SDMX Dataflow      and    some      predefined Dimensions      of    its
@@ -1668,14 +1668,14 @@
  In practice, this kind mapping is obtained like follows:
--* For a given SDMX dataflow, the user (VTL definer) declares  the dimension components on which the mapping will be based, in a given order.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[28~]^^>>path:#_ftn28]](%%) Following the example above, imagine that the user declares the dimensions INDICATOR and COUNTRY.
++* For a given SDMX dataflow, the user (VTL definer) declares  the dimension components on which the mapping will be based, in a given order.[[^^~[28~]^^>>path:#_ftn28]] Following the example above, imagine that the user declares the dimensions INDICATOR and COUNTRY.
  * The VTL dataset is given a name using a special notation also called “ordered concatenation” and composed of the following parts:
  ** The reference to the SDMX dataflow (expressed according to the rules described in the previous paragraphs, i.e. URN, abbreviated
--URN or another alias); for example DF(1.0); o a slash (“/”) as a separator; [[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[29~]^^>>path:#_ftn29]]
++URN or another alias); for example DF(1.0); o a slash (“/”) as a separator; [[^^~[29~]^^>>path:#_ftn29]]
--*
--** The reference to a specific part of the SDMX dataflow above, expressed as the concatenation of the values that the SDMX dimensions declared above must have, separated by dots (“.”) and written in the order in which these dimensions are defined[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[30~]^^>>path:#_ftn30]](%%) . For example  POPULATION.USA would mean that such a VTL dataset is mapped to the SDMX observations for which the dimension  //INDICATOR// is equal to POPULATION and the dimension //COUNTRY// is equal to USA.
++*
++** The reference to a specific part of the SDMX dataflow above, expressed as the concatenation of the values that the SDMX dimensions declared above must have, separated by dots (“.”) and written in the order in which these dimensions are defined[[^^~[30~]^^>>path:#_ftn30]] . For example  POPULATION.USA would mean that such a VTL dataset is mapped to the SDMX observations for which the dimension  //INDICATOR// is equal to POPULATION and the dimension //COUNTRY// is equal to USA.
  In the VTL transformations, this kind of dataset name must be referenced between single quotes because the slash (“/”) is not a regular character according to the VTL rules.
@@ -1693,7 +1693,7 @@
  Let us now analyse the different meaning of this kind of mapping in the two mapping directions, i.e. from SDMX to VTL and from VTL to SDMX.
--As already said, the mapping from SDMX to VTL happens when the VTL datasets are operand of VTL transformations, instead the mapping from VTL to SDMX happens when the VTL datasets are result of VTL transformations[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[31~]^^>>path:#_ftn31]](%%) and need to be treated as SDMX objects. This kind of mapping can be applied independently in the two directions and the Dimensions on which the mapping is based can be different in the two directions: these Dimensions are defined in the ToVtlSpaceKey and in the FromVtlSpaceKey classes respectively.
++As already said, the mapping from SDMX to VTL happens when the VTL datasets are operand of VTL transformations, instead the mapping from VTL to SDMX happens when the VTL datasets are result of VTL transformations[[^^~[31~]^^>>path:#_ftn31]] and need to be treated as SDMX objects. This kind of mapping can be applied independently in the two directions and the Dimensions on which the mapping is based can be different in the two directions: these Dimensions are defined in the ToVtlSpaceKey and in the FromVtlSpaceKey classes respectively.
  First, let us see what happens in the mapping direction from SDMX to VTL, i.e. when parts of a SDMX dataflow (e.g. DF1(1.0)) need to be mapped to distinct VTL datasets that are operand of some VTL transformations.
@@ -1703,7 +1703,7 @@
  //COUNTRYvalue//. For example, the VTL dataset ‘DF1(1.0)/POPULATION.USA’ would contain all the observations of DF1(1.0) having INDICATOR = POPULATION and COUNTRY = USA.
--In order to obtain the data structure of these VTL datasets from the SDMX one, it is assumed that the SDMX dimensions 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 datasets[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[32~]^^>>path:#_ftn32]](%%). After that, the mapping method from SDMX to VTL specified for the dataflow DF1(1.0) is applied (i.e. basic, pivot …).
++In order to obtain the data structure of these VTL datasets from the SDMX one, it is assumed that the SDMX dimensions 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 datasets[[^^~[32~]^^>>path:#_ftn32]]. After that, the mapping method from SDMX to VTL specified for the dataflow DF1(1.0) is applied (i.e. basic, pivot …).
  In the example above, for all the datasets of the kind
@@ -1723,7 +1723,7 @@
  …   …   …
--In fact the VTL operator “sub” has exactly the same behaviour. Therefore, mapping different parts of a SDMX dataflow to different VTL datasets in the direction from SDMX to VTL through the ordered concatenation notation is equivalent to a proper use of the operator “**sub**” on such a dataflow. [[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[33~]^^>>path:#_ftn33]]
++In fact the VTL operator “sub” has exactly the same behaviour. Therefore, mapping different parts of a SDMX dataflow to different VTL datasets 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. [[^^~[33~]^^>>path:#_ftn33]]
  In the direction from SDMX to VTL it is allowed to omit the value of one or more Dimensions on which the mapping is based, but maintaining all the separating dots (therefore it may happen to find two or more consecutive dots and dots in the beginning or in the end). The absence of value means that for the corresponding Dimension all the values are kept and the Dimension is not dropped.
@@ -1746,12 +1746,12 @@
  For example, let us assume that the VTL programmer wants to calculate the SDMX dataflow DF2(1.0) having the Dimensions TIME_PERIOD, INDICATOR, and COUNTRY and that such a programmer finds it convenient to calculate separately the parts of DF2(1.0) that have different combinations of values for INDICATOR and COUNTRY:
--* each part is calculated as a  VTL derived dataset, result of a dedicated VTL transformation; [[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[34~]^^>>path:#_ftn34]](%%)
--* the data structure of all these VTL datasets has the TIME_PERIOD identifier and does not have the INDICATOR and COUNTRY identifiers.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[35~]^^>>path:#_ftn35]]
++* each part is calculated as a  VTL derived dataset, result of a dedicated VTL transformation; [[^^~[34~]^^>>path:#_ftn34]]
++* the data structure of all these VTL datasets has the TIME_PERIOD identifier and does not have the INDICATOR and COUNTRY identifiers.[[^^~[35~]^^>>path:#_ftn35]]
--Under these hypothesis, such derived VTL datasets can be mapped to DF2(1.0) by declaring the Dimensions INDICATOR and COUNTRY as mapping dimensions[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[36~]^^>>path:#_ftn36]](%%).
++Under these hypothesis, such derived VTL datasets can be mapped to DF2(1.0) by declaring the Dimensions INDICATOR and COUNTRY as mapping dimensions[[^^~[36~]^^>>path:#_ftn36]].
--The corresponding VTL transformations, assuming that the result needs to be persistent, would be of this kind:^^ ^^[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[37~]^^>>path:#_ftn37]]
++The corresponding VTL transformations, assuming that the result needs to be persistent, would be of this kind:^^ ^^[[^^~[37~]^^>>path:#_ftn37]]
  ‘DF2(1.0)///INDICATORvalue//.//COUNTRYvalue//’  <-  expression
@@ -1773,19 +1773,19 @@
  As said, it is assumed that these VTL derived datasets 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:
  |(((
--      //VTL dataset                                             //
++      //VTL dataset                                             //
  )))|(% colspan="2" %)//INDICATOR value //|(% colspan="2" %)//COUNTRY value//
--|‘DF2(1.0)/GDPPERCAPITA.USA’              |GDPPERCAPITA| | |USA
++|‘DF2(1.0)/GDPPERCAPITA.USA’              |GDPPERCAPITA| | |USA
  |(((
  ‘DF2(1.0)/GDPPERCAPITA.CANADA’
  …   …   …
  )))|GDPPERCAPITA| | |CANADA
--|‘DF2(1.0)/POPGROWTH.USA’                  |POPGROWTH | | |USA
++|‘DF2(1.0)/POPGROWTH.USA’                  |POPGROWTH | | |USA
  |(((
--‘DF2(1.0)/POPGROWTH.CANADA’
++‘DF2(1.0)/POPGROWTH.CANADA’
  …   …   …
  )))|POPGROWTH | | |CANADA
@@ -1818,9 +1818,9 @@
  …);
--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)[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[38~]^^>>path:#_ftn38]](%%), which can be mapped one-to-one to the homonymous SDMX dataflow having the dimension components TIME_PERIOD, INDICATOR and COUNTRY.
++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)[[^^~[38~]^^>>path:#_ftn38]], which can be mapped one-to-one to the homonymous SDMX dataflow having the dimension components TIME_PERIOD, INDICATOR and COUNTRY.
--Therefore, mapping different VTL datasets having the same data structure to different parts of a SDMX dataflow, i.e. in the direction from VTL to SDMX, through the ordered concatenation notation is equivalent to a proper use of the operators “calc” and “union” on such datasets. [[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[39~]^^>>path:#_ftn39]](%%)[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[40~]^^>>path:#_ftn40]]
++Therefore, mapping different VTL datasets having the same data structure to different parts of a SDMX dataflow, i.e. in the direction from VTL to SDMX, through the ordered concatenation notation is equivalent to a proper use of the operators “calc” and “union” on such datasets. [[^^~[39~]^^>>path:#_ftn39]][[^^~[40~]^^>>path:#_ftn40]]
  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).
@@ -1843,7 +1843,7 @@
  )))
  |**Code**|**Code** (for enumerated Dimension, PrimaryMeasure, DataAttribute) or **Concept** (for MeasureDimension)
  |**Described Value Domain**|(((
--non-enumerated** Representation**
++non-enumerated** Representation**
  (having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
  )))
@@ -1869,7 +1869,7 @@
  Domain) is not identifiable. As a consequence, the definition of the VTL rulesets, which in VTL can refer either to enumerated or non-enumerated value domains, in SDMX can refer only to enumerated Value Domains (i.e. to SDMX Codelists).
--As for the mapping between VTL variables and SDMX Concepts, it should be noted that these artefacts do not coincide perfectly. In fact, the VTL variables are  represented variables, defined always on the same Value Domain (“Representation” in SDMX) independently of the data set / data structure in which they appear[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[41~]^^>>path:#_ftn41]](%%), while the SDMX Concepts can have different Representations in different DataStructures.[[(% class="wikiinternallink wikiinternallink wikiinternallink wikiinternallink wikiinternallink" %)^^~[42~]^^>>path:#_ftn42]](%%) This means that one SDMX Concept can correspond to many VTL Variables, one for each representation the Concept has.
++As for the mapping between VTL variables and SDMX Concepts, it should be noted that these artefacts do not coincide perfectly. In fact, the VTL variables are  represented variables, defined always on the same Value Domain (“Representation” in SDMX) independently of the data set / data structure in which they appear[[^^~[41~]^^>>path:#_ftn41]], while the SDMX Concepts can have different Representations in different DataStructures.[[^^~[42~]^^>>path:#_ftn42]] This means that one SDMX Concept can correspond to many VTL Variables, one for each representation the Concept has.
  Therefore, it is important to be aware that some VTL operations (for example the binary operations at data set level) are consistent only if the components having the same names in the operated VTL data sets have also the same representation (i.e. the same Value Domain as for VTL).   For example, it is possible to obtain correct results from the VTL expression
@@ -1918,7 +1918,7 @@
  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.
--=== 10.4.3 Mapping SDMX data types to VTL basic scalar types ===
++=== 10.4.3 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.
@@ -1985,7 +1985,7 @@
  |(((
  **Boolean **
--(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of binary-valued logic: {true, false})
++(corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of binary-valued logic: {true, false})
  )))|**boolean**
  |(((
  **URI **
@@ -2128,7 +2128,7 @@
  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).
--=== 10.4.4 Mapping VTL basic scalar types to SDMX data types ===
++=== 10.4.4 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 .
@@ -2214,7 +2214,7 @@
  |N|fixed number of digits used in the preceding  textual representation of the month or the day
  | |
--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" %)^^~[43~]^^>>path:#_ftn43]](%%).
++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[[^^~[43~]^^>>path:#_ftn43]].
  === 10.4.5 Null Values ===