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Helena 3.1 1 {{box title="**Contents**"}}
2 {{toc/}}
3 {{/box}}
4
5 **Revision History**
6
7 |**Revision**|**Date**|**Contents**
8 | |April 2011|Initial release
9 |1.0|April 2013|Added section 9 - Transforming between versions of SDMX
10 |2.0|July 2020|Added section 10 – Validation and Transformation Language – before the Annex 1.
11
12 = 1 Purpose and Structure =
13
14 == 1.1 Purpose ==
15
16 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
17
18 Information Model.
19
20 == 1.2 Structure ==
21
22 This document is organized into the following major parts:
23
24 A guide to the SDMX Information Model relating to Data Structure Definitions and Data Sets, statement of differences in functionality supported by the different formats and syntaxes for Data Structure Definitions and Data Sets, and best practices for use of SDMX formats, including the representation for time period
25
26 A guide to the SDMX Information Model relating to Metadata Structure Definitions, and Metadata Sets
27
28 Other structural artefacts of interest: agencies, concept role. constraint, partial code list
29
30 = 2 General Notes on This Document =
31
32 At this version of the standards, the term “Key family” is replaced by Data Structure Definition (also known and referred to as DSD) both in the XML schemas and the Information Model. The term “Key family” is not familiar to many people and its name was taken from the model of SDMX-EDI (previously known as GESMES/TS). The more familiar name “Data Structure Definition” which was used in many documents is now also the technical artefact in the SDMX-ML and Information Model technical specifications. The term “Key family” is still used in the SDMX-EDI specification.
33
34 There has been much work within the SDMX community on the creation of user guides, tutorials, and other aides to implementation and understanding of the standard. This document is not intended to duplicate the function of these documents, but instead represents a short set of technical notes not generally covered elsewhere.
35
36 = 3 Guide for SDMX Format Standards =
37
38 == 3.1 Introduction ==
39
40 This guide exists to provide information to implementers of the SDMX format standards – SDMX-ML and SDMX-EDI – that are concerned with data, i.e. Data Structure Definitions and Data Sets. This section is intended to provide information which will help users of SDMX understand and implement the standards. It is not normative, and it does not provide any rules for the use of the standards, such as those found in //SDMX-ML: Schema and Documentation// and //SDMX-EDI: Syntax and Documentation//.
41
42 == 3.2 SDMX Information Model for Format Implementers ==
43
44 === 3.2.1 Introduction ===
45
46 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
48 The SDMX-IM is used to describe the basic data and metadata structures used in all of the SDMX data formats. The Information Model concerns itself with statistical data and its structural metadata, and that is what is described here. Both structural metadata and data have some additional metadata in common, related to their management and administration. These aspects of the data model are not addressed in this section and covered elsewhere in this guide or in the full SDMX-IM document.
49
50 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:
51
52 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;
53
54 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
55
56 the section on data representation is now a convention, to support interoperability with EDIFACT-syntax implementations ( see section 3.3.2);
57
58 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
59
60 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.
61
62 Note that in the descriptions below, text in courier and italicised are the names used in the information model (e.g. //DataSet//).
63
64 == 3.3 SDMX-ML and SDMX-EDI: Comparison of Expressive Capabilities and Function ==
65
66 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.
67
68 === 3.3.1 Format Optimizations and Differences ===
69
70 The following section provides a brief overview of the differences between the various SDMX formats.
71
72 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
73
74 SDMX 2.1 there are just two types of data formats: //GenericData// and
75
76 //StructureSpecificData// (i.e. specific to one Data Structure Definition).
77
78 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.
79
80 To allow for applications which only understand time series data, variations of these formats have been introduced in the form of two data messages;
81
82 //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.
83
84 === //Structure Definition// ===
85
86 The SDMX-ML Structure Message supports the use of annotations to the structure, which is not supported by the SDMX-EDI syntax.
87
88 The SDMX-ML Structure Message allows for the structures on which a Data Structure Definition depends – that is, codelists and concepts – to be either included in the message or to be referenced by the message containing the data structure definition. XML syntax is designed to leverage URIs and other Internet-based referencing mechanisms, and these are used in the SDMX-ML message. This option is not available to those using the SDMX-EDI structure message.
89
90 === //Validation// ===
91
92 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
93
94 definition.)
95
96 The SDMX-ML Generic Data Message also leaves validation above the XML syntax level to the application.
97
98 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.
99
100 === //Update and Delete Messages and Documentation Messages// ===
101
102 All SDMX data messages allow for both delete messages and messages consisting of only data or only documentation.
103
104 === //Character Encodings// ===
105
106 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
107
108 SET (LATIN 1 OR “WESTERN”) in the document “SYNTAX AND
109
110 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.
111
112 === //Data Typing// ===
113
114 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.
115
116 ==== 3.3.2 Data Types ====
117
118 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.
119
120 It should be noted that this section does not address character encodings – it is assumed that conversion software will include the use of transformations which will map between the ISO 8879-1 encoding of the SDMX-EDI format and the UTF-8 encoding of the SDMX-ML formats.
121
122 Note that the following conventions may be followed for ease of interoperation between EDIFACT and XML representations of the data and metadata. For implementations in which no transformation between EDIFACT and XML syntaxes is foreseen, the restrictions below need not apply.
123
124 1. **Identifiers** are:
125 1*. Maximum 18 characters;
126 1*. Any of A..Z (upper case alphabetic), 0..9 (numeric), _ (underscore);
127 1*. The first character is alphabetic.
128 1. **Names** are:
129 1*. Maximum 70 characters.
130 1*. From ISO 8859-1 character set (including accented characters)
131 1. **Descriptions **are:
132 1*. Maximum 350 characters;  From ISO 8859-1 character set.
133 1. **Code values** are:
134 1*. Maximum 18 characters;
135 1*. Any of A..Z (upper case alphabetic), 0..9 (numeric), _ (underscore), / (solidus, slash), = (equal sign), - (hyphen);
136
137 However, code values providing values to a dimension must use only the following characters:
138
139 A..Z (upper case alphabetic), 0..9 (numeric), _ (underscore)
140
141 1. **Observation values** are:
142 1*. Decimal numerics (signed only if they are negative);
143 1*. The maximum number of significant figures is:
144 1*. 15 for a positive number
145 1*. 14 for a positive decimal or a negative integer
146 1*. 13 for a negative decimal
147 1*. Scientific notation may be used.
148 1. **Uncoded statistical concept** text values are:
149 1*.
150 1**. Maximum 1050 characters;
151 1**. From ISO 8859-1 character set.
152 1. **Time series keys**:
153
154 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.
155
156 == 3.4 SDMX-ML and SDMX-EDI Best Practices ==
157
158 === 3.4.1 Reporting and Dissemination Guidelines ===
159
160 **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.
161
162 Central institutions can play a double role:
163
164 * collecting and further disseminating statistics;
165 * devising structural definitions for use in data exchanges.
166
167 **3.4.1.2 Defining Data Structure Definitions (DSDs)**
168
169 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.
170
171 === Dimensions, Attributes and Code Lists ===
172
173 **//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.
174
175 **//Devise DSDs with a small number of Dimensions for public viewing of data.//** A DSD with the number dimensions in excess 6 or 7 is often difficult for non specialist users to understand. In these cases it is better to have a larger number of DSDs with smaller “cubes” of data, or to eliminate dimensions and aggregate the data at a higher level. Dissemination of data on the web is a growing use case for the SDMX standards: the differentiation of observations by dimensionality which are necessary for statisticians and economists are often obscure to public consumers who may not always understand the semantic of the differentiation.
176
177 **//Avoid composite dimensions.//**  Each dimension should correspond to a single characteristic of the data, not to a combination of characteristics.
178
179 **//Consider the inclusion of the following attributes//**. Once the key structure of a data structure definition has been decided, then the set of (preferably mandatory) attributes  of this data structure definition has to be defined. In general, some statistical concepts are deemed necessary across all Data Structure Definitions to qualify the contained information. Examples of these are:
180
181 * A descriptive title for the series (this is most useful for dissemination of data for viewing e.g. on the web)
182 * Collection (e.g. end of period, averaged or summed over period)
183 * Unit (e.g. currency of denomination)
184 * Unit multiplier (e.g. expressed in millions)
185 * Availability (which institutions can a series become available to)
186 * Decimals (i.e. number of decimal digits used in numerical observations)
187 * Observation Status (e.g. estimate, provisional, normal)
188
189 Moreover, additional attributes may be considered as mandatory when a specific data structure definition is defined.
190
191 **//Avoid creating a new code list where one already exists.//** It is highly recommended that structural definitions and code lists be consistent with internationally agreed standard methodologies, wherever they exist, e.g., System of National Accounts 1993; Balance of Payments Manual, Fifth Edition; Monetary and Financial Statistics Manual; Government Finance Statistics Manual, etc. When setting-up a new data exchange, the following order of priority is suggested when considering the use of code lists:
192
193 * international standard code lists;
194 * international code lists supplemented by other international and/or regional institutions;
195 * standardised lists used already by international institutions;
196 * new code lists agreed between two international or regional institutions;
197 * new specific code lists.
198
199 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.
200
201 === Data Structure Definition Structure ===
202
203 The following items have to be specified by a structural definitions maintenance agency when defining a new data structure definition:
204
205 Data structure definition (DSD) identification:
206
207 * DSD identifier
208 * DSD name
209
210 A list of metadata concepts assigned as dimensions of the data structure definition. For each:
211
212 * (statistical) concept identifier
213 * ordinal number of the dimension in the key structure (SDMX-EDI only)
214 * code list identifier (Id, version, maintenance agency) if the representation is coded
215
216 A list of (statistical) concepts assigned as attributes for the data structure definition. For each:
217
218 * (statistical) concept identifier
219 * code list identifier if the concept is coded
220 * assignment status: mandatory or conditional
221 * attachment level
222 * maximum text length for the uncoded concepts
223 * maximum code length for the coded concepts
224
225 A list of the code lists used in the data structure definition. For each:
226
227 * code list identifier
228 * code list name
229 * code values and descriptions
230
231 Definition of data flow definitions.  Two (or more) partners performing data exchanges in a certain context need to agree on:
232
233 * the list of data set identifiers they will be using;
234 * for each data flow:
235 * its content and description
236 * the relevant DSD that defines the structure of the data reported or disseminated according the the dataflow definition
237
238 **3.4.1.3 Exchanging Attributes**
239
240 **//3.4.1.3.1 Attributes on series, sibling and data set level //**//Static properties//.
241
242 * 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.
243 * A centre may agree with its data exchange partners special procedures for authorising the setting of attributes' initial values.
244 * Attribute values at a data set level are set and maintained exclusively by the centre administrating the exchanged data set.
245
246 //Communication of changes// to the centre.
247
248 * Following the creation of a series, the attribute values do not have to be reported again by senders, as long as they do not change.
249 * Whenever changes in attribute values for a series (or sibling group) occur, the reporting institutions should report either all attribute values again (this is the recommended option) or only the attribute values which have changed.  This applies both to the mandatory and the conditional attributes. For example, if a previously reported value for a conditional attribute is no longer valid, this has to be reported to the centre.
250 * A centre may agree with its data exchange partners special procedures for authorising modifications in the attribute values.
251
252 Communication of observation level attributes “observation status”, "observation confidentiality", "observation pre-break".
253
254 * In SDMX-EDI, the observation level attribute “observation status” is part of the fixed syntax of the ARR segment used for observation reporting. Whenever an observation is exchanged, the corresponding observation status must also be exchanged attached to the observation, regardless of whether it has changed or not since the previous data exchange. This rule also applies to the use of the SDMX-ML formats, although the syntax does not necessarily require this.
255 * If the “observation status” changes and the observation remains unchanged, both components would have to be reported.
256 * 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.
257
258 ==== 3.4.2 Best Practices for Batch Data Exchange ====
259
260 **3.4.2.1 Introduction**
261
262 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.
263
264 **3.4.2.2 Positioning of the Dimension "Frequency"**
265
266 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.
267
268 **3.4.2.3 Identification of Data Structure Definitions (DSDs)**
269
270 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.
271
272 **3.4.2.4 Identification of the Data Flows**
273
274 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//)//.
275
276 The statistical information in SDMX is broken down into two fundamental parts - structural metadata (comprising the Data Structure Definition, and associated Concepts and Code Lists) - see Framework for Standards -, and observational data (the DataSet). This is an important distinction, with specific terminology associated with each part. Data - which is typically a set of numeric observations at specific points in time - is organized into data sets (//DataSet//) These data sets are structured according to a specific Data Structure Definition (//DataStructureDefinition//) and are described in the data flow definition (//DataflowDefinition)// The Data Structure Definition describes the metadata that allows an understanding of what is expressed in the data set, whilst the data flow definition provides the identifier and other important information (such as the periodicity of reporting) that is common to all of its component data sets.
277
278 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.
279
280 **3.4.2.5 Special Issues**
281
282 ===== 3.4.2.5.1 "Frequency" related issues =====
283
284 **//Special frequencies.//** The issue of data collected at special (regular or irregular) intervals at a lower than daily frequency (e.g. 24 or 36 or 48 observations per year, on irregular days during the year) is not extensively discussed here. However, for data exchange purposes:
285
286 * such data can be mapped into a series with daily frequency; this daily series will only hold observations for those days on which the measured event takes place;
287 * if the collection intervals are regular, additional values to the existing frequency code list(s) could be added in the future.
288
289 **//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.
290
291
292 = 4 General Notes for Implementers =
293
294 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.
295
296 This section does not go into great detail on these topics, but provides a useful overview of these features to assist implementors in further use of the parts of the specification which are relevant to them.
297
298 == 4.1 Representations ==
299
300 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.
301
302 |**SDMX-ML Data Type**|**XML Schema Data Type**|**.NET Framework Type**|(((
303 **Java Data Type**
304
305 **~ **
306 )))
307 |String|xsd:string|System.String|java.lang.String
308 |Big Integer|xsd:integer|System.Decimal|java.math.BigInteg er
309 |Integer|xsd:int|System.Int32|int
310 |Long|xsd.long|System.Int64|long
311 |Short|xsd:short|System.Int16|short
312 |Decimal|xsd:decimal|System.Decimal|java.math.BigDecim al
313 |Float|xsd:float|System.Single|float
314 |Double|xsd:double|System.Double|double
315 |Boolean|xsd:boolean|System.Boolean|boolean
316 |URI|xsd:anyURI|System.Uri|Java.net.URI or java.lang.String
317 |DateTime|xsd:dateTime|System.DateTim e|javax.xml.datatype .XMLGregorianCalen dar
318 |Time|xsd:time|System.DateTim e|javax.xml.datatype .XMLGregorianCalen dar
319 |GregorianYear|xsd:gYear|System.DateTim e|javax.xml.datatype .XMLGregorianCalen dar
320 |GregorianMont h|xsd:gYearMont h|System.DateTim e|javax.xml.datatype .XMLGregorianCalen dar
321 |GregorianDay|xsd:date|System.DateTim e|javax.xml.datatype .XMLGregorianCalen dar
322 |(((
323 Day,
324
325 MonthDay, Month
326 )))|xsd:g*|System.DateTim e|javax.xml.datatype .XMLGregorianCalen dar
327 |Duration|xsd:duration |System.TimeSpa|javax.xml.datatype
328 |**SDMX-ML Data Type**|**XML Schema Data Type**|**.NET Framework Type**|(((
329 **Java Data Type**
330
331 **~ **
332 )))
333 | | |n|.Duration
334
335 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:
336
337 * AlphaNumeric (common:AlphaNumericType, string which only allows A-z and 0-9)
338 * Alpha (common:AlphaType, string which only allows A-z)
339 * Numeric (common:NumericType, string which only allows 0-9, but is not numeric so that is can having leading zeros)
340 * Count (xs:integer, a sequence with an interval of “1”)
341 * InclusiveValueRange (xs:decimal with the minValue and maxValue facets supplying the bounds)
342 * ExclusiveValueRange (xs:decimal with the minValue and maxValue facets supplying the bounds)
343 * Incremental (xs:decimal with a specified interval; the interval is typically enforced outside of the XML validation)
344 * TimeRange (common:TimeRangeType, start DateTime + Duration,)
345 * ObservationalTimePeriod (common: ObservationalTimePeriodType,  a union of StandardTimePeriod and TimeRange).
346 * StandardTimePeriod (common: StandardTimePeriodType, a union of BasicTimePeriod and TimeRange).
347 * BasicTimePeriod (common: BasicTimePeriodType, a union of GregorianTimePeriod and DateTime)
348 * GregorianTimePeriod (common:GregorianTimePeriodType, a union of GregorianYear, GregorianMonth, and GregorianDay)
349 * ReportingTimePeriod (common:ReportingTimePeriodType, a union of ReportingYear, ReportingSemester, ReportingTrimester, ReportingQuarter, ReportingMonth, ReportingWeek, and ReportingDay).  ReportingYear (common:ReportingYearType)
350 * ReportingSemester (common:ReportingSemesterType)
351 * ReportingTrimester (common:ReportingTrimesterType)
352 * ReportingQuarter (common:ReportingQuarterType)
353 * ReportingMonth (common:ReportingMonthType)
354 * ReportingWeek (common:ReportingWeekType)
355 * ReportingDay (common:ReportingDayType)
356 * XHTML (common:StructuredText, allows for multi-lingual text content that has XHTML markup)
357 * KeyValues (common:DataKeyType)
358 * IdentifiableReference (types for each identifiable object)
359 * DataSetReference (common:DataSetReferenceType)
360 * AttachmentConstraintReference
361
362 (common:AttachmentConstraintReferenceType)
363
364 Data types also have a set of facets:
365
366 * isSequence = true | false (indicates a sequentially increasing value)
367 * minLength = positive integer (# of characters/digits)
368 * maxLength = positive integer (# of characters/digits)
369 * startValue = decimal (for numeric sequence)
370 * endValue = decimal (for numeric sequence)
371 * interval = decimal (for numeric sequence)
372 * timeInterval = duration
373 * startTime = BasicTimePeriod (for time range)
374 * endTime = BasicTimePeriod (for time range)
375 * minValue = decimal (for numeric range)
376 * maxValue = decimal (for numeric range)
377 * decimal = Integer (# of digits to right of decimal point)
378 * pattern = (a regular expression, as per W3C XML Schema)
379 * isMultiLingual = boolean (for specifying text can occur in more than one language)
380
381 Note that code lists may also have textual representations assigned to them, in addition to their enumeration of codes.s
382
383 == 4.2 Time and Time Format ==
384
385 ==== 4.2.1 Introduction ====
386
387 First, it is important to recognize that most observation times are a period. SDMX specifies precisely how Time is handled.
388
389 The representation of time is broken into a hierarchical collection of representations. A data structure definition can use of any of the representations in the hierarchy as the representation of time. This allows for the time dimension of a particular data structure definition allow for only a subset of the default representation.
390
391 The hierarchy of time formats is as follows (**bold** indicates a category which is made up of multiple formats, //italic// indicates a distinct format):
392
393 * **Observational Time Period **o **Standard Time Period**
394
395 § **Basic Time Period**
396
397 * **Gregorian Time Period**
398 * //Date Time//
399
400 § **Reporting Time Period **o //Time Range//
401
402 The details of these time period categories and of the distinct formats which make them up are detailed in the sections to follow.
403
404 ==== 4.2.2 Observational Time Period ====
405
406 This is the superset of all time representations in SDMX. This allows for time to be expressed as any of the allowable formats.
407
408 ==== 4.2.3 Standard Time Period ====
409
410 This is the superset of any predefined time period or a distinct point in time. A time period consists of a distinct start and end point. If the start and end of a period are expressed as date instead of a complete date time, then it is implied that the start of the period is the beginning of the start day (i.e. 00:00:00) and the end of the period is the end of the end day (i.e. 23:59:59).
411
412 ==== 4.2.4 Gregorian Time Period ====
413
414 A Gregorian time period is always represented by a Gregorian year, year-month, or day. These are all based on ISO 8601 dates. The representation in SDMX-ML messages and the period covered by each of the Gregorian time periods are as follows:
415
416 **Gregorian Year:**
417
418 Representation: xs:gYear (YYYY)
419
420 Period: the start of January 1 to the end of December 31 **Gregorian Year Month**:
421
422 Representation: xs:gYearMonth (YYYY-MM)
423
424 Period: the start of the first day of the month to end of the last day of the month **Gregorian Day**:
425
426 Representation: xs:date (YYYY-MM-DD)
427
428 Period: the start of the day (00:00:00) to the end of the day (23:59:59)
429
430 ==== 4.2.5 Date Time ====
431
432 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.
433
434 Representation: xs:dateTime (YYYY-MM-DDThh:mm:ss)[[(% class="wikiinternallink wikiinternallink" %)^^~[1~]^^>>path:#_ftn1]]
435
436 ==== 4.2.6 Standard Reporting Period ====
437
438 Standard reporting periods are periods of time in relation to a reporting year. Each of these standard reporting periods has a duration (based on the ISO 8601 definition) associated with it. The general format of a reporting period is as follows:
439
440 [REPORTING_YEAR]-[PERIOD_INDICATOR][PERIOD_VALUE]
441
442 Where:
443
444 REPORTING_YEAR represents the reporting year as four digits (YYYY) PERIOD_INDICATOR identifies the type of period which determines the duration of the period
445
446 PERIOD_VALUE indicates the actual period within the year
447
448 The following section details each of the standard reporting periods defined in SDMX:
449
450 **Reporting Year**:
451
452 Period Indicator: A
453
454 Period Duration: P1Y (one year)
455
456 Limit per year: 1
457
458 Representation: common:ReportingYearType (YYYY-A1, e.g. 2000-A1) **Reporting Semester:**
459
460 Period Indicator: S
461
462 Period Duration: P6M (six months)
463
464 Limit per year: 2
465
466 Representation: common:ReportingSemesterType (YYYY-Ss, e.g. 2000-S2) **Reporting Trimester:**
467
468 Period Indicator: T
469
470 Period Duration: P4M (four months)
471
472 Limit per year: 3
473
474 Representation: common:ReportingTrimesterType (YYYY-Tt, e.g. 2000-T3) **Reporting Quarter:**
475
476 Period Indicator: Q
477
478 Period Duration: P3M (three months)
479
480 Limit per year: 4
481
482 Representation: common:ReportingQuarterType (YYYY-Qq, e.g. 2000-Q4) **Reporting Month**:
483
484 Period Indicator: M
485
486 Period Duration: P1M (one month)
487
488 Limit per year: 1
489
490 Representation: common:ReportingMonthType (YYYY-Mmm, e.g. 2000-M12) Notes: The reporting month 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.
491
492 **Reporting Week**:
493
494 Period Indicator: W
495
496 Period Duration: P7D (seven days)
497
498 Limit per year: 53
499
500 Representation: common:ReportingWeekType (YYYY-Www, e.g. 2000-W53)
501
502 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" %)^^~[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.
503
504 **Reporting Day**:
505
506 Period Indicator: D
507
508 Period Duration: P1D (one day)
509
510 Limit per year: 366
511
512 Representation: common:ReportingDayType (YYYY-Dddd, e.g. 2000-D366) Notes: There are either 365 or 366 days in a reporting year, depending on whether the reporting year includes leap day (February 29). The reporting day is always represented as three digits, therefore 1-99 are 0 padded (e.g. 001).
513
514 This allows the values to be sorted chronologically using textual sorting methods.
515
516 The meaning of a reporting year is always based on the start day of the year and requires that the reporting year is expressed as the year at the start of the period. This start day is always the same for a reporting year, and is expressed as a day and a month (e.g. July 1). Therefore, the reporting year 2000 with a start day of July 1 begins on July 1, 2000.
517
518 A specialized attribute (reporting year start day) exists for the purpose of communicating the reporting year start day. This attribute has a fixed identifier (REPORTING_YEAR_START_DAY) and a fixed representation (xs:gMonthDay) so that it can always be easily identified and processed in a data message. Although this attribute exists in specialized sub-class, it functions the same as any other attribute outside of its identification and representation. It must takes its identity from a concept and state its relationship with other components of the data structure definition. The ability to state this relationship allows this reporting year start day attribute to exist at the appropriate levels of a data message. In the absence of this attribute, the reporting year start date is assumed to be January 1; therefore if the reporting year coincides with the calendar year, this Attribute is not necessary.
519
520 Since the duration and the reporting year start day are known for any reporting period, it is possible to relate any reporting period to a distinct calendar period. The actual Gregorian calendar period covered by the reporting period can be computed as follows (based on the standard format of [REPROTING_YEAR][PERIOD_INDICATOR][PERIOD_VALUE] and the reporting year start day as [REPORTING_YEAR_START_DAY]):
521
522 1. **Determine [REPORTING_YEAR_BASE]:**
523
524 Combine [REPORTING_YEAR] of the reporting period value (YYYY) with [REPORTING_YEAR_START_DAY] (MM-DD) to get a date (YYYY-MM-DD).
525
526 This is the [REPORTING_YEAR_START_DATE]
527
528 **a) If the [PERIOD_INDICATOR] is W:**
529
530 1.
531 11.
532 111.
533 1111. **If [REPORTING_YEAR_START_DATE] is a Friday, Saturday, or Sunday:**
534
535 Add^^3^^ (P3D, P2D, or P1D respectively) to the [REPORTING_YEAR_START_DATE]. The result is the [REPORTING_YEAR_BASE].
536
537 1.
538 11.
539 111.
540 1111. **If [REPORTING_YEAR_START_DATE] is a Monday, Tuesday, Wednesday, or Thursday:**
541
542 Add^^3^^ (P0D, -P1D, -P2D, or -P3D respectively) to the [REPORTING_YEAR_START_DATE]. The result is the [REPORTING_YEAR_BASE].
543
544 b) **Else:**
545
546 The [REPORTING_YEAR_START_DATE] is the [REPORTING_YEAR_BASE].
547
548 1. **Determine [PERIOD_DURATION]:**
549 11.
550 111. If the [PERIOD_INDICATOR] is A, the [PERIOD_DURATION] is P1Y.
551 111. If the [PERIOD_INDICATOR] is S, the [PERIOD_DURATION] is P6M.
552 111. If the [PERIOD_INDICATOR] is T, the [PERIOD_DURATION] is P4M.
553 111. If the [PERIOD_INDICATOR] is Q, the [PERIOD_DURATION] is P3M.
554 111. If the [PERIOD_INDICATOR] is M, the [PERIOD_DURATION] is P1M.
555 111. If the [PERIOD_INDICATOR] is W, the [PERIOD_DURATION] is P7D.
556 111. If the [PERIOD_INDICATOR] is D, the [PERIOD_DURATION] is P1D.
557 1. **Determine [PERIOD_START]:**
558
559 Subtract one from the [PERIOD_VALUE] and multiply this by the [PERIOD_DURATION]. Add[[(% class="wikiinternallink wikiinternallink" %)^^~[3~]^^>>path:#_ftn3]](%%) this to the [REPORTING_YEAR_BASE]. The result is the [PERIOD_START].
560
561 1. **Determine the [PERIOD_END]:**
562
563 Multiply the [PERIOD_VALUE] by the [PERIOD_DURATION]. Add^^3^^ this to the [REPORTING_YEAR_BASE] add^^3^^ -P1D. The result is the [PERIOD_END].
564
565 For all of these ranges, the bounds include the beginning of the [PERIOD_START] (i.e. 00:00:00) and the end of the [PERIOD_END] (i.e. 23:59:59).
566
567 **Examples: **
568
569 **2010-Q2, REPORTING_YEAR_START_DAY = ~-~-07-01 (July 1)**
570
571 ~1. [REPORTING_YEAR_START_DATE] = 2010-07-01
572
573 b) [REPORTING_YEAR_BASE] = 2010-07-01
574
575 1. [PERIOD_DURATION] = P3M
576 1. (2-1) * P3M = P3M
577
578 2010-07-01 + P3M = 2010-10-01
579
580 [PERIOD_START] = 2010-10-01
581
582 4. 2 * P3M = P6M
583
584 2010-07-01 + P6M = 2010-13-01 = 2011-01-01
585
586 2011-01-01 + -P1D = 2010-12-31
587
588 [PERIOD_END] = 2011-12-31
589
590 The actual calendar range covered by 2010-Q2 (assuming the reporting year begins July 1) is 2010-10-01T00:00:00/2010-12-31T23:59:59
591
592 **2011-W36, REPORTING_YEAR_START_DAY = ~-~-07-01 (July 1)**
593
594 ~1. [REPORTING_YEAR_START_DATE] = 2010-07-01
595
596 a) 2011-07-01 = Friday
597
598 2011-07-01 + P3D = 2011-07-04
599
600 [REPORTING_YEAR_BASE] = 2011-07-04
601
602 1. [PERIOD_DURATION] = P7D
603 1. (36-1) * P7D = P245D
604
605 2011-07-04 + P245D = 2012-03-05
606
607 [PERIOD_START] = 2012-03-05
608
609 4. 36 * P7D = P252D
610
611 2011-07-04 + P252D =2012-03-12
612
613 2012-03-12 + -P1D = 2012-03-11
614
615 [PERIOD_END] = 2012-03-11
616
617 The actual calendar range covered by 2011-W36 (assuming the reporting year begins July 1) is 2012-03-05T00:00:00/2012-03-11T23:59:59
618
619 ==== 4.2.7 Distinct Range ====
620
621 In the case that the reporting period does not fit into one of the prescribe periods above, a distinct time range can be used. The value of these ranges is based on the ISO 8601 time interval format of start/duration. Start can be expressed as either an ISO 8601 date or a date-time, and duration is expressed as an ISO 8601 duration. However, the duration can only be postive.
622
623 ==== 4.2.8 Time Format ====
624
625 In version 2.0 of SDMX there is a recommendation to use the time format attribute to gives additional information on the way time is represented in the message. Following an appraisal of its usefulness this is no longer required. However, it is still possible, if required , to include the time format attribute in SDMX-ML. 
626
627 |**Code**|**Format**
628 |**OTP**|Observational Time Period: Superset of all SDMX time formats (Gregorian Time Period, Reporting Time Period, and Time Range)
629 |**STP**|Standard Time Period: Superset of Gregorian and Reporting Time Periods
630 |**GTP**|Superset of all Gregorian Time Periods and date-time
631 |**RTP**|Superset of all Reporting Time Periods
632 |**TR**|Time Range: Start time and duration (YYYY-MMDD(Thh:mm:ss)?/<duration>)
633 |**GY**|Gregorian Year (YYYY)
634 |**GTM**|Gregorian Year Month (YYYY-MM)
635 |**GD**|Gregorian Day (YYYY-MM-DD)
636 |**DT**|Distinct Point: date-time (YYYY-MM-DDThh:mm:ss)
637 |**RY**|Reporting Year (YYYY-A1)
638 |**RS**|Reporting Semester (YYYY-Ss)
639 |**RT**|Reporting Trimester (YYYY-Tt)
640 |**RQ**|Reporting Quarter (YYYY-Qq)
641 |**RM**|Reporting Month (YYYY-Mmm)
642 |**Code**|**Format**
643 |**RW**|Reporting Week (YYYY-Www)
644 |**RD**|Reporting Day (YYYY-Dddd)
645
646 **Table 1: SDMX-ML Time Format Codes**
647
648 ==== 4.2.9 Transformation between SDMX-ML and SDMX-EDI ====
649
650 When converting SDMX-ML data structure definitions to SDMX-EDI data structure definitions, only the identifier of the time format attribute will be retained. The representation of the attribute will be converted from the SDMX-ML format to the fixed SDMX-EDI code list. If the SDMX-ML data structure definition does not define a time format attribute, then one will be automatically created with the identifier "TIME_FORMAT".
651
652 When converting SDMX-ML data to SDMX-EDI, the source time format attribute will be irrelevant. Since the SDMX-ML time representation types are not ambiguous, the target time format can be determined from the source time value directly. For example, if the SDMX-ML time is 2000-Q2 the SDMX-EDI format will always be 608/708 (depending on whether the target series contains one observation or a range of observations)
653
654 When converting a data structure definition originating in SDMX-EDI, the time format attribute should be ignored, as it serves no purpose in SDMX-ML.
655
656 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.
657
658 ==== 4.2.10 Time Zones ====
659
660 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):
661
662 * Time Range (start date): 2006-06-05**-05:00**/P5D
663 * Time Range (start date-time): 2006-06-05T00:00:00**-05:00**/P5D
664 * Gregorian Year: 2006**-05:00**
665 * Gregorian Month: 2006-06**-05:00**
666 * Gregorian Day: 2006-06-05**-05:00**
667 * Distinct Point: 2006-06-05T00:00:00**-05:00**
668 * Reporting Year: 2006-A1**-05:00**
669 * Reporting Semester: 2006-S2**-05:00**
670 * Reporting Trimester: 2006-T2**-05:00**
671 * Reporting Quarter: 2006-Q3**-05:00**
672 * Reporting Month: 2006-M06**-05:00**
673 * Reporting Week: 2006-W23**-05:00**
674 * Reporting Day: 2006-D156**-05:00**
675 * Reporting Year Start Day: ~-~-07-01**-05:00**
676
677 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.
678
679 ==== 4.2.11 Representing Time Spans Elsewhere ====
680
681 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:
682
683 <Series REF_PERIODStartTime="2000-01-01T00:00:00" REF_PERIOD="P2M"/>
684
685 can now be represented with this:
686
687 <Series REF_PERIOD="2000-01-01T00:00:00/P2M"/>
688
689 ==== 4.2.12 Notes on Formats ====
690
691 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.
692
693 ==== 4.2.13 Effect on Time Ranges ====
694
695 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.
696
697 ==== 4.2.14 Time in Query Messages ====
698
699 When querying for time values, the value of a time parameter can be provided as any of the Observational Time Period formats and must be paired with an operator. In addition, an explicit value for the reporting year start day can be provided, or this can be set to "Any". This section will detail how systems processing query messages should interpret these parameters.
700
701 Fundamental to processing a time value parameter in a query message is understanding that all time periods should be handled as a distinct range of time. Since the time parameter in the query is paired with an operator, this is also effectively represents a distinct range of time. Therefore, a system processing the query must simply match the data where the time period for requested parameter is encompassed by the time period resulting from value of the query parameter. The following table details how the operators should be interpreted for any time period provided as a parameter.
702
703 |**Operator**|**Rule**
704 |Greater Than|Any data after the last moment of the period
705 |Less Than|Any data before the first moment of the period
706 |Greater Than or Equal To|(((
707 Any data on or after the first moment of
708
709 the period
710 )))
711 |Less Than or Equal To|Any data on or before the last moment of the period
712 |Equal To|Any data which falls on or after the first moment of the period and before or on the last moment of the period
713
714 Reporting Time Periods as query parameters are handled based on whether the value of the reportingYearStartDay XML attribute is an explicit month and day or "Any":
715
716 If the time parameter provides an explicit month and day value for the reportingYearStartDay XML attribute, then the parameter value is converted to a distinct range and processed as any other time period would be processed.
717
718 If the reportingYeartStartDay XML attribute has a value of "Any", then any data within the bounds of the reporting period for the year is matched, regardless of the actual start day of the reporting year. In addition, data reported against a normal calendar period is matched if it falls within the bounds of the time parameter based on a reporting year start day of January 1. When determining whether another reporting period falls within the bounds of a report period query parameter, one will have to take into account the actual time period to compare weeks and days to higher order report periods. This will be demonstrated in the examples to follow.
719
720 Note that the reportingYearStartDay XML attribute on the time value parameter is only used to qualify a reporting period value for the given time value parameter. The usage of this is different than using the attribute value parameter for the actual reporting year start day attribute. In the case that the attribute value parameters is used for the reporting year start day data structure attribute, it will be treated as any other attribute value parameter; data will be filtered to that which matches the values specified for the given attribute. For example, if the attribute value parameter references the reporting year start day attribute and specifies a value of "~-~-07-01", then only data which has this attribute with the value "~-~-07-01" will be returned. In terms of processing any time value parameters, the value supplied in the attribute value parameter will be irrelevant.
721
722 **Examples:**
723
724 **Gregorian Period**
725
726 Query Parameter: Greater than 2010
727
728 Literal Interpretation: Any data where the start period occurs after 2010-1231T23:59:59.
729
730 Example Matches:
731
732 * 2011 or later
733 * 2011-01 or later
734 * 2011-01-01 or later
735 * 2011-01-01/P[Any Duration] or any later start date
736 * 2011-[Any reporting period] (any reporting year start day)
737 * 2010-S2 (reporting year start day ~-~-07-01 or later)
738 * 2010-T3 (reporting year start day ~-~-07-01 or later)
739 * 2010-Q3 or later (reporting year start day ~-~-07-01 or later)
740 * 2010-M07 or later (reporting year start day ~-~-07-01 or later)
741 * 2010-W28 or later (reporting year start day ~-~-07-01 or later)
742 * 2010-D185 or later (reporting year start day ~-~-07-01 or later)
743
744 **Reporting Period with explicit start day**
745
746 Query Parameter: Greater than or equal to 2009-Q3, reporting year start day = "-07-01"
747
748 Literal Interpretation: Any data where the start period occurs on after 2010-0101T00:00:00 (Note that in this case 2009-Q3 is converted to the explicit date range of 2010-01-01/2010-03-31 because of the reporting year start day value). Example Matches: Same as previous example
749
750 **Reporting Period with "Any" start day**
751
752 Query Parameter: Greater than or equal to 2010-Q3, reporting year start day = "Any"
753
754 Literal Interpretation: Any data with a reporting period where the start period is on or after the start period of 2010-Q3 for the same reporting year start day, or and data where the start period is on or after 2010-07-01. Example Matches:
755
756 * 2011 or later
757 * 2010-07 or later
758 * 2010-07-01 or later
759 * 2010-07-01/P[Any Duration] or any later start date
760 * 2011-[Any reporting period] (any reporting year start day)
761 * 2010-S2 (any reporting year start day)
762 * 2010-T3 (any reporting year start day)
763 * 2010-Q3 or later (any reporting year start day)
764 * 2010-M07 or later (any reporting year start day)
765 * 2010-W27 or later (reporting year start day ~-~-01-01)^^4^^  2010-D182 or later (reporting year start day ~-~-01-01)
766 * 2010-W28 or later (reporting year start day ~-~-07-01)^^5^^
767
768 ^^4^^ 2010-Q3 (with a reporting year start day of ~-~-01-01) starts on 2010-07-01. This is day 4 of week 26, therefore the first week matched is week 27.
769
770  2010-D185 or later (reporting year start day ~-~-07-01)
771
772 == 4.3 Structural Metadata Querying Best Practices ==
773
774 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.
775
776 Any structural metadata object which contains a reference to an object can be queried based on that reference. For example, a categorisation references both a category and the object is it categorising. As this is the case, one can query for categorisations which categorise a particular object or which categorise against a particular category or category scheme. This mechanism should be used when the referenced object is known.
777
778 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.
779
780 == 4.4 Versioning and External Referencing ==
781
782 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”.
783
784 The production versions of identifiable objects/resources are assumed to be static – that is, they have their isFinal attribute set to ‘true”. Once in production, and object cannot change in any way, or it must be versioned. For cases where an object is not static, the isFinal attribute must have a value of “false”, but non-final objects should not be used outside of a specific system designed to accommodate them. For most purposes, all objects should be declared final before use in production.
785
786 This mechanism is an “early binding” one – everything with a versioned identity is a known quantity, and will not change. It is worth pointing out that in some cases relationships are essentially one-way references: an illustrative case is that of Categories. While a Category may be referenced by many dataflows and metadata flows, the addition of more references from flow objects does not version the Category. This is because the flows are not properties of the Categories – they merely make references to it. If the name of a Category changed, or its subCategories changed, then versioning would be necessary.
787
788 ^^5^^ 2010-Q3 (with a reporting year start day of ~-~-07-01) starts on 2011-01-01. This is day 6 of week 27, therefore the first week matched is week 28.
789
790 Versioning operates at the level of versionable and maintainable objects in the SDMX information model. If any of the children of objects at these levels change, then the objects themselves are versioned.
791
792 One area which is much impacted by this versioning scheme is the ability to reference external objects. With the many dependencies within the various structural objects in SDMX, it is useful to have a scheme for external referencing. This is done at the level of maintainable objects (DSDs, code lists, concept schemes, etc.) In an SDMX-ML Structure Message, whenever an “isExternalReference” attribute is set to true, then the application must resolve the address provided in the associated “uri” attribute and use the SDMX-ML Structure Message stored at that location for the full definition of the object in question. Alternately, if a registry “urn” attribute has been provided, the registry can be used to supply the full details of the object.
793
794 Because the version number is part of the identifier for an object, versions are a necessary part of determining that a given resource is the one which was called for. It should be noted that whenever a version number is not supplied, it is assumed to be “1.0”. (The “x.x” versioning notation is conventional in practice with SDMX, but not required.)
795
796 = 5 Metadata Structure Definition (MSD) =
797
798 == 5.1 Scope ==
799
800 The scope of the MSD is enhanced in this version to better support the types of construct to which metadata can be attached. In particular it is possible to specify an attachment to any key or partial key of a data set. This is particularly useful for web dissemination where metadata may be present for the data, but is not stored with the data but is related to it. For this use case to be supported it is necessary to be able to specify in the MSD that metadata is attached to a key or partial key, and the actual key or partial key to be identified in the Metadata Set.
801
802 In addition to the increase in the scope of objects that can be included in an MSD, the way the identifier mechanism works in this version, and the terminology used, is much simpler.
803
804 == 5.2 Identification of the Object Type to which the Metadata is to be Attached ==
805
806 The following example shows the structure and naming of the MSD components for the use case of defining full and partial keys.
807
808 The schematic structure of an MSD is shown below.
809
810 [[image:1747836776649-282.jpeg]]
811
812 1. **1: Schematic of the Metadata Structure Definition**
813
814 The MSD comprises the specification of the object types to which metadata can be reported in a Metadata Set (Metadata Target(s)), and the Report Structure(s) comprising the Metadata Attributes that identify the Concept for which metadata may be reported in the Metadata Set. Importantly, one Report Structure references the Metadata Target for which it is relevant. One Report Structure can reference many Metadata Target i.e. the same Report Structure can be used for different target objects.
815
816 [[image:1747836776655-364.jpeg]]
817
818 1. **2: Example MSD showing Metadata Targets**
819
820 Note that the SDMX-ML schemas have explicit XML elements for each identifiable object type because identifying, for instance, a Maintainable Object has different properties from an Identifiable Object which must also include the agencyId, version, and id of the Maintainable Object in which it resides.
821
822 == 5.3 Report Structure ==
823
824 An example is shown below.
825
826 [[image:1747836776658-510.jpeg]]
827
828 **Figure 3: Example MSD showing specification of three Metadata Attributes **This example shows the following hierarchy of Metadata Attributes:
829
830 Source – this is presentational and no metadata is expected to be reported at this level
831
832 * Source Type
833 * Collection Source Name
834
835 == 5.4 Metadata Set ==
836
837 An example of reporting metadata according to the MSD described above, is shown below.
838
839 [[image:1747836776677-246.jpeg]]
840
841 **Figure 4: Example Metadata Set **This example shows:
842
843 1. The reference to the MSD, Metadata Report, and Metadata Target
844
845 (MetadataTargetValue)
846
847 1. The reported metadata attributes (AttributeValueSet)
848
849 = 6 Maintenance Agencies =
850
851 All structural metadata in SDMX is owned and maintained by a maintenance agency (Agency identified by agencyID in the schemas). It is vital to the integrity of the structural metadata that there are no conflicts in agencyID. In order to achieve this SDMX adopts the following rules:
852
853 1. Agencies are maintained in an Agency Scheme (which is a sub class of Organisation Scheme)
854 1. The maintenance agency of the Agency Scheme must also be declared in a (different) Agency Scheme.
855 1. The “top-level” agency is SDMX and this agency scheme is maintained by SDMX.
856 1. Agencies registered in the top-level scheme can themselves maintain a single Agency Scheme. SDMX is an agency in the SDMX agency scheme. Agencies in this scheme can themselves maintain a single Agency Scheme and so on.
857 1. The AgencyScheme cannot be versioned and so take a default version number of 1.0 and cannot be made “final”.
858 1. There can be only one AgencyScheme maintained by any one Agency. It has a fixed Id of AgencyScheme.
859 1. The format of the agency identifier is agencyId.agencyID etc. The top-level agency in this identification mechanism is the agency registered in the SDMX agency scheme. In other words, SDMX is not a part of the hierarchical ID structure for agencies. SDMX is, itself, a maintenance agency.
860
861 This supports a hierarchical structure of agencyID.
862
863 An example is shown below.
864
865 [[image:1747836776680-229.jpeg]]
866
867 **Figure 5: Example of Hierarchic Structure of Agencies**
868
869 Each agency is identified by its full hierarchy excluding SDMX.
870
871 The XML representing this structure is shown below.
872
873 [[image:1747836776682-757.jpeg]]
874
875 **Figure 6: Example Agency Schemes Showing a Hierarchy**
876
877 Example of Structure Definitions:
878
879 [[image:1747836776687-934.jpeg]]
880
881 **Figure 7: Example Showing Use of Agency Identifiers**
882
883 Each of these maintenance agencies has an identical Codelist with the Id CL_BOP. However, each is uniquely identified by means of the hierarchic agency structure.
884
885 = 7 Concept Roles =
886
887 == 7.1 Overview ==
888
889 The DSD Components of Dimension and Attribute can play a specific role in the DSD and it is important to some applications that this role is specified. For instance, the following roles are some examples:
890
891 **Frequency **– in a data set the content of this Component contains information on the frequency of the observation values
892
893 **Geography** - in a data set the content of this Component contains information on the geographic location of the observation values
894
895 **Unit** **of Measure** - in a data set the content of this Component contains information on the unit of measure of the observation values
896
897 In order for these roles to be extensible and also to enable user communities to maintain community-specific roles, the roles are maintained in a controlled vocabulary which is implemented in SDMX as Concepts in a Concept Scheme. The Component optionally references this Concept if it is required to declare the role explicitly. Note that a Component can play more than one role and therefore multiple “role” concepts can be referenced.
898
899 == 7.2 Information Model ==
900
901 The Information Model for this is shown below:
902
903
904 **Figure 8: Information Model Extract for Concept Role**
905
906 It is possible to specify zero or more concept roles for a Dimension, Measure Dimension and Data Attribute (but not the ReportingYearStartDay). The Time Dimension, Primary Measure, and the  Attribute ReportingYearStartDay have explicitly defined roles and cannot be further specified with additional concept roles.
907
908 == 7.3 Technical Mechanism ==
909
910 The mechanism for maintain and using concept roles is as follows:
911
912 1. Any recognized Agency can have a concept scheme that contains concepts that identify concept roles. Indeed, from a technical perspective any agency can have more than one of these schemes, though this is not recommended.
913 1. The concept scheme that contains the “role” concepts can contain concepts that do not play a role.
914 1. There is no explicit indication on the Concept whether it is a ‘role” concept.
915 1. Therefore, any concept in any concept scheme is capable of being a “role” concept.
916 1. It is the responsibility of Agencies to ensure their community knows which concepts in which concept schemes play a “role” and the significance and interpretation of this role. In other words, such concepts must be known by applications, there is no technical mechanism that can inform an application on how to process such a “role”.
917 1. If the concept referenced in the Concept Identity in a DSD component (Dimension, Measure Dimension, Attribute) is contained in the concept scheme containing concept roles then the DSD component could play the role implied by the concept, if this is understood by the processing application.
918 1. If the concept referenced in the Concept Identity in a DSD component (Dimension, Measure Dimension, Attribute) is not contained in the concept scheme containing concept roles, and the DSD component is playing a role, then the concept role is identified by the Concept Role in the schema.
919
920 == 7.4 SDMX-ML Examples in a DSD ==
921
922 The Cross-Domain Concept Scheme maintained by SDMX contains concept role concepts (FREQ chosen as an example).
923
924 [[image:1747836776691-440.jpeg]]
925
926 Whether this is a role or not depends upon the application understanding that FREQ in the Cross-Domain Concept Scheme is a role of Frequency.
927
928 Using a Concept Scheme that is not the Cross-Domain Concept Scheme where it is required to assign a role using the Cross-Domain Concept Scheme. Again FREQ is chosen as the example.
929
930 [[image:1747836776693-898.jpeg]]
931
932
933 This explicitly states that this Dimension is playing a role identified by the FREQ concept in the Cross-Domain Concept Scheme. Again the application needs to understand what FREQ in the Cross-Domain Concept Scheme implies in terms of a role.
934
935 This is all that is required for interoperability within a community. The important point is that a community must recognise a specific Agency as having the authority to define concept roles and to maintain these “role” concepts in a concept scheme together with documentation on the meaning of the role and any relevant processing implications. This will then ensure there is interoperability between systems that understand the use of these concepts.
936
937 Note that each of the Components (Data Attribute, Primary Measure, Dimension, Measure Dimension, Time Dimension) has a mandatory identity association (Concept Identity) and if this Concept also identifies the role then it is possible to state this by
938
939 == 7.5 SDMX Cross Domain Concept Scheme ==
940
941 All concepts in the SDMX Cross Domain Concept Scheme are capable of playing a role and this scheme will contain all of the roles that were allowed at version 2.0 and will be maintained with new roles that are agreed at the level of the community using the Cross Domain Concept Scheme.
942
943 The table below lists the Concepts that need to be in this scheme either for compatibility with version 2.0 or because of requests for additional roles at version 2.1 which have been accepted.
944
945 Note that each of the Components (Data Attribute, Primary Measure, Dimension, Measure Dimension, Time Dimension) has a mandatory identity association (Concept Identity) and if this Concept also identifies the role then it is possible to state this by means of the isRole attribute (isRole=true) Additional roles can still be specified by means of the +role association to additional Concepts that identify the role.
946
947 = 8 Constraints =
948
949 == 8.1 Introduction ==
950
951 In this version of SDMX the Constraints is a Maintainable Artefact can be associated to one or more of:
952
953 * Data Structure Definition
954 * Metadata Structure Definition
955 * Dataflow
956 * Metadataflow
957 * Provision Agreement
958 * Data Provider (this is restricted to a Release Calendar Constraint)
959 * Simple or Queryable Datasources
960
961 Note that regardless of the artifact to which the Constraint is associated, it is constraining the contents of code lists in the DSD to which the constrained object is related. This does not apply, of course, to a Data Provider as the Data Provider can be associated, via the Provision Agreement, to many DSDs. Hence the reason for the restriction on the type of Constraint that can be attached to a Data Provider.
962
963 == 8.2 Types of Constraint ==
964
965 The Constraint can be of one of two types:
966
967 * Content constraint
968 * Attachable constraint
969
970 The attachable constraint is used to define “cube slices” which identify sub sets of data in terms of series keys or dimension values. The purpose of this is to enable metadata to be attached to the constraint, and thereby to the cube slices defined in the Constraint. The metadata can be attached via the “reference metadata” mechanism – MSD and Metadata Set – or via a Group in the DSD. Below is snippet of the schema for a DSD that shows the constructs that enable the Constraint to referenced from a Group in a DSD.
971
972 [[image:1747836776695-806.jpeg]]
973
974 **Figure 9: Extract from the SDMX-ML Schema showing reference to Attachment Constraint**
975
976 For the Content Constraint specific “inheritance” rules apply and these are detailed below.
977
978 == 8.3 Rules for a Content Constraint ==
979
980 === 8.3.1 Scope of a Content Constraint ===
981
982 A Content Constraint is used specify the content of a data or metadata source in terms of the component values or the keys.
983
984 In terms of data the components are:
985
986 * Dimension
987 * Measure Dimension
988 * Time Dimension
989 * Data Attribute
990 * Primary Measure
991
992 And the keys are the content of the KeyDescriptor – i.e. the series keys composed, for each key, by a value for each Dimension and Measure Dimension
993
994 In terms of reference metadata the components are:
995
996 * Target Object which is one of:
997 ** Key Descriptor Values o Data Set o Report Period
998 ** IdentifiableObject
999 * Metadata Attribute
1000
1001 The “key” is therefore the combination of the Target Objects that are defined for the  Metadata Target.
1002
1003 For a Constraint based on a DSD the Content Constraint can reference one or more of:
1004
1005 * Data Structure Definition
1006 * Dataflow
1007 * Provision Agreement
1008
1009 For a Constraint based on an MSD the Content Constraint can reference one or more of:
1010
1011 * Metadata Structure Definition
1012 * Metadataflow
1013 * Provision Agreement
1014
1015 Furthermore, there can be more than one Content Constraint specified for a specific object e.g. more than one Constraint for a specific DSD.
1016
1017 In view of the flexibility of constraints attachment, clear rules on their usage are required. These are elaborated below.
1018
1019 === 8.3.2 Multiple Content Constraints ===
1020
1021 There can be many Content Constraints for any Constrainable Artefact (e.g. DSD), subject to the following restrictions:
1022
1023 **8.3.2.1 Cube Region**
1024
1025 1. The constraint can contain multiple Member Selections (e.g. Dimension) but:
1026 1. A specific  Member Selection (e.g. Dimension FREQ)  can only be contained in one Content Constraint for any one attached object (e.g. a specific DSD or specific Dataflow)
1027
1028 **8.3.2.2 Key Set**
1029
1030 Key Sets will be processed in the order they appear in the Constraint and wildcards can be used (e.g. any key position not reference explicitly is deemed to be “all values”). As the Key Sets can be “included” or “excluded” it is recommended that Key Sets with wildcards are declared before KeySets with specific series keys. This will minimize the risk that keys are inadvertently included or excluded.  
1031
1032 === 8.3.3 Inheritance of a Content Constraint ===
1033
1034 **8.3.3.1 Attachment levels of a Content Constraint**
1035
1036 There are three levels of constraint attachment for which these inheritance rules apply:
1037
1038  DSD/MSD – top level o Dataflow/Metadataflow – second level
1039
1040 § Provision Agreement – third level
1041
1042 Note that these rules do not apply to the Simple Datasoucre or Queryable Datasource: the Content Constraint(s) attached to these artefacts are resolved for this artefact only and do not take into account Constraints attached to other artefacts (e.g. Provision Agreement. Dataflow, DSD).
1043
1044 It is not necessary for a Content Constraint to be attached to higher level artifact. e.g. it is valid to have a Content Constraint for a Provision Agreement where there are no constraints attached the relevant dataflow or DSD.
1045
1046 **8.3.3.2 Cascade rules for processing Constraints**
1047
1048 The processing of the constraints on either Dataflow/Metadataflow or Provision Agreement must take into account the constraints declared at higher levels. The rules for the lower level constraints (attached to Dataflow/ Metadataflow and Provision Agreement) are detailed below.
1049
1050 Note that there can be a situation where a constraint is specified at a lower level before a constraint is specified at a higher level. Therefore, it is possible that a higher level constraint makes a lower level constraint invalid. SDMX makes no rules on how such a conflict should be handled when processing the constraint for attachment. However, the cascade rules on evaluating constraints for usage are clear - the higher level constraint takes precedence in any conflicts that result in a less restrictive specification at the lower level.
1051
1052 **8.3.3.3 Cube Region**
1053
1054 1. It is not necessary to have a constraint on the higher level artifact (e.g. DSD referenced by the Dataflow) but if there is such a constraint at the higher level(s) then:
1055 11. The lower level constraint cannot be less restrictive than the constraint specified for the same Member Selection (e.g. Dimension) at the next higher level which constraints that Member Selection (e.g. if the Dimension FREQ is constrained to A, Q in a DSD then the constraint at the Dataflow or Provision Agreement cannot be A, Q, M or even just M – it can only further constrain A,Q).
1056 11. The constraint at the lower level for any one Member Selection further constrains the content for the same Member Selection at the higher level(s).
1057 1. Any Member Selection which is not referenced in a Content Constraint is deemed to be constrained according to the Content Constraint specified at the next higher level which constraints that Member Selection.
1058 1. If there is a conflict when resolving the constraint in terms of a lower-level constraint being less restrictive than a higher-level constraint then the constraint at the higher-level is used.
1059
1060 Note that it is possible for a Content Constraint at a higher level to constrain, say, four Dimensions in a single constraint, and a Content Constraint at a lower level to constrain the same four in two, three, or four Content Constraints.
1061
1062 **8.3.3.4 Key Set**
1063
1064 1. It is not necessary to have a constraint on the higher level artefact (e.g. DSD referenced by the Dataflow) but if there is such a constraint at the higher level(s) then:
1065 11. The lower level constraint cannot be less restrictive than the constraint specified at the higher level.
1066 11. The constraint at the lower level for any one Member Selection further constrains the keys specified at the higher level(s).
1067 1. Any Member Selection which is not referenced in a Content Constraint is deemed to be constrained according to the Content Constraint specified at the next higher level which constraints that Member Selection.
1068 1. If there is a conflict when resolving the keys in the constraint at two levels, in terms of a lower-level constraint being less restrictive than a higher-level constraint, then the offending keys specified at the lower level are not deemed part of the constraint.
1069
1070 Note that a Key in a Key Set can have wildcarded Components. For instance the constraint may simply constrain the Dimension FREQ to “A”, and all keys where the FREQ=A are therefore valid.
1071
1072 The following logic explains how the inheritance mechanism works. Note that this is conceptual logic and actual systems may differ in the way this is implemented. 
1073
1074 1. Determine all possible keys that are valid at the higher level.
1075 1. These keys are deemed to be inherited by the lower level constrained object, subject to the constraints specified at the lower level.
1076 1. Determine all possible keys that are possible using the constraints specified at the lower level.
1077 1. At the lower level inherit all keys that match with the higher level constraint.
1078 1. If there are keys in the lower level constraint that are not inherited then the key is invalid (i.e. it is less restrictive).
1079
1080 **8.3.4 Constraints Examples**
1081
1082 The following scenario is used.
1083
1084 === DSD ===
1085
1086 This contains the following Dimensions:
1087
1088 * GEO – Geography
1089 * SEX – Sex
1090 * AGE – Age
1091 * CAS – Current Activity Status
1092
1093 In the DSD common code lists are used and the requirement is to restrict these at various levels to specify the actual code that are valid for the object to which the Content Constraint is attached.
1094
1095
1096 |(((
1097
1098 )))
1099
1100 |(((
1101
1102 )))
1103
1104 |(((
1105
1106 )))
1107
1108 |(((
1109 **Figure**
1110 )))
1111
1112 |(((
1113 **10**
1114 )))
1115
1116 |(((
1117 **:**
1118 )))
1119
1120 |(((
1121 **~ Example Sce**
1122 )))
1123
1124 |(((
1125 **nario for Constraints**
1126 )))
1127
1128 |(((
1129 **~ **
1130 )))
1131
1132
1133
1134 Constraints are declared as follows:
1135
1136
1137 |(((
1138
1139 )))
1140
1141 |(((
1142
1143 )))
1144
1145 |(((
1146
1147 )))
1148
1149 |(((
1150 **Figure**
1151 )))
1152
1153 |(((
1154 **11**
1155 )))
1156
1157 |(((
1158 **:**
1159 )))
1160
1161 |(((
1162 **~ Example Content Constraints**
1163 )))
1164
1165 |(((
1166 **~ **
1167 )))
1168
1169
1170
1171 **Notes:**
1172
1173 1. AGE is constrained for the DSD and is further restricted for the Dataflow
1174
1175 CENSUS_CUBE1.
1176
1177 1. The same Constraint applies to both Provision Agreements.
1178
1179 The cascade rules elaborated above result as follows:
1180
1181 DSD
1182
1183 ~1. Constrained by eliminating code 001 from the code list for the AGE Dimension.
1184
1185 === Dataflow CENSUS_CUBE1 ===
1186
1187 1. Constrained by restricting the code list for the AGE Dimension to codes 002 and 003(note that this is a more restrictive constraint than that declared for the DSD which specifies all codes except code 001).
1188 1. Restricts the CAS codes to 003 and 004.
1189
1190 === Dataflow CENSUS_CUBE2 ===
1191
1192 1. Restricts the code list for the CAS Dimension to codes TOT and NAP.
1193 1. Inherits the AGE constraint applied at the level of the DSD.
1194
1195 === Provision Agreements CENSUS_CUBE1_IT ===
1196
1197 1. Restricts the codes for the GEO Dimension to IT and its children.
1198 1. Inherits the constraints from Dataflow CENSUS_CUBE1  for the AGE and CAS Dimensions.
1199
1200 === Provision Agreements CENSUS_CUBE2_IT ===
1201
1202 1. Restricts the codes for the GEO Dimension to IT and its children.
1203 1. Inherits the constraints from Dataflow CENSUS_CUBE2 for the CAS Dimension.
1204 1. Inherits the AGE constraint applied at the level of the DSD.
1205
1206 The constraints are defined as follows:
1207
1208 === DSD Constraint ===
1209
1210 [[image:1747836776698-720.jpeg]]
1211
1212 === Dataflow Constraints ===
1213
1214 [[image:1747836776701-360.jpeg]]
1215
1216 === [[image:1747836776707-834.jpeg]] ===
1217
1218 === Provision Agreement Constraint ===
1219
1220 [[image:1747836776710-262.jpeg]]
1221
1222 = 9 Transforming between versions of SDMX =
1223
1224 == 9.1 Scope ==
1225
1226 The scope of this section is to define both best practices and mandatory behaviour for specific aspects of transformation between different formats of SDMX.
1227
1228 == 9.2 Groups and Dimension Groups ==
1229
1230 === 9.2.1 Issue ===
1231
1232 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.
1233
1234 However, there is one case where the attribute MUST be attached to a Group in the version 2.0 and 1.0 message. The conditions of this case are:
1235
1236 1. A Group is defined in the DSD with exactly the same Dimensions as a Dimension Group in the same DSD.
1237 1. The Attribute is defined in the DSD with an Attribute Relationship to the Dimension Group. This attribute is NOT defined as having an Attribute Relationship to the Group.
1238
1239 === 9.2.2 Structural Metadata ===
1240
1241 If the conditions defined in 9.2.1are true then on conversion to a version 2.0 or 1.0 DSD (Key Family) the Component/Attribute.attachmentLevel must be set to “Group” and the Component/Attribute/AttachmentGroup” is used to identify the Group. Note that under rule(1) in 1.2.1 this group will have been defined in the V 2.1 DSD and so will be present in the V 2.0 transformation.
1242
1243 === 9.2.3 Data ===
1244
1245 If the conditions defined in 9.2.1are true then, on conversion from a 2.1 data set to a 2.0 or 1.0 dataset the attribute value will be placed in the relevant <Group>. If these conditions are not true then the attribute value will be placed in the <Series>.
1246
1247 === 9.2.4 Compact Schema ===
1248
1249 If the conditions defined in 9.2.1are true then the Compact Schema must be generated with the Group present and the Attribute(s) present in that group definition.
1250
1251 = 10 Validation and Transformation Language (VTL) =
1252
1253 == 10.1 Introduction ==
1254
1255 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" %)^^~[4~]^^>>path:#_ftn4]](%%). The purpose of the VTL in the SDMX context is to enable the:
1256
1257 * definition of validation and transformation algorithms, in order to specify how to calculate new data  from existing ones;
1258 * 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);
1259 * compilation and execution of VTL algorithms, either interpreting the VTL transformations or translating them in whatever other computer language is deemed as appropriate.
1260
1261 It is important to note that the VTL has its own information model (IM), derived from the Generic Statistical Information Model (GSIM) and described in the VTL User Guide. The VTL IM is designed to be compatible with more standards, like SDMX, DDI (Data Documentation Initiative) and GSIM, and includes the model artefacts that can be manipulated (inputs and/or outputs of transformations, e.g. “Data Set”, “Data Structure”) and the model artefacts that allow the definition of  the transformation algorithms (e.g. “Transformation”, “Transformation Scheme”).
1262
1263 The VTL language can be applied to SDMX artefacts by mapping the SDMX IM model artefacts to the model artefacts that VTL can manipulate. Thus, the SDMX artefacts can be used in VTL as inputs and/or outputs of transformations.  It is important to be aware that the artefacts do not always have the same names in the SDMX and VTL IMs, nor do they always have the same meaning. The more evident example is given by the SDMX Dataset and the VTL “Data Set”, which do not correspond one another: as a matter of fact, the VTL “Data Set” maps to the SDMX “Dataflow”, while the SDMX “Dataset” has no explicit mapping to VTL (such an abstraction is not needed in the definition of VTL transformations). A SDMX “Dataset”, however, is an instance of a SDMX “Dataflow” and can be the artefact on which the VTL transformations are executed (i.e., the transformations are defined on Dataflows and are applied to Dataflow instances that can be Datasets). 
1264
1265 The VTL programs (Transformation Schemes) are represented in SDMX through the TransformationScheme maintainable class which is composed of Transformation (nameable artefact). Each Transformation assigns the outcome of the evaluation of a VTL expression to a result.
1266
1267 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.
1268
1269 == 10.2 References to SDMX artefacts from VTL statements ==
1270
1271 === 10.2.1 Introduction ===
1272
1273 The VTL can manipulate SDMX artefacts (or objects) by referencing them through pre-defined conventional names (aliases). 
1274
1275 The alias of a SDMX artefact can be its URN (Universal Resource Name), an abbreviation of its URN or another user-defined name.
1276
1277 In any case, the aliases used in the VTL transformations have to be mapped to the
1278
1279 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" %)^^~[5~]^^>>path:#_ftn5]](%%) or user defined operators[[(% class="wikiinternallink wikiinternallink" %)^^~[6~]^^>>path:#_ftn6]](%%)  to reference SDMX artefacts. A VtlMappingScheme is a container for zero or more VtlMapping. 
1280
1281 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.
1282
1283 The references through the URN and the abbreviated URN are described in the following paragraphs.
1284
1285 === 10.2.2 References through the URN ===
1286
1287 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.
1288
1289 The SDMX URN[[(% class="wikiinternallink wikiinternallink" %)^^~[7~]^^>>path:#_ftn7]](%%) is the concatenation of the following parts, separated by special symbols like dot, equal, asterisk, comma, and parenthesis:^^ ^^
1290
1291 * SDMXprefix                                                                                   
1292 * SDMX-IM-package-name             
1293 * class-name                                                                        
1294 * agency-id                                                                          
1295 * maintainedobject-id
1296 * maintainedobject-version
1297 * container-object-id [[(% class="wikiinternallink wikiinternallink" %)^^~[8~]^^>>path:#_ftn8]]
1298 * object-id
1299
1300 The generic structure of the URN is the following:
1301
1302 SDMXprefix**.**SDMX-IM-package-name**.**class-name**=**agency-id**:**maintainedobject-id
1303
1304 **(**maintainedobject-version**).***container-object-id**.**object-id
1305
1306 The **SDMX prefix** is “urn:sdmx:org”, always the same for all SDMX artefacts.
1307
1308 The **SDMX-IM-package-name **is the concatenation of the string** **“sdmx.infomodel.” with the package-name which the artefact belongs to. For example, for referencing a dataflow the SDMX-IM-package-name is  “sdmx.infomodel.datastructure”, because the class ,,Dataflow,, belongs to the package “datastructure”.
1309
1310 The **class-name** is the name of the SDMX object class which the SDMX object belongs to (e.g., for referencing a dataflow the class-name is “Dataflow”). The VTL can reference SDMX artefacts that belong to the classes ,,Dataflow, Dimension,,,
1311
1312 MeasureDimension, TimeDimension, PrimaryMeasure, DataAttribute, Concept, ConceptScheme, Codelist.
1313
1314 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).
1315
1316 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" %)^^~[9~]^^>>path:#_ftn9]](%%), coincides with the name of the artefact. Therefore the maintainedobject-id depends on the class of the artefact:
1317
1318 * if the artefact is a ,,Dataflow,,, which is a maintainable class,  the maintainedobject-id is the Dataflow name (dataflow-id);
1319 * 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;
1320 * if the artefact is a ,,Concept,,, which is not maintainable and belongs to the ConceptScheme maintainable class, ,, ,,the maintainedobject-id is the name of the ConceptScheme (conceptScheme-id) which the artefact belongs to;
1321 * if the artefact is a ,,ConceptScheme,,, which is a maintainable class, ,, ,,the maintainedobject-id is the name of the ConceptScheme (conceptScheme-id);
1322 * if the artefact is a ,,Codelist, ,,which is a maintainable class,  the maintainedobject-id is the Codelist name (codelist-id).
1323
1324 The **maintainedobject-version** is the version of the maintained object which the artefact belongs to (for example, possible versions are 1.0, 2.1, 3.1.2).
1325
1326 The **container-object-id** does not apply to the classes that can be referenced in VTL transformations, therefore is not present in their URN
1327
1328 The **object-id** is the name of the non-maintainable artefact (when the artefact is maintainable its name is already specified as the maintainedobject-id, see above), in particular it has to be specified:
1329
1330 * if the artefact is a Dimension, MeasureDimension, TimeDimension, PrimaryMeasure or DataAttribute  (the object-id is the name of one of
1331
1332 the artefacts above, which are data structure components)
1333
1334 * if the artefact is a ,,Concept ,,(the object-id is the name of the ,,Concept,,)
1335
1336 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" %)^^~[10~]^^>>path:#_ftn10]](%%):
1337
1338 ‘urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DFR(1.0)’  <-
1339
1340 ‘urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF1(1.0)’   +
1341
1342 ‘urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF2(1.0)’
1343
1344 === 10.2.3 Abbreviation of the URN ===
1345
1346 The complete formulation of the URN described above is exhaustive but verbose, even for very simple statements. In order to reduce the verbosity through a simplified identifier and make the work of transformation definers easier, proper abbreviations of the URN are possible. Using this approach, the referenced artefacts remain intelligible in the VTL code by a human reader.
1347
1348 The URN can be abbreviated by omitting the parts that are not essential for the identification of the artefact or that can be deduced from other available information, including the context in which the invocation is made. The possible abbreviations are described below.
1349
1350 * 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.
1351 * 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: 
1352 ** “datastructure” for the classes Dataflow, Dimension, MeasureDimension, TimeDimension, PrimaryMeasure, DataAttribute,  
1353 ** “conceptscheme” for the classes Concept and ConceptScheme o “codelist” for the class Codelist.
1354 * 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" %)^^~[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" %)^^~[12~]^^>>path:#_ftn12]](%%).
1355 * 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" %)^^~[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).
1356 * 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;
1357 ** 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
1358
1359 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;
1360
1361 *
1362 ** if the referenced artefact is a ,,ConceptScheme, ,,which is a,, ,,maintainable class,,, ,,the maintained object is the ,,conceptScheme-id,, and obviously cannot be omitted;
1363 ** if the referenced artefact is a ,,Codelist, ,,which is a maintainable class, the maintainedobject-id is the ,,codelist-id,, and obviously cannot be omitted.
1364 * 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.,, ,,
1365 * 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
1366 * 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
1367
1368 them the object-id is the main identifier of the artefact
1369
1370 The simplified object identifier is obtained by omitting all the first part of the URN, including the special characters, till the first part not omitted.
1371
1372 For example, the full formulation that uses the complete URN shown at the end of the previous paragraph:
1373
1374 ‘urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DFR(1.0)’  := ‘urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF1(1.0)’   +
1375
1376 ‘urn:sdmx:org.sdmx.infomodel.datastructure.Dataflow=AG:DF2(1.0)’
1377
1378 by omitting all the non-essential parts would become simply:                          
1379
1380 DFR  :=  DF1 + DF2
1381
1382 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" %)^^~[14~]^^>>path:#_ftn14]](%%):
1383
1384 ‘urn:sdmx:org.sdmx.infomodel.codelist.Codelist=AG:CL_FREQ(1.0)’
1385
1386 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" %)^^~[15~]^^>>path:#_ftn15]](%%):
1387
1388 CL_FREQ
1389
1390 As for the references to the components, it can be enough to specify the  componentId, given that the dataStructure-Id can be omitted. An example of non-abbreviated reference, if the data structure is DST1 and the component is SECTOR, is the following:
1391
1392 ‘urn:sdmx:org.sdmx.infomodel.datastructure.DataStructure=AG:DST1(1.0).SECTOR’ The corresponding fully abbreviated reference, if made from a transformation scheme belonging to AG, would become simply: 
1393
1394 SECTOR
1395
1396 For example, the transformation for renaming the component SECTOR of the dataflow DF1 into SEC can be written as[[(% class="wikiinternallink wikiinternallink" %)^^~[16~]^^>>path:#_ftn16]](%%):
1397
1398 ‘DFR(1.0)’ := ‘DF1(1.0)’ [rename SECTOR to SEC]
1399
1400 In the references to the Concepts, which can exist for example in the definition of the VTL Rulesets, at least the conceptScheme-id and the concept-id must be specified.
1401
1402 An example of non-abbreviated reference, if the conceptScheme-id is CS1 and the concept-id is SECTOR, is the following:
1403
1404 ‘urn:sdmx:org.sdmx.infomodel.conceptscheme.Concept=AG:CS1(1.0).SECTOR’
1405
1406 The corresponding fully abbreviated reference, if made from a RulesetScheme belonging to AG, would become simply: 
1407
1408 CS1(1.0).SECTOR
1409
1410 The Codes and in general all the Values can be written without any other specification, for example, the transformation to check if the values of the measures of the dataflow DF1 are between 0 and 25000 can be written like follows:
1411
1412 ‘DFR(1.0)’ := between ( ‘DF1(1.0)’, 0, 25000 )
1413
1414 The artefact (component, concept, codelist …) which the Values are referred to can be deduced from the context in which the reference is made, taking also into account the VTL syntax. In the transformation above, for example, the values 0 and 2500 are compared to the values of the measures of DF1(1.0).
1415
1416 === 10.2.4 User-defined alias ===
1417
1418 The third possibility for referencing SDMX artefacts from VTL statements is to use user-defined aliases not related to the SDMX URN of the artefact.
1419
1420 This approach gives preference to the use of symbolic names for the SDMX artefacts. As a consequence, in the VTL code the referenced artefacts would become not directly intelligible by a human reader. In any case, the VTL aliases are associated to the SDMX URN through the VtlMappingScheme and VtlMapping classes. These classes provide for structured references to SDMX artefacts whatever kind of reference is used in VTL statements (URN, abbreviated URN or user-defined aliases).
1421
1422 === 10.2.5 References to SDMX artefacts from VTL Rulesets ===
1423
1424 The VTL Rulesets allow defining sets of reusable rules that can be applied by some
1425
1426 VTL operators, like the ones for validation and hierarchical roll-up. A “rule” consists in a relationship between Values belonging to some Value Domains or taken by some Variables, for example: (i) when the Country is USA then the Currency is USD; (ii) the Benelux is composed by Belgium, Luxembourg, Netherlands.
1427
1428 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. 
1429
1430 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" %)^^~[17~]^^>>path:#_ftn17]](%%).
1431
1432 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" %)^^~[18~]^^>>path:#_ftn18]](%%)
1433
1434 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.
1435
1436 == 10.3 Mapping between SDMX and VTL artefacts ==
1437
1438 === 10.3.1 When the mapping occurs ===
1439
1440 The mapping methods between the VTL and SDMX object classes allow transforming a SDMX definition in a VTL one and vice-versa for the artefacts to be manipulated.
1441
1442 It should be remembered that VTL programs (i.e. Transformation Schemes) are represented in SDMX through the TransformationScheme maintainable class which is composed of Transformations (nameable  artefacts). Each Transformation assigns the outcome of the evaluation of a VTL expression to a result: the input operands of the expression and the result can be SDMX artefacts.
1443
1444 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. 
1445
1446 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" %)^^~[19~]^^>>path:#_ftn19]](%%).
1447
1448 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). 
1449
1450 === 10.3.2 General mapping of VTL and SDMX data structures ===
1451
1452 This section makes reference to the VTL “Model for data and their structure”[[(% class="wikiinternallink wikiinternallink" %)^^~[20~]^^>>path:#_ftn20]](%%) and the correspondent SDMX “Data Structure Definition”[[(% class="wikiinternallink wikiinternallink" %)^^~[21~]^^>>path:#_ftn21]](%%).
1453
1454 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" %)^^~[22~]^^>>path:#_ftn22]](%%)
1455
1456 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.
1457
1458 A VTL Data Set is structured by one and just one Data Structure and a VTL Data Structure can structure any number of Data Sets. Correspondingly, in the SDMX context a SDMX Dataflow is structured by one and just one DataStructureDefinition and one DataStructureDefinition can structure any number of Dataflows.
1459
1460 A VTL Data Set has a Data Structure made of Components, which in turn can be Identifiers, Measures and Attributes. Similarly, a SDMX DataflowDefinition has a DataStructureDefinition made of components that can be DimensionComponents, PrimaryMeasure and DataAttributes. In turn, a
1461
1462 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. 
1463
1464 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" %)^^~[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.
1465
1466 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. 
1467
1468 Instead VTL allows either  the method above (an identifier containing the name of the measure together with just one measure component) or a more generic method that consists in defining more measure components in the data structure, one for each measure.
1469
1470 Therefore for multi-measure data more mapping options are possible, as described in more detail in the following sections.
1471
1472 === 10.3.3 Mapping from SDMX to VTL data structures ===
1473
1474 **10.3.3.1 Basic Mapping **
1475
1476 The main mapping method from SDMX to VTL is called **Basic **mapping. This is considered as the default mapping method and is applied unless a different method is specified through the VtlMappingScheme and VtlDataflowMapping classes. 1842 When transforming **from SDMX to VTL**, this method consists in leaving the 1843 components unchanged and maintaining their names and roles, according to the 1844 following table:
1477
1478 |SDMX|VTL
1479 |Dimension|(Simple) Identifier
1480 |Time Dimension|(Time) Identifier
1481 |Measure Dimension|(Measure) Identifier
1482 |Primary Measure|Measure
1483 |Data Attribute|Attribute
1484
1485 According to this method, the resulting VTL structures are always mono-measure
1486
1487 (i.e., they have just one measure component) and their Measure is the SDMX
1488
1489 PrimaryMeasure. Nevertheless, if the SDMX data structure has a MeasureDimension, which can convey the name of one or more measure concepts, such unique measure component can contain the value of more (conceptual) measures (one for each observation).
1490
1491 As for the SDMX DataAttributes, in VTL they are all considered “at data point / observation level” (i.e. dependent on all the VTL Identifiers), because VTL does not have the SDMX AttributeRelationships, which defines the construct to which the DataAttribute is related (e.g. observation, dimension or set or group of dimensions, whole data set).
1492
1493 With the Basic mapping, one SDMX observation generates one VTL data point.
1494
1495 **10.3.3.2 Pivot Mapping **
1496
1497 An alternative mapping method from SDMX to VTL is the **Pivot **mapping, which is different from the Basic method only for the SDMX data structures that contain a MeasureDimension, which are mapped to multi-measure VTL data structures.  
1498
1499 The SDMX structures that do not contain a MeasureDimension are mapped like in the Basic mapping (see the previous paragraph).
1500
1501 The SDMX structures that contain a MeasureDimension are mapped as follows (this mapping is equivalent to a pivoting operation):
1502
1503 * A SDMX simple dimension becomes a VTL (simple) identifier and a SDMX TimeDimension becomes a VTL (time) identifier;
1504 * Each possible Concept Cj of the SDMX MeasureDimension is mapped to a VTL Measure, having the same name as the SDMX Concept (i.e. Cj); the VTL Measure Cj is a new VTL component even if the SDMX data structure has not such a Component;
1505 * The SDMX MeasureDimension is not mapped to VTL (it disappears in the VTL Data Structure);
1506 * The SDMX PrimaryMeasure is not mapped to VTL as well (it disappears in the VTL Data Structure);
1507 * A SDMX DataAttribute is mapped in different ways according to its AttributeRelationship:
1508 ** If, according to the SDMX AttributeRelationship, the values of the DataAttribute do not depend on the values of the MeasureDimension, the SDMX DataAttribute becomes a VTL Attribute having the same name.  This happens if the AttributeRelationship is not specified (i.e. the DataAttribute does not depend on any DimensionComponent and therefore is at data set level), or if it refers to a set (or a group) of dimensions which does not include the MeasureDimension;    
1509 ** Otherwise if, according to the SDMX AttributeRelationship,  the values of the DataAttribute depend on the MeasureDimension, the SDMX DataAttribute is mapped to one VTL Attribute for each possible Concept of the SDMX MeasureDimension; by default, the names of the VTL Attributes are obtained by concatenating the name of the SDMX DataAttribute and the names of the correspondent
1510
1511 Concept of the MeasureDimension separated by underscore; for example, if the SDMX DataAttribute is named DA and the possible concepts of the SDMX MeasureDimension are named C1, C2, …, Cn, then the corresponding VTL Attributes will be named DA_C1, DA_C2, …, DA_Cn (if different names are desired, they can be achieved afterwards by renaming the Attributes through VTL operators). o Like in the Basic mapping, the resulting VTL Attributes are considered as dependent on all the VTL identifiers (i.e. “at data point / observation level”), because VTL does not have the SDMX notion of Attribute Relationship.
1512
1513 The summary mapping table of the “pivot” mapping from SDMX to VTL for the SDMX data structures that contain a MeasureDimension is the following:
1514
1515 |SDMX|VTL
1516 |Dimension|(Simple) Identifier
1517 |TimeDimension|(Time) Identifier
1518 |MeasureDimension & PrimaryMeasure|One Measure for each Concept of the SDMX Measure Dimension
1519 |DataAttribute not depending on the MeasureDimension|Attribute
1520 |DataAttribute depending on the MeasureDimension|One Attribute for each Concept of the SDMX Measure Dimension
1521
1522 Using this mapping method, the components of the data structure can change in the conversion from SDMX to VTL and it must be taken into account that the VTL 1908 statements can reference only the components of the resulting VTL data structure.
1523
1524 At observation / data point level, calling Cj (j=1, … n) the j^^th^^ Concept of the 1911 MeasureDimension:
1525
1526  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;
1527
1528 *
1529 ** 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.
1530 ** 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
1531 ** 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
1532
1533 **10.3.3.3 From SDMX DataAttributes to VTL Measures **
1534
1535 *
1536 ** 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.
1537
1538 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.
1539
1540 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.
1541
1542 === 10.3.4 Mapping from VTL to SDMX data structures ===
1543
1544 **10.3.4.1 Basic Mapping **
1545
1546 The main mapping method **from VTL to SDMX** is called **Basic **mapping as well.
1547
1548 This is considered as the default mapping method and is applied unless a different method is specified through the VtlMappingScheme and VtlDataflowMapping classes. 
1549
1550 The method consists in leaving the components unchanged and maintaining their names and roles in SDMX, according to the following mapping table, which is the same as the basic mapping from SDMX to VTL, only seen in the opposite direction.
1551
1552 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
1553
1554 PrimaryMeasure). In this case it becomes mandatory to specify a different 1958 mapping method through the VtlMappingScheme and VtlDataflowMapping 1959 classes.[[(% class="wikiinternallink wikiinternallink" %)^^~[24~]^^>>path:#_ftn24]](%%)
1555
1556 1960 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. 
1557
1558 Mapping table:
1559
1560 |VTL|SDMX
1561 |(Simple) Identifier|Dimension
1562 |(Time) Identifier|TimeDimension
1563 |(Measure) Identifier|MeasureDimension
1564 |Measure|PrimaryMeasure
1565 |Attribute|DataAttribute
1566
1567 If the distinction between simple identifier, time identifier and measure identifier is not maintained in the VTL environment, the classification between Dimension, TimeDimension and MeasureDimension exists only in SDMX, as declared in the relevant DataStructureDefinition.
1568
1569 Regarding the Attributes, because VTL considers all of them “at observation level”, the corresponding SDMX DataAttributes should be put “at observation level” as well (AttributeRelationships referred to the PrimaryMeasure), unless some other information about their AttributeRelationship is available.
1570
1571 Note that the basic mappings in the two directions (from SDMX 2.1 to VTL 2.0 and vice-versa) are (almost completely) reversible. In fact, if a SDMX 2.1 structure is mapped to a VTL structure and then the latter is mapped back to SDMX 2.1, the resulting data structure is like the original one (apart for the AttributeRelationship, that can be different if the original SDMX 2.1 structure contains DataAttributes that are not at observation level). In reverse order, if a VTL 2.0 mono-measure structure is mapped to SDMX 2.1 and then the latter is mapped back to VTL 2.0, the original data structure is obtained (apart from the name of the VTL measure, that in SDMX 2.1 must become “obs_value”).
1572
1573 As  said, the resulting SDMX definitions must be compliant with the SDMX consistency rules. For example, the SDMX DSD must have the assignmentStatus,  which does not exist in VTL, the AttributeRelationship for the DataAttributes and so on.
1574
1575 **10.3.4.2 Unpivot Mapping **
1576
1577 An alternative mapping method from VTL to SDMX is the **Unpivot **mapping.  
1578
1579 Although this mapping method can be used in any case, it makes major sense in case the VTL data structure has more than one measure component (multi-measures VTL structure). For such VTL structures, in fact, the basic method cannot be applied, given that by maintaining the data structure unchanged the resulting SDMX data structure would have more than one measure component, which is not allowed by SDMX 2.1 (it allows just one measure component, the PrimaryMeasure, called
1580
1581 “obs_value”).
1582
1583 The multi-measures VTL structures have not a Measure Identifier (because the Measures are separate components) and need to be converted to SDMX dataflows having an added MeasureDimension which disambiguates the multiple measures, and an added PrimaryMeasure, in which the measures’ values are maintained.
1584
1585 The **unpivot** mapping behaves like follows:
1586
1587 * like in the basic mapping, a VTL (simple) identifier becomes a SDMX
1588
1589 Dimension and a VTL (time) identifier becomes a SDMX TimeDimension (as said, a  measure identifier cannot exist in multi-measure VTL structures);
1590
1591 * a MeasureDimension component called “measure_name” is added to the SDMX DataStructure;
1592 * a PrimaryMeasure component called  “obs_value” is added to the SDMX DataStructure;
1593 * each VTL Measure is mapped to a Concept of the SDMX MeasureDimension  having the same name as the VTL Measure (therefore all the VTL Measure Components do not originate Components in the SDMX DataStructure);
1594 * a VTL Attribute becomes a SDMX DataAttribute having AttributeRelationship  referred to all the SDMX DimensionComponents including the TimeDimension  and except the MeasureDimension. 
1595
1596 The summary mapping table of the **unpivot** mapping method is the following:
1597
1598
1599 |VTL|SDMX
1600 |(Simple) Identifier|Dimension
1601 |(Time) Identifier|TimeDimension
1602 |All Measure Components|(((
1603 MeasureDimension (having one Measure Concept for each VTL measure component) &
1604
1605 PrimaryMeasure
1606 )))
1607 |Attribute |(((
1608 DataAttribute depending on all
1609
1610 SDMX Dimensions including the
1611
1612 TimeDimension and except the MeasureDimension
1613 )))
1614
1615 At observation / data point level:
1616
1617  a multi-measure VTL Data Point becomes a set of SDMX observations, one for each VTL measure
1618
1619  the values of the VTL identifiers become the values of the corresponding SDMX Dimensions, for all the observations of the set above
1620
1621 *
1622 ** 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)
1623 ** the value of the j^^th^^ VTL measure becomes the value of the SDMX PrimaryMeasure of the j^^th^^ observation of the set
1624 ** the values of the VTL Attributes become the values of the corresponding SDMX DataAttributes (in principle for all the observations of the set above)
1625
1626 If desired, this method can be applied also to mono-measure VTL structures, provided that none of the VTL components has already the role of measure identifier.
1627
1628 Like in the general case, a MeasureDimension component called “measure_name” would be added to the SDMX DataStructure and would have just one possible measure concept, corresponding to the unique VTL measure. The original VTL measure component would not become a Component in the SDMX data structure. The value of the VTL measure would be assigned to the SDMX PrimaryMeasure called “obs_value”.
1629
1630 In any case, the resulting SDMX definitions must be compliant with the SDMX consistency rules. For example, the possible Concepts of the SDMX MeasureDimension need to be listed in a SDMX ConceptScheme, with proper id, agency and version; moreover, the SDMX DSD must have the assignmentStatus, which does not exist in VTL, the attributeRelationship for the DataAttributes and so on.
1631
1632 **10.3.4.3 From VTL Measures to SDMX Data Attributes **
1633
1634 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 one VTL measure in the SDMX primaryMeasure and all the other VTL Measures in SDMX DataAttributes. This method is called M2A (“M2A” stands for “Measures to DataAttributes”).
1635
1636 When applied to mono-measure VTL structures (having one Measure component), the M2A method behaves like the Basic mapping (the VTL Measure component becomes the SDMX primary measure “obs_value”, there is no additional VTL measure to be converted to SDMX DataAttribute). Therefore the mapping table is the same as for the Basic method:
1637
1638 |VTL|SDMX
1639 |(Simple) Identifier|Dimension
1640 |(Time) Identifier|TimeDimension
1641 |(Measure) Identifier (if any)|MeasureDimension
1642 |Measure|PrimaryMeasure
1643 |Attribute|DataAttribute
1644
1645 For multi-measure VTL structures (having more than one Measure component), one VTL Measure becomes the SDMX PrimaryMeasure while the other VTL Measures maintain their names and values but assume the role of DataAttribute in SDMX. The choice of the VTL Measure that correspond to the SDMX PrimaryMeasure is left to the definer of the SDMX data structure definition.
1646
1647 2Taking into account that the multi-measure VTL structures do not have a measure 2073 identifier, the mapping table is the following:
1648
1649 |VTL|SDMX
1650 |(Simple) Identifier|Dimension
1651 |(Time) Identifier|TimeDimension
1652 |One of the Measures|PrimaryMeasure
1653 |Other Measures|DataAttribute
1654 |Attribute|DataAttribute
1655
1656 Even in this case, the resulting SDMX definitions must be compliant with the SDMX consistency rules. For example, the SDMX DSD must have the assignmentStatus,  which does not exist in VTL, the attributeRelationship for the DataAttributes and so on. In particular, the primaryMeasure of the SDMX 2.1 DSD must be called “obs_value” and must be one of the VTL Measures, chosen by the DSD definer.
1657
1658 === 10.3.5 Declaration of the mapping methods between data structures ===
1659
1660 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.
1661
1662
1663 A customized mapping can be defined through the VtlMappingScheme and VtlDataflowMapping classes (see the section of the SDMX IM relevant to the VTL). A VtlDataflowMapping allows specifying the mapping methods to be used for a specific dataflow, both in the direction from SDMX to VTL (toVtlMappingMethod) and from VTL to SDMX (fromVtlMappingMethod); in fact a VtlDataflowMapping associates the structured URN that identifies a SDMX dataflow to its VTL alias and its mapping methods.
1664
1665 It is possible to specify the toVtlMappingMethod and fromVtlMappingMethod also for the conventional dataflow called “generic_dataflow”: in this case the specified mapping methods are intended to become the default ones, overriding the
1666
1667 “Basic” methods. In turn, the toVtlMappingMethod and fromVtlMappingMethod declared for a specific Dataflow are intended to override the default ones for such a Dataflow.
1668
1669 The VtlMappingScheme is a container for zero or more VtlDataflowMapping (besides possible mappings to artefacts other than dataflows).
1670
1671 === 10.3.6 Mapping dataflow subsets to distinct VTL data sets[[(% class="wikiinternallink wikiinternallink" %)^^**~[25~]**^^>>path:#_ftn25]](%%) ===
1672
1673 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
1674
1675 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).
1676
1677 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" %)^^~[26~]^^>>path:#_ftn26]](%%)
1678
1679 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" %)^^~[27~]^^>>path:#_ftn27]](%%)
1680
1681 Given a SDMX Dataflow and some predefined Dimensions of its
1682
1683 DataStructure, it is allowed to map the subsets of observations that have the same combination of values for such Dimensions to correspondent VTL datasets.
1684
1685 For example, assuming that the SDMX dataflow DF1(1.0) has the Dimensions INDICATOR, TIME_PERIOD and COUNTRY, and that the user declares the
1686
1687 Dimensions INDICATOR and COUNTRY as basis for the mapping (i.e. the mapping dimensions):  the observations that have the same values for INDICATOR and COUNTRY would be mapped to the same VTL dataset (and vice-versa).
1688
1689 In practice, this kind mapping is obtained like follows:
1690
1691 * 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" %)^^~[28~]^^>>path:#_ftn28]](%%) Following the example above, imagine that the user declares the dimensions INDICATOR and COUNTRY.
1692 * The VTL dataset is given a name using a special notation also called “ordered concatenation” and composed of the following parts: 
1693 ** The reference to the SDMX dataflow (expressed according to the rules described in the previous paragraphs, i.e. URN, abbreviated
1694
1695 URN or another alias); for example DF(1.0); o a slash (“/”) as a separator; [[(% class="wikiinternallink wikiinternallink" %)^^~[29~]^^>>path:#_ftn29]]
1696
1697 *
1698 ** 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" %)^^~[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.
1699
1700 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.
1701
1702 Therefore, the generic name of this kind of VTL datasets would be:
1703
1704 ‘DF(1.0)///INDICATORvalue//.//COUNTRYvalue//’
1705
1706 Where DF(1.0) is the Dataflow and //INDICATORvalue// and //COUNTRYvalue //are placeholders for one value of the INDICATOR and // //COUNTRY dimensions.
1707
1708 Instead the specific name of one of these VTL datasets would be:
1709
1710 ‘DF(1.0)/POPULATION.USA’
1711
1712 In particular, this is the VTL dataset that contains all the observations of the dataflow DF(1.0) for which  //INDICATOR// = POPULATION and //COUNTRY// = USA.
1713
1714 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.
1715
1716 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" %)^^~[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.
1717
1718 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.
1719
1720 As already said, each VTL dataset is assumed to contain all the observations of the
1721
1722 SDMX dataflow having INDICATOR=//INDICATORvalue //and COUNTRY=
1723
1724 //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.
1725
1726 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" %)^^~[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 …). 
1727
1728 In the example above, for all the datasets of the kind
1729
1730 ‘DF1(1.0)///INDICATORvalue//.//COUNTRYvalue//’, the dimensions INDICATOR and COUNTRY would be dropped so that the data structure of all the resulting VTL data sets would have the identifier TIME_PERIOD only.
1731
1732 It should be noted that the desired VTL datasets (i.e. of the kind ‘DF1(1.0)/// INDICATORvalue//.//COUNTRYvalue//’) can be obtained also by applying the VTL operator “**sub**” (subspace) to the dataflow DF1(1.0), like in the following VTL expression:
1733
1734 ‘DF1(1.0)/POPULATION.USA’ := 
1735
1736 DF1(1.0) [ sub  INDICATOR=“POPULATION”, COUNTRY=“USA” ];
1737
1738
1739 ‘DF1(1.0)/POPULATION.CANADA’ := 
1740
1741 DF1(1.0) [ sub  INDICATOR=“POPULATION”, COUNTRY=“CANADA” ];
1742
1743
1744 …   …   …
1745
1746 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" %)^^~[33~]^^>>path:#_ftn33]]
1747
1748 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.
1749
1750 For example, ‘DF(1.0)/POPULATION.’ (note the dot in the end of the name) is the VTL dataset that contains all the observations of the dataflow DF(1.0) for which  //INDICATOR// = POPULATION and COUNTRY = any value.
1751
1752 This is equivalent to the application of the VTL “sub” operator only to the identifier //INDICATOR//:
1753
1754 ‘DF1(1.0)/POPULATION.’ := 
1755
1756 DF1(1.0) [ sub  INDICATOR=“POPULATION” ];
1757
1758
1759 Therefore the VTL dataset ‘DF1(1.0)/POPULATION.’ would have the identifiers COUNTRY and TIME_PERIOD.
1760
1761 Heterogeneous invocations of the same Dataflow are allowed, i.e. omitting different Dimensions in different invocations.
1762
1763 Let us now analyse the mapping direction from VTL to SDMX.
1764
1765 In this situation, distinct parts of a SDMX dataflow are calculated as distinct VTL datasets, under the constraint that they must have the same VTL data structure.
1766
1767 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:
1768
1769 * each part is calculated as a  VTL derived dataset, result of a dedicated VTL transformation; [[(% class="wikiinternallink wikiinternallink" %)^^~[34~]^^>>path:#_ftn34]](%%)
1770 * 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" %)^^~[35~]^^>>path:#_ftn35]]
1771
1772 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" %)^^~[36~]^^>>path:#_ftn36]](%%).
1773
1774 The corresponding VTL transformations, assuming that the result needs to be persistent, would be of this kind:^^ ^^[[(% class="wikiinternallink wikiinternallink" %)^^~[37~]^^>>path:#_ftn37]]
1775
1776 ‘DF2(1.0)///INDICATORvalue//.//COUNTRYvalue//’  <-  expression
1777
1778 Some examples follow, for some specific values of INDICATOR and COUNTRY:
1779
1780 ‘DF2(1.0)/GDPPERCAPITA.USA’    <-   expression11;
1781
1782 ‘DF2(1.0)/GDPPERCAPITA.CANADA’   <-   expression12;
1783
1784 …   …   …
1785
1786 ‘DF2(1.0)/POPGROWTH.USA’   <-   expression21;
1787
1788 ‘DF2(1.0)/POPGROWTH.CANADA’    <-   expression22;
1789
1790 …   …   …
1791
1792
1793 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:
1794
1795 |(((
1796 //VTL dataset                                             //
1797
1798
1799 )))|(% colspan="2" %)//INDICATOR value //|(% colspan="2" %)//COUNTRY value//
1800 |‘DF2(1.0)/GDPPERCAPITA.USA’              |GDPPERCAPITA| | |USA
1801 |(((
1802 ‘DF2(1.0)/GDPPERCAPITA.CANADA’  
1803
1804 …   …   …
1805 )))|GDPPERCAPITA| | |CANADA
1806 |‘DF2(1.0)/POPGROWTH.USA’                  |POPGROWTH | | |USA
1807 |(((
1808 ‘DF2(1.0)/POPGROWTH.CANADA’         
1809
1810 …   …   …
1811 )))|POPGROWTH | | |CANADA 
1812
1813 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)), that can be mapped one-to-one to the homonymous SDMX Dataflow.  Following the same example, these VTL transformations would be:
1814
1815 DF2bis_GDPPERCAPITA_USA    :=   ‘DF2(1.0)/GDPPERCAPITA.USA’
1816
1817 [calc  identifier INDICATOR := ”GDPPERCAPITA”,  identifier  COUNTRY := ”USA”];
1818
1819 DF2bis_GDPPERCAPITA_CANADA :=   ‘DF2(1.0)/GDPPERCAPITA.CANADA’   [calc  identifier INDICATOR:=”GDPPERCAPITA”,  identifier COUNTRY:=”CANADA”]; …   …   …
1820
1821 DF2bis_POPGROWTH_USA     :=  ‘DF2(1.0)/POPGROWTH.USA’ 
1822
1823 [calc  identifier INDICATOR := ”POPGROWTH”,  identifier  COUNTRY :=”USA”];
1824
1825 DF2bis_POPGROWTH_CANADA’  :=  ‘DF2(1.0)/POPGROWTH.CANADA’
1826
1827 [calc  identifier INDICATOR := ”POPGROWTH”,  identifier  COUNTRY := ”CANADA”]; …   …   …
1828
1829 DF2(1.0)   <-   UNION          (DF2bis_GDPPERCAPITA_USA’,
1830
1831 DF2bis_GDPPERCAPITA_CANADA’,
1832
1833 … ,
1834
1835 DF2bis_POPGROWTH_USA’,
1836
1837 DF2bis_POPGROWTH_CANADA’ 
1838
1839 …);
1840
1841 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" %)^^~[38~]^^>>path:#_ftn38]](%%), which can be mapped one-to-one to the homonymous SDMX dataflow having the dimension components TIME_PERIOD, INDICATOR and COUNTRY.
1842
1843 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" %)^^~[39~]^^>>path:#_ftn39]](%%)[[(% class="wikiinternallink wikiinternallink" %)^^~[40~]^^>>path:#_ftn40]]
1844
1845 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).
1846
1847 === 10.3.7 Mapping variables and value domains between VTL and SDMX ===
1848
1849 With reference to the VTL “model for Variables and Value domains”, the following additional mappings have to be considered:
1850
1851 |VTL|SDMX
1852 |**Data Set Component**|Although this abstraction exists in SDMX, it does not have an explicit definition and correspond to a Component (either a Dimension or a PrimaryMeasure or a DataAttribute) belonging to one specific Dataflow^^42^^
1853 |**Represented Variable**|**Concept** with  a definite Representation
1854 |**Value Domain**|**Representation** (see the Structure Pattern in the Base Package)
1855 |**Enumerated Value Domain / Code List**|(((
1856 **Codelist** (for enumerated
1857
1858 Dimension, PrimaryMeasure,
1859
1860 DataAttribute) or **ConceptScheme**
1861
1862 (for MeasureDimension)
1863 )))
1864 |**Code**|**Code** (for enumerated Dimension, PrimaryMeasure, DataAttribute) or **Concept** (for MeasureDimension)
1865 |**Described Value Domain**|(((
1866 non-enumerated** Representation**
1867
1868 (having Facets / ExtendedFacets, see the Structure Pattern in the Base Package)
1869 )))
1870 |**Value**|(((
1871 Although this abstraction exists in SDMX, it does not have an explicit definition and correspond to a **Code** of a
1872
1873 Codelist (for enumerated
1874
1875 Representations) or to a valid **value **(for non-enumerated** **
1876
1877 Representations) or to a **Concept**
1878
1879 (for MeasureDimension)
1880 )))
1881 |**Value Domain Subset / Set**|This abstraction does not exist in SDMX
1882 |**Enumerated Value Domain Subset / Enumerated Set**|This abstraction does not exist in SDMX
1883 |**Described Value Domain Subset / Described Set**|This abstraction does not exist in SDMX
1884 |**Set list**|This abstraction does not exist in SDMX
1885
1886 The main difference between VTL and SDMX relies on the fact that the VTL artefacts for defining subsets of Value Domains do not exist in SDMX, therefore the VTL features for referring to predefined subsets are not available in SDMX. These artefacts are the Value Domain Subset (or Set), either enumerated or described, the Set List (list of values belonging to enumerated subsets) and the Data Set Component (aimed at defining the set of values that the Component of a Data Set can take, possibly a subset of the codes of Value Domain).
1887
1888 Another difference consists in the fact that all Value Domains are considered as identifiable objects in VTL either if enumerated or not, while in SDMX  the Codelist (corresponding to a VTL enumerated Value Domain) is identifiable, while the SDMX non-enumerated Representation (corresponding to a VTL non-enumerated Value
1889
1890 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). 
1891
1892 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" %)^^~[41~]^^>>path:#_ftn41]](%%), while the SDMX Concepts can have different Representations in different DataStructures.[[(% class="wikiinternallink wikiinternallink" %)^^~[42~]^^>>path:#_ftn42]](%%) This means that one SDMX Concept can correspond to many VTL Variables, one for each representation the Concept has.
1893
1894 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
1895
1896 DS_c  :=  DS_a  +  DS_b  (where DS_a, DS_b, DS_c   are VTL Data Sets)
1897
1898 if the matching components in DS_a and DS_b (e.g. ref_date, geo_area, sector …) refer to the same general representation. In simpler words, DS_a  and DS_b must use the same values/codes (for ref_date, geo_area, sector … ), otherwise the relevant values would not match and the result of the operation would be wrong.
1899
1900 As mentioned, the property above is not enforced by construction in SDMX, and different representations of the same Concept can be not compatible one another (for example, it may happen that geo_area is represented by ISO-alpha-3 codes in DS_a and by ISO alpha-2 codes in DS_b). Therefore, it will be up to the definer of VTL transformations to ensure that the VTL expressions are consistent with the actual representations of the correspondent SDMX Concepts.
1901
1902 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. ISO-alpha-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.
1903
1904 == 10.4 Mapping between SDMX and VTL Data Types ==
1905
1906 === 10.4.1 VTL Data types ===
1907
1908 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 compiletime errors.
1909
1910 The VTL data types are sub-divided in scalar types (like integers, strings, etc.), which are the types of the scalar values, and compound types (like data sets, components, rulesets, etc.), which are the types of the compound structures. See below the diagram of the VTL data types, taken from the VTL User Manual:
1911
1912 [[image:1747836776716-178.png]]
1913
1914 **Figure 12 – VTL Data Types**
1915
1916 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.
1917
1918 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):
1919
1920
1921 **Figure 13 – VTL Basic Scalar Types**
1922
1923 === 10.4.2 VTL basic scalar types and SDMX data types ===
1924
1925 The VTL assumes that a basic scalar type has a unique internal representation and can have more external representations.
1926
1927 The internal representation is the format used within a VTL system to represent (and process) all the scalar values of a certain type. In principle, this format is hidden and not necessarily known by users. The external representations are instead the external formats of the values of a certain basic scalar type, i.e. the formats known by the users. For example, the internal representation of the dates can be an integer counting the days since a predefined date (e.g. from 01/01/4713 BC up to 31/12/5874897 AD like in Postgres) while two possible external representations are the formats YYYY-MM-GG and MM-GG-YYYY (e.g. respectively 2010-12-31 and 1231-2010).
1928
1929 The internal representation is the reference format that allows VTL to operate on more values of the same type (for example on more dates) even if such values have different external formats: these values are all converted to the unique internal representation so that they can be composed together (e.g. to find the more recent date, to find the time span between these dates and so on).
1930
1931 The VTL assumes that a unique internal representation exists for each basic scalar type but does not prescribe any particular format for it, leaving the VTL systems free to using they preferred or already existing internal format. By consequence, in VTL the basic scalar types are abstractions not associated to a specific format.
1932
1933 SDMX data types are conceived instead to support the data exchange, therefore they do have a format, which is known by the users and correspond, in VTL terms, to external representations. Therefore, for each VTL basic scalar type there can be more SDMX data types (the latter are explained in the section “General Notes for Implementers” of this document and are actually much more numerous than the former).
1934
1935 The following paragraphs describe the mapping between the SDMX data types and the VTL basic scalar types. This mapping shall be presented in the two directions of possible conversion, i.e. from SDMX to VTL and vice-versa.
1936
1937 The conversion from SDMX to VTL happens when an SDMX artefact acts as inputs of a VTL transformation. As already said, in fact, at compile time the VTL needs to know the VTL type of the operands in order to check their compliance with the VTL operators and at runtime it must convert the values from their external (SDMX) representations to the corresponding internal (VTL) ones.
1938
1939 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.
1940
1941 === 10.4.3 Mapping SDMX data types to VTL basic scalar types ===
1942
1943 The following table describes the default mapping for converting from the SDMX data types to the VTL basic scalar types.
1944
1945 |**SDMX data type (BasicComponentDataType)**|**Default VTL basic scalar type**
1946 |(((
1947 **String   **
1948
1949 (string allowing any character)
1950 )))|**string**
1951 |(((
1952 **Alpha    **
1953
1954 (string which only allows A-z)
1955 )))|**string**
1956 |(((
1957 **AlphaNumeric  **
1958
1959 (string which only allows A-z and 0-9)
1960 )))|**string**
1961 |(((
1962 **Numeric   **
1963
1964 (string which only allows 0-9, but is not numeric so that is can having leading zeros)
1965 )))|**string**
1966 |(((
1967 **BigInteger **
1968
1969 (corresponds to XML Schema xs:integer datatype; infinite set of integer values)
1970 )))|**integer**
1971 |(((
1972 **Integer **
1973
1974 (corresponds to XML Schema xs:int datatype; between
1975
1976 -2147483648 and +2147483647 (inclusive))
1977 )))|**integer**
1978 |(((
1979 **Long **
1980
1981 (corresponds to XML Schema xs:long datatype;
1982
1983 between -9223372036854775808 and +9223372036854775807 (inclusive))
1984 )))|**integer**
1985 |(((
1986 **Short **
1987
1988 (corresponds to XML Schema xs:short datatype; between -32768 and -32767 (inclusive))
1989 )))|**integer**
1990 |(((
1991 **Decimal**
1992
1993 (corresponds to XML Schema xs:decimal datatype; subset of real numbers that can be represented as decimals)
1994 )))|**number**
1995 |(((
1996 **Float **
1997
1998 (corresponds to XML Schema xs:float datatype; patterned after the IEEE single-precision 32-bit floating point type)
1999 )))|**number**
2000 |(((
2001 **Double **
2002
2003 (corresponds to XML Schema xs:double datatype; patterned after the IEEE double-precision 64-bit floating point type)
2004 )))|**number**
2005 |(((
2006 **Boolean **
2007
2008 (corresponds to the XML Schema xs:boolean datatype; support the mathematical concept of binary-valued logic: {true, false}) 
2009 )))|**boolean**
2010 |(((
2011 **URI **
2012
2013 (corresponds to the XML Schema xs:anyURI; absolute or relative Uniform Resource Identifier Reference)
2014 )))|**string**
2015 |(((
2016 **Count   **
2017
2018 (an integer following a sequential pattern, increasing by 1 for each occurrence)
2019 )))|**integer**
2020 |(((
2021 **InclusiveValueRange **
2022
2023 (decimal number within a closed interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
2024 )))|**number**
2025 |(((
2026 **ExclusiveValueRange **
2027
2028 (decimal number within an open interval, whose bounds are specified in the SDMX representation by the facets minValue and maxValue)
2029 )))|**number**
2030 |(((
2031 **Incremental  **
2032
2033 (decimal number the increased by a specific interval (defined by the interval facet), which is typically enforced outside of the XML validation)
2034 )))|**number**
2035 |(((
2036 **ObservationalTimePeriod   **
2037
2038 (superset of StandardTimePeriod and TimeRange)
2039 )))|**time**
2040 |(((
2041 **StandardTimePeriod   **
2042
2043 (superset of BasicTimePeriod and ReportingTimePeriod)
2044 )))|**time**
2045 |(((
2046 **BasicTimePeriod  **
2047
2048 (superset of GregorianTimePeriod and DateTime)
2049 )))|**date**
2050 |(((
2051 **GregorianTimePeriod   **
2052
2053 (superset of GregorianYear, GregorianYearMonth, and GregorianDay)
2054 )))|**date**
2055 |**GregorianYear     **(YYYY)  |**date**
2056 |**GregorianYearMonth** / **GregorianMonth**    (YYYY-MM)|**date**
2057 |**GregorianDay    **(YYYY-MM-DD)|**date**
2058 |(((
2059 **ReportingTimePeriod **
2060
2061 (superset of RepostingYear, ReportingSemester,
2062
2063 ReportingTrimester, ReportingQuarter, ReportingMonth,
2064
2065 ReportingWeek, ReportingDay)
2066 )))|**time_period**
2067 |(((
2068 **ReportingYear   **
2069
2070 (YYYY-A1 – 1 year period)
2071 )))|**time_period**
2072 |(((
2073 **ReportingSemester  **
2074
2075 (YYYY-Ss – 6 month period)
2076 )))|**time_period**
2077 |(((
2078 **ReportingTrimester **
2079
2080 (YYYY-Tt – 4 month period)
2081 )))|**time_period**
2082 |(((
2083 **ReportingQuarter   **
2084
2085 (YYYY-Qq – 3 month period)
2086 )))|**time_period**
2087 |(((
2088 **ReportingMonth   **
2089
2090 (YYYY-Mmm – 1 month period)
2091 )))|**time_period**
2092 |(((
2093 **ReportingWeek   **
2094
2095 (YYYY-Www – 7 day period; following ISO 8601 definition of a week in a year)
2096 )))|**time_period**
2097 |(((
2098 **ReportingDay   **
2099
2100 (YYYY-Dddd – 1 day period)
2101 )))|**time_period**
2102 |(((
2103 **DateTime  **
2104
2105 (YYYY-MM-DDThh:mm:ss)
2106 )))|**date**
2107 |(((
2108 **TimeRange   **
2109
2110 (YYYY-MM-DD(Thh:mm:ss)?/<duration>)
2111 )))|**time**
2112 |(((
2113 **Month   **
2114
2115 (~-~-MM; speicifies a month independent of a year; e.g.
2116
2117 February is black history month in the United States)
2118 )))|**string**
2119 |(((
2120 **MonthDay   **
2121
2122 (~-~-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)
2123 )))|**string**
2124 |(((
2125 **Day   **
2126
2127 (~-~--DD; specifies a day independent of a month or year; e.g. the 15^^th^^ is payday)
2128 )))|**string**
2129 |(((
2130 **Time   **
2131
2132 (hh:mm:ss; time independent of a date; e.g. coffee break is at 10:00 AM)
2133 )))|**string**
2134 |(((
2135 **Duration **
2136
2137 (corresponds to XML Schema xs:duration datatype)
2138 )))|**duration**
2139 |XHTML|Metadata type – not applicable
2140 |KeyValues|Metadata type – not applicable
2141 |IdentifiableReference|Metadata type – not applicable
2142 |DataSetReference|Metadata type – not applicable
2143 |AttachmentConstraintReference|Metadata type – not applicable
2144
2145
2146
2147 **Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
2148
2149 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).
2150
2151 === 10.4.4 Mapping VTL basic scalar types to SDMX data types ===
2152
2153 The following table describes the default conversion from the VTL basic scalar types to the SDMX data types .
2154
2155 |**VTL basic scalar type**|**Default SDMX data type (BasicComponentDataType)**|**Default output format**
2156 |**String**|**String **|Like XML (xs:string)
2157 |**Number**|**Float **|Like XML (xs:float)
2158 |**Integer**|**Integer **|Like XML (xs:int)
2159 |**Date**|**DateTime**|YYYY-MM-DDT00:00:00Z
2160 |**Time**|**StandardTimePeriod**|<date>/<date> (as defined above)
2161 |**time_period**|(((
2162 **ReportingTimePeriod**
2163
2164 **(StandardReportingPeriod)**
2165 )))|(((
2166 YYYY-Pppp
2167
2168 (according to SDMX )
2169 )))
2170 |**Duration**|**Duration **|(((
2171 Like XML (xs:duration)
2172
2173 PnYnMnDTnHnMnS
2174 )))
2175 |**Boolean**|**Boolean **|(((
2176 Like XML (xs:boolean) with the values
2177
2178 “true” or “false”
2179 )))
2180
2181 **Figure 14 – Mappings from SDMX data types to VTL Basic Scalar Types**
2182
2183 In case a different default conversion is desired, it can be achieved through the
2184
2185 CustomTypeScheme and CustomType artefacts (see also the section Transformations and Expressions of the SDMX information model).
2186
2187 The custom output formats can be specified by means of the VTL formatting mask described in the section “Type Conversion and Formatting Mask” of the VTL Reference Manual. Such a section describes the masks for the VTL basic scalar types “number”, “integer”, “date”, “time”, “time_period” and “duration” and gives examples. As for the types “string” and “boolean” the VTL conventions are extended with some other special characters as described in the following table.
2188
2189 |(% colspan="2" %)**VTL special characters for the formatting masks**
2190 |(% colspan="2" %)** **
2191 |(% colspan="2" %)**Number **
2192 |D|one numeric digit (if the scientific notation is adopted, D is only for the mantissa)
2193 |E|one numeric digit (for the exponent of the scientific notation)
2194 |.    (dot)|possible separator between the integer and the decimal parts.
2195 |,   (comma)|possible separator between the integer and the decimal parts.
2196 | |
2197 |(% colspan="2" %)**Time and duration**
2198 |C |century
2199 |Y|year
2200 |S|semester
2201 |Q|quarter
2202 |M|month
2203 |W|week
2204 |D|day
2205 |h |hour digit (by default on 24 hours)
2206 |M|minute
2207 |S|second
2208 |D|decimal of second
2209 |P|period indicator (representation in one digit for the duration)
2210 |P|number of the periods specified in the period indicator
2211 |AM/PM |indicator of AM / PM (e.g. am/pm for “am” or “pm”)
2212 |MONTH|uppercase textual representation of the month (e.g., JANUARY for January)
2213 |DAY|uppercase textual representation of the day (e.g., MONDAY for Monday)
2214 |Month|lowercase textual representation of the month (e.g., january)
2215 |Day|lowercase textual representation of the month (e.g., monday)
2216 |Month|First character uppercase, then lowercase textual representation of the month (e.g., January)
2217 |Day|First character uppercase, then lowercase textual representation of the day using (e.g. Monday)
2218 | |
2219 |(% colspan="2" %)**String  **
2220 |X|any string character
2221 |Z|any string character from “A” to “z”
2222 |9|any string character from “0” to “9”
2223 | |
2224 |(% colspan="2" %)**Boolean **
2225 |B|Boolean using “true” for True and “false” for False
2226 |1|Boolean using “1” for True and “0” for False
2227 |0|Boolean using “0” for True and “1” for False
2228 | |
2229 |(% colspan="2" %)Other qualifiers
2230 |*|an arbitrary number of digits (of the preceding type)
2231 |+|at least one digit (of the preceding type)
2232 |( )|optional digits (specified within the brackets)
2233 |\|prefix for the special characters that must appear in the mask
2234 |N|fixed number of digits used in the preceding  textual representation of the month or the day
2235 | |
2236
2237 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" %)^^~[43~]^^>>path:#_ftn43]](%%).
2238
2239 === 10.4.5 Null Values ===
2240
2241 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.
2242
2243 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.
2244
2245 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.
2246
2247 === 10.4.6 Format of the literals used in VTL transformations ===
2248
2249 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.
2250
2251 Given this discretion, it is essential to know which are the external representations adopted for the literals in a VTL program, in order to interpret them correctly.  For example, if the external format for the dates is YYYY-MM-DD the date literal 201001-02 has the meaning of 2^^nd^^ January 2010, instead if the external format for the dates is YYYY-DD-MM the same literal has the meaning of 1^^st^^ February 2010.
2252
2253 Hereinafter, i.e. in the SDMX implementation of the VTL, it is assumed that the literals are expressed according to the “default output format” of the table of the previous paragraph (“Mapping VTL basic scalar types to SDMX data types”) unless otherwise specified.
2254
2255 A different format can be specified in the attribute “vtlLiteralFormat” of the CustomType artefact (see also the section Transformations and Expressions of the SDMX information model).
2256
2257 Like in the case of the conversion of NULLs described in the previous paragraph, the overriding assumption is applied, for a certain VTL basic scalar type, if a value is found for the vtlLiteralFormat attribute of the CustomType of such VTL basic scalar type. The overriding assumption is applied for all the literals of a related VTL
2258
2259 TransformationScheme.
2260
2261 In case a literal is operand of a VTL Cast operation, the format specified in the Cast overrides all the possible otherwise specified formats.
2262
2263 = 11 Annex I: How to eliminate extra element in the .NET SDMX Web Service =
2264
2265 == 11.1 Problem statement ==
2266
2267 For implementing an SDMX compliant Web Service the standardised WSDL file should be used that describes the expected request/response structure. The request message of the operation contains a wrapper element (e.g. “GetGenericData”) that wraps a tag called “GenericDataQuery”, which is the actual SDMX query XML message that contains the query to be processed by the Web Service. In the same way the response is formulated in a wrapper element “GetGenericDataResponse”.
2268
2269 As defined in the SOAP specification, the root element of a SOAP message is the Envelope, which contains an optional Header and a mandatory Body. These are illustrated below along with the Body contents according to the WSDL:
2270
2271 The problem that initiated the present analysis refers to the difference in the way SOAP requests are when trying to implement the aforementioned Web Service in .NET framework.
2272
2273 Building such a Web Service using the .NET framework is done by exposing a method (i.e. the getGenericData in the example) with an XML document argument (lets name it “Query”). **The difference that appears in Microsoft .Net implementations is that there is a need for an extra XML container around the SDMX GenericDataQuery.** This is the expected behavior since the framework is let to publish automatically the Web Service as a remote procedure call, thus wraps each parameter into an extra element. The .NET request is illustrated below:
2274
2275 Furthermore this extra element is also inserted in the automatically generated WSDL from the framework. Therefore this particularity requires custom clients for the .NET Web Services that is not an interoperable solution.
2276
2277 == 11.2 Solution ==
2278
2279 The solution proposed for conforming the .NET implementation to the envisioned SOAP requests has to do with the manual intervention to the serialisation and deserialisation of the XML payloads. Since it is a Web Service of already prepared XML messages requests/responses this is the indicate way so as to have full control on the XML messages. This is the way the Java implementation (using Apache Axis) of the SDMX Web Service has adopted.
2280
2281 As regards the .NET platform this is related with the usage of **XmlAnyElement** parameter for the .NET web methods.
2282
2283 Web methods use XmlSerializer in the .NET Framework to invoke methods and build the response.
2284
2285 [[image:1747836776717-914.jpeg]]
2286
2287 The XML is passed to the XmlSerializer to de-serialize it into the instances of classes in managed code that map to the input parameters for the Web method. Likewise, the output parameters and return values of the Web method are serialized into XML in order to create the body of the SOAP response message.
2288
2289 In case the developer wants more control over the serialization and de-serialization process a solution is represented by the usage of **XmlElement** parameters. This offers the opportunity of validating the XML against a schema before de-serializing it, avoiding de-serialization in the first place, analyzing the XML to determine how you want to de-serialize it, or using the many powerful XML APIs that are available to deal with the XML directly. This also gives the developer the control to handle errors in a particular way instead of using the faults that the XmlSerializer might generate under the covers.
2290
2291 In order to control the de-serialization process of the XmlSerializer for a Web method, **XmlAnyElement** is a simple solution to use.
2292
2293 To understand how the **XmlAnyElement** attribute works we present the following two web methods:
2294
2295 In this method the **input** parameter is decorated with the **XmlAnyElement** parameter. This is a hint that this parameter will be de-serialized from an **xsd:any** element. Since the attribute is not passed any parameters, it means that the entire XML element for this parameter in the SOAP message will be in the Infoset that is represented by this **XmlElement** parameter.
2296
2297 The difference between the two is that for the first method, **SubmitXml**, the
2298
2299 XmlSerializer will expect an element named **input** to be an immediate child of the **SubmitXml** element in the SOAP body. The second method, **SubmitXmlAny**, will not care what the name of the child of the **SubmitXmlAny** element is. It will plug whatever XML is included into the input parameter. The message style from ASP.NET Help for the two methods is shown below. First we look at the message for the method without the **XmlAnyElement** attribute.
2300
2301 Now we look at the message for the method that uses the **XmlAnyElement** attribute.
2302
2303 The method decorated with the **XmlAnyElement** attribute has one fewer wrapping elements. Only an element with the name of the method wraps what is passed to the **input** parameter.
2304
2305 For more information please consult:  [[http:~~/~~/msdn.microsoft.com/en>>url:http://msdn.microsoft.com/en-us/library/aa480498.aspx]][[->>url:http://msdn.microsoft.com/en-us/library/aa480498.aspx]][[us/library/aa480498.aspx>>url:http://msdn.microsoft.com/en-us/library/aa480498.aspx]][[url:http://msdn.microsoft.com/en-us/library/aa480498.aspx]]
2306
2307 Furthermore at this point the problem with the different requests has been solved. However there is still the difference in the produced WSDL that has to be taken care. The automatic generated WSDL now doesn’t insert the extra element, but defines the content of the operation wrapper element as “xsd:any” type.
2308
2309 Without a common WSDL still the solution doesn’t enforce interoperability. In order to
2310
2311 “fix” the WSDL, there two approaches. The first is to intervene in the generation process. This is a complicated approach, compared to the second approach, which overrides the generation process and returns the envisioned WSDL for the SDMX Web Service.
2312
2313 This is done by redirecting the request to the “/Service?WSDL” to the envisioned WSDL stored locally into the application. To do this, from the project add a “Global Application Class” item (.asax file) and override the request in the “Application_BeginRequest” method. This is demonstrated in detail in the next section.
2314
2315 This approach has the disadvantage that for each deployment the WSDL end point has to be changed to reflect the current URL. However this inconvenience can be easily eliminated if a developer implements a simple rewriting module for changing the end point to the one of the current deployment.
2316
2317 == 11.3 Applying the solution ==
2318
2319 In the context of the SDMX Web Service, applying the above solution translates into the following:
2320
2321 The SOAP request/response will then be as follows:
2322
2323 **GenericData Request**
2324
2325 **GenericData Response**
2326
2327 For overriding the automatically produced WSDL, in the solution explorer right click the project and select “Add” -> “New item…”. Then select the “Global Application Class”. This will create “.asax” class file in which the following code should replace the existing empty method:
2328
2329 The SDMX_WSDL.wsdl should reside in the in the root directory of the application. After applying this solution the returned WSDL is the envisioned. Thus in the request message definition contains:
2330
2331
2332 ----
2333
2334 [[~[1~]>>path:#_ftnref1]] The seconds can be reported fractionally
2335
2336 [[~[2~]>>path:#_ftnref2]] ISO 8601 defines alternative definitions for the first week, all of which produce equivalent results. Any of these definitions could be substituted so long as they are in relation to the reporting year start day.
2337
2338 [[~[3~]>>path:#_ftnref3]] The rules for adding durations to a date time are described in the W3C XML Schema specification. See [[http:~~/~~/www.w3.org/TR/xmlschema>>url:http://www.w3.org/TR/xmlschema-2/#adding-durations-to-dateTimes]][[->>url:http://www.w3.org/TR/xmlschema-2/#adding-durations-to-dateTimes]][[2/#adding>>url:http://www.w3.org/TR/xmlschema-2/#adding-durations-to-dateTimes]][[->>url:http://www.w3.org/TR/xmlschema-2/#adding-durations-to-dateTimes]][[durations>>url:http://www.w3.org/TR/xmlschema-2/#adding-durations-to-dateTimes]][[->>url:http://www.w3.org/TR/xmlschema-2/#adding-durations-to-dateTimes]][[to>>url:http://www.w3.org/TR/xmlschema-2/#adding-durations-to-dateTimes]][[dateTimes>>url:http://www.w3.org/TR/xmlschema-2/#adding-durations-to-dateTimes]][[ >>url:http://www.w3.org/TR/xmlschema-2/#adding-durations-to-dateTimes]]for further details.
2339
2340 [[~[4~]>>path:#_ftnref4]] The Validation and Transformation Language is a standard language designed and published under the SDMX initiative. VTL is described in the VTL User and Reference Guides available on the SDMX website [[https:~~/~~/sdmx.org>>url:https://sdmx.org/]][[.>>url:https://sdmx.org/]]
2341
2342 [[~[5~]>>path:#_ftnref5]] See also the section “VTL-DL Rulesets” in the VTL Reference Manual.
2343
2344 [[~[6~]>>path:#_ftnref6]] The VTLMapping are used also for User Defined Operators (UDO). Although UDOperators are envisaged to be defined on generic operands, so that the specific artefacts to be manipulated are passed as parameters at their invocation, it is also possible that an UDOperator invokes directly some specific SDMX artefacts. These SDMX artefacts have to be mapped to the corresponding aliases used in the definition of the UDO through the VtlMappingScheme and VtlMapping classes as well.
2345
2346 [[~[7~]>>path:#_ftnref7]] For a complete description of the structure of the URN see the SDMX 2.1 Standards - Section 5 - Registry Specifications, paragraph 6.2.2 (“Universal Resource Name (URN)”).
2347
2348 [[~[8~]>>path:#_ftnref8]] The container-object-id can repeat and may not be present.
2349
2350 [[~[9~]>>path:#_ftnref9]] i.e., the artefact belongs to a maintainable class
2351
2352 [[~[10~]>>path:#_ftnref10]] Since these references to SDMX objects include non-permitted characters as per the VTL ID notation, they need to be included between single quotes, according to the VTL rules for irregular names.
2353
2354 [[~[11~]>>path:#_ftnref11]] For the syntax of the VTL operators see the VTL Reference Manual
2355
2356 [[~[12~]>>path:#_ftnref12]] In case the invoked artefact is a VTL component, which can be invoked only within the invocation of a
2357
2358 VTL data set (SDMX dataflow), the specific SDMX class-name (e.g. Dimension, MeasureDimension, TimeDimension, PrimaryMeasure or DataAttribute) can be deduced from the data structure of the SDMX Dataflow which the component belongs to. 
2359
2360 [[~[13~]>>path:#_ftnref13]] If the Agency is composite (for example AgencyA.Dept1.Unit2), the agency is considered different even if only part of the composite name is different (for example AgencyA.Dept1.Unit3 is a different Agency than the previous one). Moreover the agency-id cannot be omitted in part (i.e., if a  TransformationScheme owned by AgencyA.Dept1.Unit2 references an artefact coming from AgencyA.Dept1.Unit3, the specification of the agency-id becomes mandatory and must be complete, without omitting the possibly equal parts like AgencyA.Dept1)
2361
2362 [[~[14~]>>path:#_ftnref14]] Single quotes are needed because this reference is not a VTL regular name.
2363
2364 [[~[15~]>>path:#_ftnref15]] Single quotes are not needed in this case because CL_FREQ is a VTL regular name.
2365
2366 [[~[16~]>>path:#_ftnref16]] The result DFR(1.0)  is be equal to DF1(1.0) save that the component SECTOR is called SEC
2367
2368 [[~[17~]>>path:#_ftnref17]] Rulesets of this kind cannot be reused when the referenced Concept has a different representation.
2369
2370 [[~[18~]>>path:#_ftnref18]] See also the section “VTL-DL Rulesets” in the VTL Reference Manual.
2371
2372 [[~[19~]>>path:#_ftnref19]] If a calculated artefact is persistent, it needs a persistent definition, i.e. a SDMX definition in a SDMX environment. Also possible calculated artefact that are not persistent may require a SDMX definition, for example when the result of a non-persistent calculation is disseminated through SDMX tools (like an inquiry tool).
2373
2374 [[~[20~]>>path:#_ftnref20]] See the VTL 2.0 User Manual
2375
2376 [[~[21~]>>path:#_ftnref21]] See the SDMX 2.1 Section 2 – Information Model
2377
2378 [[~[22~]>>path:#_ftnref22]] Besides the mapping between one SDMX Dataflow and one VTL Data Set, it is also possible to map distinct parts of a SDMX Dataflow to different VTL Data Set, as explained in a following paragraph.
2379
2380 [[~[23~]>>path:#_ftnref23]] The SDMX community is evaluating the opportunity of allowing more than one measure component in a DataStructureDefinition in the next SDMX major version.
2381
2382 [[~[24~]>>path:#_ftnref24]] If future SDMX major versions will allow multi-measures data structures, this method is expected to  become applicable even if the VTL data structure has more than one measure
2383
2384 [[~[25~]>>path:#_ftnref25]] The kind of mapping explained here works in combination with a SDMX specific naming convention that requires pre-processing before parsing the VTL expressions. As highlighted below, the identifiers of the VTL datasets are a shortcut of some specific VTL operators applied to the SDMX Dataflows. This is not safe to use outside an SDMX context, as the naming convention may have no meaning there.
2385
2386 [[~[26~]>>path:#_ftnref26]] 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 dimension components of the Dataflow except the time Dimension. 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.
2387
2388 [[~[27~]>>path:#_ftnref27]] 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 datasets.
2389
2390 [[~[28~]>>path:#_ftnref28]] This definition is made through the ToVtlSubspace and ToVtlSpaceKey classes and/or the FromVtlSuperspace  and FromVtlSpaceKey classes, depending on the direction of the mapping (“key” means “dimension”). The mapping of Dataflow subsets can be applied independently in the two directions, also according to different Dimensions.  When no Dimension is declared for a given direction, it is assumed that the option of mapping different parts of a SDMX Dataflow to different VTL datasets is not used.
2391
2392 [[~[29~]>>path:#_ftnref29]] As a consequence of this formalism, a slash in the name of the VTL dataset assumes the specific meaning of separator between the name of the Dataflow and the values of some of its Dimensions.
2393
2394 [[~[30~]>>path:#_ftnref30]] This is the order in which the dimensions are defined in the ToVtlSpaceKey class or in the FromVtlSpaceKey class, depending on the direction of the mapping.
2395
2396 [[~[31~]>>path:#_ftnref31]] It should be remembered that, according to the VTL consistency rules, a given VTL dataset cannot be the result of more than one VTL transformation.
2397
2398 [[~[32~]>>path:#_ftnref32]] If these dimensions would not be dropped, taking into account that the typical binary VTL operations at dataset level (+, -, *, / and so on) are executed on the observations having matching identifiers, the VTL datasets resulting from this kind of mapping would have non-matching values for the mapping dimensions (e.g. POPULATION and COUNTRY), therefore it would not be possible to compose the resulting VTL datasets one another  (e.g. it would not be possible to calculate the population ratio between USA and CANADA). ^^ ^^
2399
2400 [[~[33~]>>path:#_ftnref33]] In case  the ordered concatenation notation is used, the VTL Transformation described above, e.g.
2401
2402 ‘DF1(1.0)/POPULATION.USA’ :=  DF1(1.0) [ sub  INDICATOR=“POPULATION”, COUNTRY=“USA”], is implicitly executed and, in order to test the overall compliance of the VTL program to the VTL consistency rules, it has to be considered as part of the VTL program even if it is not explicitly coded.
2403
2404 [[~[34~]>>path:#_ftnref34]] If the whole DF2(1.0) is calculated by means of just one VTL transformation,  then the mapping between the SDMX dataflow and the corresponding VTL dataset is one-to-one and this kind of mapping (one SDMX Dataflow to many VTL datasets) does not apply..
2405
2406 [[~[35~]>>path:#_ftnref35]] This is possible as each VTL dataset corresponds to one particular combination of values of INDICATOR and COUNTRY
2407
2408 [[~[36~]>>path:#_ftnref36]] The mapping dimensions are defined as FromVtlSpaceKeys of the FromVtlSuperSpace of the ,,VtlDataflowMapping,, relevant to DF2(1.0)
2409
2410 [[~[37~]>>path:#_ftnref37]] the symbol of the VTL persistent assignment is used (<-)
2411
2412 [[~[38~]>>path:#_ftnref38]] The result is persistent in this example but it can be also non persistent if needed.
2413
2414 [[~[39~]>>path:#_ftnref39]] In case the ordered concatenation notation from VTL to SDMX is used, the set of transformations described above is implicitly performed; therefore, in order to test the overall compliance of the VTL program to the VTL consistency rules, these implicit transformations have to be considered as part of the VTL program even if they are not explicitly coded.
2415
2416 [[~[40~]>>path:#_ftnref40]] Through SDMX Constraints, it is possible to specify the values that a Component of a Dataflow can assume.
2417
2418 [[~[41~]>>path:#_ftnref41]] By using represented variables, VTL can assume that data structures having the same variables as identifiers can be composed one another because the correspondent values can match.
2419
2420 [[~[42~]>>path:#_ftnref42]] A Concept becomes a Component in a DataStructureDefinition, and Components can have different LocalRepresentations in different DataStructureDefinitions, also overriding the (possible) base representation of the Concept.
2421
2422 [[~[43~]>>path:#_ftnref43]] The representation given in the DSD should obviously be compatible with the VTL data type.