SDMX Basics Core Elements Information Model Data Structure Definition (DSD) SDMX-ML Messages

1Eurostat Unit B5 – Statistical Information TechnologiesSDMX Basics – October 2011

SDMX Basics

Core ElementsInformation ModelData Structure Definition (DSD)SDMX-ML MessagesMajor changes in SDMX v 2.1


THE SDMX COMPONENTS

Technical Specifications

The SDMX

Information Model

Guidelines to

Hamonise Content

The Content Oriented Guidelines (COG)

Tools

IT Architectures for data exchange

SDMX compliant tools


The SDMX Information Model is a meta-model describing the objects involved in:

The collection The dissemination The publication of aggregated statistics and related metadata

The abstract model is like a structured set of containers

Everything in SDMX is model-driven: All messages and interfaces are implementations of the

information model

THE SDMX INFORMATION MODEL


SDMX INFORMATION MODEL – SCOPE

DATA & METADATA

FLOWS

Structure Definition

Category Scheme

Category

ConstraintProvision Agreement

Data Provider

Data & Metadata set


SDMX Information Model


STATISTICAL DATA & METADATA

Time series data representation

Cross-sectional data representation

Statistical Data (Figures)

Statistical Metadata (Identifiers, Descriptors)

Structural metadata

Reference metadata

Statistical Metadata (Methodology, Quality)


Statistical data - Cube

Time

20052006

Country FR ITESAT

Tourism activity

A100

B010

B020

2007

Time series

Cross-section for 2006

time/activity B0102005 81742006 81382007 8052

Number of tourist campsites - France - annual data

geo/activity B010AT 542ES 1216FR 8138IT 2510

Number of tourist campsites - national - 2006

817481388052

542121681382510

STATISTICAL DATA & METADATATwo different ways to represent data


STATISTICAL DATA - TIME SERIES REPRESENTATION


STATISTICAL DATA - CROSS-SECTIONAL REPRESENTATION


From a number to statistical data

11353511 11353511

STRUCTURAL METADATA Introduction


CONCEPTS

STRUCTURAL METADATA

Identify and describe data

Dimension, Attribute or

Measure in a DSD to define a Data set’s structure

Attributes in a MSD to define the

structure of a Metadata set


I ndicatorTime

2002A00 33411 2374 61479

2003A00 33480 2530 58526

2004A00 33518 2529 56586

2005A00 33527 2411 68385

2006A00 33768 2510 68376

2007A00 34058 2587 61810

Number of touristic establishmentsin I taly, annual data

A100Hotels and similar

B010Tourist Campsites

B020Holiday dwellings

STRUCTURAL METADATAFrom a statistical table to its descriptor concepts


STRUCTURAL METADATA – CONCEPTS AND ROLES


DSD

STRUCTURAL METADATA: DATA STRUCTURE DEFINTION To easily exchange and process data, we first define a standard container based on the structure of the real statistical table: The Data Structure Definition (DSD)

Code lists

Code lists

Code lists

Dimensions

Attributes

Measures

Concepts

UNITTIME_PERIOD

COUNTRY

OBSERVATIONS

The DSD can be seen as a "logical container" for a specific set of data that we want to exchange. It includes the concepts that represent the data, gives them roles (Dimension, Measure, Attributes) and links them to code lists.


ELEMENTS OF A DATA STRUCTURE DEFINITION

16Eurostat Unit B5 – Statistical Information TechnologiesSDMX Basics – 10-11 and 14-15 March 2011

DatasetDSD

SDMX does not introduce any new concept for statisticians. It just provides a framework for what statisticians already know.

Code lists

Observations

Table structure The SMDX dataset is a standard container in which statistical data are represented together with the structural metadata, according to the DSD.

SDMX INFORMATION MODEL - DATA SET

Now you have an easy way to exchange and process data and metadata automatically.


DATA SET

KEYKEYKEYGROUP KEYGROUP KEYGROUP KEY

KEY VALUESKEY VALUESKEY VALUES

TIME PERIODOBSERVATIO

N VALUE

ATTRIBUTEVALUE

Attribute attachmentAttribute attachment

Cross-section

Time series







REFERENCE METADATA


Reference Metadata Set

SDMX INFORMATION MODEL - METADATA SETConcepts


SDMX INFORMATION MODEL – DATA & METADATA FLOW

DATA & METADATA

FLOWS

Structure Definition

Category Scheme

Category


Data Provider

Data & Metadata set


SDMX INFORMATION MODEL – CATEGORIES


SDMX IM – DATA PROVIDERS & PROVISION AGREEMENT

Production and dissemination of Statistical data

Production and dissemination of

Reference Metadata


DATA & METADATA

FLOWS


SDMX IM - CONSTRAINTS


SDMX IM - CONSTRAINTS

Example: A data provider can restrict his reporting of monthly data to only some months.

Example: A data provider can restrict his reporting of data to subsets of statistical cubes.


SDMX IM - SUMMARY


THE SDMX COMPONENTS

Technical Specifications

The SDMX

Information Model

Guidelines to

Hamonise Content

The Content Oriented Guidelines (COG)

Tools

IT Architectures for data exchange

SDMX compliant tools


IT ARCHITECTURES FOR DATA EXCHANGE


SDMX REGISTRY

REGISTRY


SDMX REGISTRY DEMONSTRATION


SDMX Data Structure Definition (DSD)

33

COMPLIANCE & IMPLEMENTATION

Generally the following four steps need to be done:

1.Preparation: The statisticians from the organisations involved in the data exchange describe the data and the different dataflows, dataset and provision agreements.

2.Compliance: you create all the necessary objects according to the SDMX Technical Specifications.

3.Implementation: Now we put into practice. Standard software is installed and configured to use the DSDs. The exchange process is set up and tested.

4.Production: use the objects in the production process. SDMX implementation is achieved when the data and metadata exchanges within the domain are carried out according to SDMX-compliant specifications.


Define the DSD– List of concepts (Concept scheme)– Roles of concepts (Dimension, Attribute, Measure)– Code lists

Provide the related Dataflows (e.g. STSRTD_TURN_M, DEMOGRAPHY_RQ)

CREATE ALL THE NECESSARY OBJECTS


THE STEPS TO BUILD A DATA STRUCTURE DEFINITIONIdentification of the descriptor concepts for the data Choose the type of data representation (Time Series

and Cross-sectional )

Choice of Cross Domain code lists or definition of specific code

lists for coded conceptsDefinition of the text format

for non coded concepts

Definition of the concept role (Dimension, Attribute or Measure)

Define Dimensions for Time Series and Cross-sectional

data representation

Define Attributes with the attachment levels Time

Series and Cross-sectional data representation

Define Time Series primary measure and/or Cross-

sectional measures with their measure concepts

Create the defined artefacts in a SDMX Data Structure Definition tool (e.g. DSW)

1

2

3

4

5


1- IDENTIFICATION OF THE DESCRIPTOR CONCEPTS


2 – DEFINE THE CODE LISTS


Cross-sectional slice

Time s

eries

slice

Statistical data - Cube

Country ES ITFRAT

Tourism activity

A100

B010

B020

Time

20052006

2007

Time series

Cross-section for 2006

geo/activity B010AT 542ES 1216FR 8138IT 2510

Number of tourist campsites - national - 2006

125012161220

542121681382510

3- CHOOSE THE TYPE OF DATA REPRESENTATION TIME SERIES (TS) / CROSS-SECTIONAL (CS)


DATA REPRESENTATION – TIME SERIES


DATA REPRESENTATION – CROSS-SECTIONAL


4- DEFINE ROLES OF CONCEPTS AND LIST OF CONCEPTS


5 – DEFINE GROUPS AND ATTRIBUTE ATTACHEMENTS

43Eurostat Unit B5 – Statistical Information TechnologiesSDMX Basics – October 2011Eurostat Unit B5 – Statistical Information TechnologiesSDMX Training for Statisticians – March 2010

6 – DEFINE THE VIEW OF THE DATA STRUCTURE


Year MonthTurnover

index Status Confidentiality2002 January 84.5 actual free2002 February 85.6 actual free2002 March 95.4 actual free2002 April 106.2 actual free2002 May 98.0 actual free2002 June 95.3 actual free2002 July 105.4 actual free2002 August 107.1 actual free2002 September 105.2 actual free2002 October 109.4 actual free2002 November 104.5 actual free2002 December 111.9 actual free2003 January 89.1 provisional free2003 February 88.3 provisional free2003 March 96.1 provisional free

Source: National Statistical Service of GreeceData prepared to be transmitted to the European Commission (including EUROSTAT)

Table 1. Deflated turnover index (on volume of sales) for retail trade for Greece (no adjustment). Reference period: January 2002 to March 2003.

(monthly data - Base year: 2000)

EXAMPLE: STS SAMPLE DATASET

Dimensions

Attributes

Primary Measure

Dimensions



STS_INDICATORTITLE STS_ACTIVITY

REFERENCE_AREAFREQ STS_ BASE_YEAR

ADJT


OBS_STATUSOBS_VALUE

REFERENCE_PERIOD

OBS_CONF

STS_INSTITUTION



M;GR;N;TOTV;NS5201;1;2000;200201;88.8;A;FM;GR;N;TOTV;NS5201;1;2000;200202;84.7;A;FM;GR;N;TOTV;NS5201;1;2000;200203;88.8;A;FM;GR;N;TOTV;NS5201;1;2000;200204;93.0;A;FM;GR;N;TOTV;NS5201;1;2000;200205;60.8;A;FM;GR;N;TOTV;NS5201;1;2000;200206;78.2;A;FM;GR;N;TOTV;NS5201;1;2000;200207;89.9;A;F

AttributesPrimary MeasureDimensions

M;GR;N;TOTV;NS5201;1;2000;200201;88.8;A,F

Reference PeriodGroup

EXAMPLE: STS SAMPLE DATASETIDENTIYING CONCEPTS AND GROUPING SERIES IN CSV FILES


DSD OF DATAFLOW STSRTD_IND_M

Concept Concept ID

frequency FREQ reference area REF_AREA

adjustment ADJUSTMENT

type of index STS_INDICATOR

activity STS_ACTIVITY

type of institution STS_INSTITUTION

base year STS_BASE_YEAR reference period TIME_PERIOD

turnover idex OBS_VALUE status OBS_STATUS

confidentiality OBS_CONF time duration set TIME_FORMAT

Title TITLEdecimals DECIMALS

Example of value Remark

M Monthly GR Greece N No

TOVV Turnover deflated (volume of sales)

NS5201 Retail trade

11=NSI or 2=National

Bbank 2000

200201 CCYYMM 108.6 observation

A actual data F Free of publication

P1M ISO8601 1 One

Code List

CL_FREQ CL_AREA_EE

CL_ADJUSTMENT

CL_STS_INDICATOR CL_STS_ACTIVITY

CL_STS_INSTITUTION CL_STS_BASE_YEAR

CL_OBS_STATUS CL_OBS_CONF

CL_TIME_FORMAT

CL_DECIMALS

Dimensions

Measure Attributes

Attachment level

Obs Obs

Series Group

Group

List of variables ValuesCodesRolesFootnotes


STRUCTURE OF THE DATASET FOR TIME SERIES

Group of series

Series M;GR;N;TOTV;NS5201;1;2000;200201;88.8;A;FM;GR;N;TOTV;NS5201;1;2000;200202;84.7;A;FM;GR;N;TOTV;NS5201;1;2000;200203;88.8;A;FM;GR;N;TOTV;NS5201;1;2000;200204;93.0;A;F

REF_AREA="GR" ADJUSTMENT="N" STS_INDICATOR="TOTV" STS_ACTIVITY="NS5201" STS_INSTITUTION="1" STS_BASE_YEAR="2000" DECIMAL="1" TITLE="Retail trade"

Attributes and attachment level: group

M;GR;N;TOTV;N15220;1;2000;200201;60.8;A;FM;GR;N;TOTV;N15220;1;2000;200202;78.2;A;FM;GR;N;TOTV;N15220;1;2000;200203;89.9;A;F

Group of series REF_AREA="GR" ADJUSTMENT="N" STS_INDICATOR="TOTV" STS_ACTIVITY="N15220" STS_INSTITUTION="1" STS_BASE_YEAR="2000" DECIMAL="1" TITLE="Retail sale of food"

Attributes can be attached to groups

Series

Series

Series

Series

Series

Series


Definition of Series 1

M;GR;N;TOTV;NS0006;1;2000;200201;88.8;A;FM;GR;N;TOTV;NS0006;1;2000;200202;84.7;A;FM;GR;N;TOTV;NS0006;1;2000;200203;88.8;A;F

FREQ="M" REF_AREA="GR" ADJUSTMENT="N" STS_INDICATOR="TOTV" STS_ACTIVITY="NS0006" STS_INSTITUTION="1" STS_BASE_YEAR="2000" TIME_FORMAT="P1M"

Attributes and attachment level: series

M;GR;N;TOTV;N14500;1;2000;200201;60.8;A;FM;GR;N;TOTV;NS0006;1;2000;200202;78.2;A;FM;GR;N;TOTV;NS0006;1;2000;200203;89.9;A;F


FREQ="M" REF_AREA="GR" ADJUSTMENT="N" STS_INDICATOR="TOTV" STS_ACTIVITY="N14500" STS_INSTITUTION="1" STS_BASE_YEAR="2000" TIME_FORMAT="P1M"

Attributes can be attached to seriesAttributes can be attached to series

Series 1

Series 1

Series 1

Series 2

Series 2

Series 2




FREQ="M" REF_AREA="GR" ADJUSTMENT="N" STS_INDICATOR="TOTV" STS_ACTIVITY="NS0006" STS_INSTITUTION="1" STS_BASE_YEAR="2000" TIME_FORMAT="P1M"

Attributes and attachment level: series

Attributes can be attached to observations

Definition of Observation 1

TIME_PERIOD="200201" OBS_VALUE="88.8" OBS_STATUS="A" OBS_CONF="F"





M;GR;N;TOTV;NS0006;1;2000;200201;88.8;A;FM;GR;N;TOTV;NS0006;1;2000;200202;84.7;A;FM;GR;N;TOTV;NS0006;1;2000;200203;88.8;A;F

Observation 1

Observation 2

Observation 3

CSV



EXAMPLE 2: DEMOGRAPHY SAMPLE DATASET

Measures

AttributesDimensionsDimensions


TITLE

TIME_PERIODTIME_PERIOD

TAB_NUM

REV_NUM OBS_STATUSFREQFREQ

COUNTRYCOUNTRY

Dimensions attached to the dataset level

Dimensions attached to the group level



OBS-VALUE

DEMODEMO

SEXSEXUNIT

MALE

Dimensions attached to the observation level

Measure Dimension

FEMALE TOTAL



DSD FOR DATAFLOW: DEMOGRAPHY_RQ Attachment

level Concept Concept ID Code List Values

reference period TIME_PERIOD 2005

reporting country COUNTRY CL_COUNTRY Fi (for Finland)

sex SEX CL_SEX demographic

characteristic DEMO CL_DEMO # of births, etc. frequency FREQ CL_FREQ A (for annual) Male MALE number of persons Female FEMALE number of persons Total TOTAL number of persons

dataset title TITLE dataset version REV_NUM 1st revision

dataset reference

table TAB_NUM RQFI05V1 Section (Series) unit of value UNIT CL_UNIT PERS (for persons)

observation status OBS_STATUS CL_OBS_STATUS provisional data

observation series time duration set TIME_FORMAT CL_TIME_FORMAT P1M

Concept Concept ID Code List Values

TIME_PERIOD 2005 COUNTRY CL_COUNTRY Fi (for Finland) sex SEX CL_SEX

M (male), F (Female),

DEMO CL_DEMO # of births, etc. frequency FREQ CL_FREQ A (for annual) Male MALE number of persons Female FEMALE number of persons Total TOTAL number of persons

dataset title TITLE Title of the

exchanged dataset dataset version REV_NUM 1st revision

dataset TAB_NUM RQFI05V1

unit of value UNIT CL_UNIT PERS (for persons) observation status OBS_STATUS CL_OBS_STATUS provisional data

observation TIME_FORMAT CL_TIME_FORMAT P1M


TIME_PERIOD 2005 COUNTRY CL_COUNTRY Fi (for Finland) sex SEX CL_SEX DEMO CL_DEMO # of births, etc. frequency FREQ CL_FREQ A (for annual) Male MALE number of persons Female FEMALE number of persons Total TOTAL number of persons






TIME_PERIOD 2005 COUNTRY CL_COUNTRY Fi (for Finland) sex SEX CL_SEX DEMO CL_DEMO # of births, etc. frequency FREQ CL_FREQ A (for annual) Male MALE number of persons Female FEMALE number of persons Total TOTAL number of persons





Dimensions

Cross-sectional Measures

Attributes

Attachment level Concept Concept ID Code List Values

reference period TIME_PERIOD 2005

reporting country COUNTRY CL_COUNTRY Fi (for Finland)

sex SEX CL_SEX demographic

characteristic DEMO CL_DEMO # of births, etc. frequency FREQ CL_FREQ A (for annual) Male MALE number of persons Female FEMALE number of persons Total TOTAL number of persons


dataset reference

table TAB_NUM RQFI05V1 Section (Series) unit of value UNIT CL_UNIT PERS (for persons)

observation status OBS_STATUS CL_OBS_STATUS provisional data

observation series time duration set TIME_FORMAT CL_TIME_FORMAT P1M

Dimensions

Cross-sectional Measures

Attributes


Dataset

Attributes and attachment level

Attribute attached to group

COUNTRY="FI"

Group REF_PERIOD="2005" FREQ="A" TIME_FORMAT="P1Y"

Section DECI="0" UNIT="PERS" UNIT_MULT="0"

Dimension attached to dataset

Attributes attached to sections

Dimension attached to group

Observation FEMALE OBS_VALUE="35" DEMO="ADJT" OBS_STATUS="P"

Cross–sectional measureDimensions attached to observation

Attribute attached to observation

MALE OBS_VALUE="29400" DEMO="LBIRTHST" OBS_STATUS="P"

TOTAL OBS_VALUE="8986" DEMO="NETMT" OBS_STATUS="P"

Observation

Observation

STRUCTURE OF THE DATASET FOR CROSS SECTIONAL


Organisation Schemes

DSDs

Concept Schemes

Category Schemes

DataFlows

Code lists

CREATION OF THE DSDTHE SDMX OBJECTS RELATED TO THE DATA STRUCTURE


DSW – “standalone” desktop application

(replaced KeyFamily AccessDB tool)

Offline version of Eurostat’s SDMX Registry

Maintenance of SDMX v2.0 data and meta data

structures (create, modify, delete, query)

Import/Export SDMX-ML structures (validate

structure messages)

Import/Export GESMES/TS structure files

Reporting of structures

Advanced search features

Export metadata for use with the GENEDI tool

Data Authoring (building SDMX-ML sample datasets)

Interaction with any SDMX v2.0 compliant Registry

Query SDMX v2.0 Registry

Submit data structures to SDMX v2.0 Registry

SDMX Registry

Import/Export SDMX-ML messages

CREATION OF THE DSD: DATA STRUCTURE WIZARD


Example - DSD import / creationusing the DSW


LIFE DEMONSTRATION - DSD IMPORT / CREATION USING THE DATA STRUCTURE WIZARD


DATA STRUCTURE DEFINITIONID FISH_CATCH_A

Name Catches for all fishing areas

Version 1.0

AgencyID ESTAT

Valid From

Valid To

EXERCISE: CREATION OF THE DSD: FISH_CATCH_A


DIMENSIONS

Position in Key

CONCEPT REPRESENTATION

Dimension TypeID Name

CONCEPT SCHEME CODELISTTEXT

FORMATID VER AGENCY ID VER AGENCY

1 FREQ Frequency CS_FISHERIES 1.0 ESTAT CL_FREQ 1.1 ESTAT Frequency

2 REPORTING_AREACountry ISO3 codes (extended)

CS_FISHERIES 1.0 ESTATCL_REPORTING_AREA

1.0 ESTAT

3PRODUCTION_AREA

Production Area (from major area to sub-unit)

CS_FISHSTAT 1.0 FAOCL_PRODUCTION_AREA

1.0 FAO

4 SPECIESASFIS Species Alpha 3 Code

CS_FISHSTAT 1.0 FAOCL_SPECIES

1.0 FAO

TIME TIME_PERIOD Reference year CS_FISHERIES 1.0 ESTAT



MEASURES

TYPE

CONCEPT REPRESENTATIONMEASUR

E DIMENSI

ON

CODEID Name



Primary OBS_VALUE Value of the measureCS_FISHERIES

1.0 ESTAT N/A N/A

ATTRIBUTES

ATTACHMENT LEVEL

CONCEPT REPRESENTATION

ATTRIBUTE TYPE

ASSIGNMENT STATUSID Name



Observation UNIT unit CS_FISHERIES 1.0 ESTAT CL_UNIT 1.1 ESTAT C



SDMX Converter Data Structure Wizard

SDMX Technical Standard v2.0 (http://www.sdmx.org/index.php?page_id=16)

Help-desk: [email protected]

USEFUL LINKS


SDMX-ML Messages


Based on a common Information Model– SDMX-EDI (GESMES/TS)

• EDIFACT syntax• Time-series oriented – One format for Data

Sets– SDMX-ML

• XML syntax• Four different formats for Data Sets• Easier validation (XML based)

SYNTAXES FOR SDMX MESSAGES


Element Example id TEST0000 test true truncated false name FISH_AQ_TEST prepared 2010-30-01T09:30:47+01:00 senderid ESTAT sendername Eurostat sendercontactname G. Smith sendercontactdepartment Statistics sendercontactrole Response sendercontacttelephone 0210 2222222 sendercontactfax 0210 00010999 sendercontactx400 sendercontacturi www.sdmx.org sendercontactemail [email protected] receiverid NSI_GB receivername CSO receivercontactname P. Mustermann receivercontactdepartment Statistics receivercontactrole Statistician receivercontacttelephone 02101234567 receivercontactfax 02103810999 receivercontactx400 receivercontacturi www.sdmx.org receivercontactemail [email protected] datasetagency ESTAT datasetid FISH_AQX datasetaction Append extracted 2010-30-01T09:30:47+01:00 reportingbegin 2008-01-01T00:00:00 reportingend 2008-12-31T00:00:00 source DH lang en

SDMX DATA COMMON HEADERS


Equivalent representations for reporting Datasets

SDMX DATA MESSAGES

Version 2.0 Version 2.14 data messages, each with a distinct format.

GenericData

CrossSectional DataCompact Data

UtilityData

Therefore, there are now 4 data messages which are based on two general formats: • GenericData GenericTimeSeriesData• StructureSpecificData StructureSpecificTimeSeriesData

Phased out


EXAMPLE OF GENERIC SDMX-ML MESSAGE


EXAMPLE OF COMPACT SDMX-ML MESSAGE


EXAMPLE OF CROSS-SECTIONAL SDMX-ML MESSAGE


Equivalent formats

Generic SDMX-ML

Cross-sectional SDMX-ML

Compact SDMX-ML

Can be expanded to other formats (e.g. CSV, GESMES)

Based on the

same IM

Exceptions:

If a Cross-Sectional DSD does NOT contain a

time dimension

CONVERSIONS SDMX V2.0


Read the input message

Parsing Populate the data model of the tool

(based on the SDMX v2.0 information

model)

Write the converted message

Uses the data model to write the output message in the required

target format.

Information retrieved from the Registry

Data flow ID is used to retrieve the data flow definition from the

Registry.

The DSD ID, version and agencyID are retrieved from the data flow definition

and are used to acquire the DSD

SDMX CONVERTER


Possible conversionsCSV

Compact SDMX-ML

Generic SDMX-ML

Utility SDMX-ML

Cross-sectional SDMX-ML *

SDMX-EDI (GESMES/TS)

CSV

Compact SDMX-ML

Generic SDMX-ML

Utility SDMX-ML

Cross-sectional SDMX-ML

SDMX-EDI (GESMES/TS)

Main use: Conversion CSV Compact SDMX-ML

SDMX CONVERTER MAIN FUNCTIONALITY

SDMX training session on basic principles, Major Changes in version 2.1

Fabien JACQUET

SDMX Basics

MMMM 2011

Select the Input file Select the output file

Select the input and output formats

Select the DSD on the local driveIdentify a DSD to

download from the SDMX Registry

Identify a dataflow linked to the DSD to download from the SDMX Registry Select / manage

headers for CSV input formats

Select mapping / transoding tables

CSV parameters

GESMES representation for GESMES output

formats

Load / save the current settings

XML parameters for SDMX output formats


Conversion Example

77

Major changes in SDMX v 2.1


Overview of the changes

Structural Metadata– Data Structure Definition (DSD)– Metadata Structure Definition

(MSD)– Constraint– Code List– Organisation Scheme– Categorising Structures– Process– Provision Agreement– Transformations and

Expressions

Data Set– Message Changes– Structured Data

Mechanism Revised Metadata Set

– Message Changes– Alignment of Formats– Structured Metadata

Mechanism Revised


Data structure Definition (DSD) Support for non-time-series data structuresMeasure Dimension

DSD

Code lists

Code lists

Code lists

DimensionsAnd

Measure dimension

Attributes

Measures

Concepts

DSD

Version 2.0 Version 2.1

Measure Dimension

Dimensions

Attributes

Primary Measure

Concepts

Concept Scheme

Code lists

Code lists


Maintainable artefact

Constraint


Dataflow

Provision agreement

Constraint

Constraint

Registry Constraint

Dataflow Code list

Provision agreement

DSD


Code List

Common

Code listConstraint 1 Par

tial

DSD DSD

Constraint 2

Version 2.1


Categorising Structures


Category Scheme

Data/Metadata flow

Reference

Categorisation

Data/Metadataflow Code list

Category

ReferenceProvision

agreementDSD

Category

Only

Maintainable artefact



Message Changes

Data Set

4 data messages, each with a distinct format.

GenericData

CrossSectionalDataCompactData

UtilityData

Therefore, there are now 4 data messages which are based on two general formats: • GenericData o GenericTimeSeriesData• StructureSpecificData o StructureSpecificTimeSeriesData

Phased out

Documents

SDMX Basics Core Elements Information Model Data Structure Definition (DSD) SDMX-ML Messages