Eurostat

Standardisation within the ESS: SDMX present and future

Luxembourg, October 2015

Marco PellegrinoEurostat, Statistical Office of the European Union

marco.pellegrino@ec.europa.eu

Eurostat

Outline

• Evolution of SDMX

• Standards integration- Examples

• Opportunities and challenges- All good standards change

2

Eurostat

3

A model to describe statistical data and metadata

A standard for automated communication from machine to machine

A technology supporting standardised IT tools

A common language for statistics Statisticians agree to use a common description for data and metadata The data exchange process is then driven by this common description Data descriptions are made available for everybody who wants to

understand and reuse the data

SDMX provides

EurostatEurostat

Why do we need a model?

• To define and describe statistical processes in a coherent way

• To standardize process terminology

• To compare and benchmark processes within and between organisations

• To identify synergies between processes

• To inform decisions on systems architectures and organisation of resources

4

EurostatEurostat

5

The SDMXComponents

Describe statistics in a standard way Objects and their relationships

Data Structure Definition (DSD), Concepts, Code List

Central management and standard access SDMX Registry, SDMX Web Services

Cross Domain Concepts Cross Domain Code Lists Statistical Domains Metadata Common Vocabulary

Push Provider generates and sends file to receiver

Pull Provider opens web service to data Receiver downloads regularly

Hub Special case of pull: receiver downloads on end user request

Eurostat

• The same information is needed for exchange between different steps in a statistical production process.

• The use of SDMX throughout the process, in combination with a metadata registry (central storage of definitions, classifications, etc.) makes it more efficient and coherent to implement changes, e.g. in definitions

• Metadata-driven systems

6

Broadening the scope of SDMX

Eurostat

Standard metadata layer for the description and use of data and metadata throughout the process

7

Broadening the scope of SDMX

Eurostat

GSBPM and SDMX: towards a more complete picture

8

Eurostat

SDMX and standards integration

• SDMX promotes an incremental movement towards a data and metadata sharing model with the production of comparable and accurate statistics.

• The increasing use of SDMX:a) improves the quality of the statistical processb) enables simplified exchange and dissemination processes, improving timeliness and accessibility

• Statistical integration goes hand-in-hand with technical integration and standardisation.

9

Eurostat

Building bridges

10

…not walls

Eurostat

11

Building bridges

Eurostat

SDMX and Linked Open Data

• Based on RDF - Resource Description Framework - a family of specifications published by W3C allowing for machine-actionable, semantically rich linking of things found on the Web.

• Main RDF vocabulary for statistical data: → Data Cube VocabularySimplified version of the SDMX model covering data structures

12

https://open-data.europa.eu/en/linked-data

Building bridges

SDMX Data Structure Definition

RDF Data Cube Vocabularyhttp://www.w3.org/TR/2014/REC-vocab-data-cube-20140116

SDMX Data Set structured by

dim

ensio

nality

Latest Version

The RDF Data Cube VocabularyW3C Recommendation 16 January 2014This version:

http://www.w3.org/TR/2014/REC-vocab-data-cube-20140116/

14

5 star-schema of Linked Open Data

★ Make your stuff available on the Web (whatever format) under an open license.

★★ Make it available as structured data (e.g., Excel instead of image scan of a table).

★★★ Use non-proprietary formats (e.g., CSV instead of Excel).

★★★★ Use URIs to denote things, so that people can point at your stuff.

★★★★★ Link your data to other data to provide context.

Slide 15

The Data Cube Vocabulary

DataCube is a W3C recommendation, and has gained some momentum

Data producers using SDMX can also publish in the Data Cube Vocabulary (DCV)

As with any other RDF publication, the applications processing the RDF must understand the DCV data model to make sense of the data

Therefore applications wishing to process any additional information added to the DCV triples need to understand the model of the attached data

16

The SDMX Perspective

If you are using SDMX today (GESMES or XML), what does this mean?

Most DataCube implementation today is being done by organizations that don’t use SDMX-ML

For statistical organisations there is an increasing interest in RDF and there is a need to be able to integrate DataCube as an alternative query and delivery sourced originally from existing SDMX-based systems

17

SDMX and RDF: Scenario

RDF File

StatisticalDissemination

System

DataCube Writer

SDMX-ML File

SDMX-ML to RDF Transformer

Either

Or

Using SDMX Component Architecture

SDMXWriter Interface

18

Scenario : Publish RDF triples as flat files

Publish to a server exposed to the web

Packaged in a meaningful way using named graphs• Data by data set• Structures (all in one file or codelists and concepts in one file

and DSDs in another file

Considerations• Needs to be kept up to date (either republish as a replace or as

an incremental update)• Simple Approach but not easily queryable (discovery and

linking tools typically work with SPARQL endpoints)

19

SDMX and RDF: Scenario

Triple Store(DataCube)

StatisticalDissemination

System

RDF Service

SPARQL

SDMX-MLFile

SDMX-ML File to RDF Transformer

Either

Or

DataCube Writer

SDMXWriter Interface

20

Scenario : Populate a SPARQL endpoint

•Deploy RDF triple in a “triple store”• Dedicated database system that natively understands SPARQL

queries• Supported by many RDF tools, some supporting a variety of

flavours of RDF (XML, TURTLE, N-Triples)• Data could be updated at the level of dataflow

Considerations• Good support for linking (the reason for LOD)• Good support for cross dataflow queries• Data with some common dimensions

21

Considerations

• If RDF is treated as a completely separate syntax, then the burden of data management is doubled

• If it is treated as a delivery format (just another data writer) then it is relatively easy to implement

• Up-front cost for tools development

• Low ongoing maintenance

• The benefits of RDF-based technology are realized in a cost-effective manner

22

Eurostat

Data validation “Technical”

- Covered by SDMX today

- Format Check (SDMX-ML)- Codes exist (SDMX DSD)- Codes used correctly(Dataflow & Constraint)

“Statistical Domain”- Not yet covered by SDMX (VTL)

- Value check- Time series- Revisions- Validation expressions

Building bridges

Eurostat

VTL: Validation and Transformation Language

24

Standard language for defining validation and transformation rules• Validation (now)• Transformation (partially now, to be enriched at a later stage)

Main goals• Define and preserve validation and transformation rules • Exchange and share rules• Apply rules in industrialized processes • Apply to several standards (e.g. SDMX, DDI, GSIM) thanks to a

generic information model

Eurostat

DDI is split into 2 branches:• DDI-Codebook (DDI-C): DDI-C is a light-weight version of the standard, intended

primarily to document simple survey data.• DDI-Lifecycle (DDI-L or DDI 3+): DDI-L is designed to document and manage

data across the entire life cycle, from conceptualization to data publication and analysis and beyond. DDI-L is currently being evaluated in several statistical organizations across the world.

The DDI Lifecycle standard provides a data model for describing surveys in a very detailed fashion using XML.• This can support many parts of the process of survey management particularly in

the case of households surveys. E.g. exchange between question banks and data collection applications, generation of collection instruments, …

25

DDI: The Data Documentation Initiative

Eurostat

DDI: The DDI data lifecycle model

26

Eurostat

SDMX and DDI• SDMX can provide:• Metadata describing the structure

of dimensional data• Stand-alone metadata sets

(“reference metadata”)• Formats for dimensional data• A model of data reporting and

dissemination• Standard registry interfaces,

providing a catalogue of resources• Guidelines for deploying standard

web services• A way of describing statistical

processes

27

DDI Lifecycle can provide a very detailed set of metadata, covering:

• Surveys and processing of microdata

• Structure of data files, including hierarchical files and complex relationships

• Archiving of data files and their metadata

• Tabulation and processing of data into tables

• Link between microdata variables and resulting aggregates

Building bridges

Eurostat

SDMX and DDI: similarities and differences

• Both standards use a similar model for identifiable, versionable and maintainable artefacts

• Both standards use “schemes”, as packages for lists of items, and XML “schemas”

• Both standards are designed to support reuse

• DDI has much more detailed metadata at the level of the study domain, and provides more complete descriptions of the processing of data

• SDMX provides more architectural components to support registration, reporting/collecting and exchange, and has a solid information model

28

29

Other relevant standards

Geospatial standards

DDI

SDMX

GSIMConceptual model

Implementationstandards

30

Eurostat

Opportunities and challenges• SDMX is interacting well with other standards (GSIM, DDI,

RDF Linked Open Data, JSON) and this “complementarity” opens us new perspectives for the innovation of statistical processes.

• Common data validation and processing procedures are required (from structural validation to content).

• Better metadata-driven statistical production systems, with the use of standards throughout the processes in combination with a metadata registry.

• Better maintenance and developments of SDMX (e.g. support to use cases, new functions, more formats, etc.) using the wealth of its Information Model.

31

Eurostat

All good standards change

32

Version 1.0

Version 2.0

Version 2.1

September 2004 April 2011 November 2005

Version 2.0

SDMX-EDISDMX-MLSDMX Registry

Version 1.0

GESMES/TS

• Too much change may discourage adoption

But…

• not giving users the functionalities they want would also discourage adoption

Eurostat

Where do we want SDMX to be, in 2020?

“Would you tell me, please, which way I ought to go from here?”

“That depends a good deal on where you want to get to,” said the Cat.

“I don’t much care where–” said Alice.

“Then it doesn’t matter which way you go,” said the Cat.

“–so long as I get SOMEWHERE,” Alice added as an explanation.

“Oh, you’re sure to do that,” said the Cat, “if you only walk long enough.”

(Alice’s Adventures in Wonderland, Chapter 6)

33

EurostatEurostat

Where are we?

• Dramatic changes in the environment of official statistics producers (e.g. data deluge)

• Modernization of statistical information system seen as a question of survival for the sector of official statistics

• Standardization viewed as a key enabler for modernization

• Standards-based industrialization of statistical production

34

Eurostat

SDMX 2020

Main challenges for the years to come:• Strengthening implementation • Facilitating data consumption • Supporting statistical process innovation • Enhancing communication • Investing on training and capacity-building

Action Plan

SWG/TWG's work plan

Eurostat

Thanks for your attention!

Marco.Pellegrino@ec.europa.eu

36

SDMX present and future

« If you are not sure where you are goingyou will finish someplace else »