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Metadata use in the Statistical Value Chain

Metadata use in the Statistical Value Chain. UNECE-Eurostat-OECD Meeting on Management of Statistical Information Systems MSIS 2008 Luxembourg, 7-9 April 2008 Georges Pongas Adam Wroński. Content. Introduction Operational Characteristics of Metadata

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Metadata use in the Statistical Value Chain

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  1. Metadata use in the Statistical Value Chain UNECE-Eurostat-OECD Meeting on Management of Statistical Information Systems MSIS 2008 Luxembourg, 7-9 April 2008 Georges Pongas Adam Wroński

  2. Content • Introduction • Operational Characteristics of Metadata • Technical Characteristics of the Metadata • Metadata types needed in the various steps of the SVC (statistical value chain) • Conclusion Metadata use in the Statistical Value Chain

  3. Seven SVC steps • Expression of the need • Data collection design • Specification and development of the tools needed for the data collection • Data collection • Data editing and imputation • Data processing • Data dissemination Metadata use in the Statistical Value Chain

  4. Basics • Leave out the statistical notions from the technical (implementation oriented) characteristics of the metadata. • Design metadata technical characteristics so the same metadata structures can cover both statistical and non-statistical requirements Metadata use in the Statistical Value Chain

  5. Operational Characteristics of Metadata • Static nature • Long production process • Located in various places (resources) • Critical link with statistical data • depends on statistical data changes • Strong coupling of structural metadata with the statistical data • Large number of metadata entities needed in SVC Metadata use in the Statistical Value Chain

  6. Technical Characteristics of Metadata • Terminology often complex • Technical characteristics and statistical notions frequently mixed Metadata use in the Statistical Value Chain

  7. Statistical Notions and Metadata • Examples • Classification, keyword list and set of information related to the SDDS standard • Correspondence table between two classifications & table containing the links (access rights) between the user names and the statistical datasets of a database • The only difference is the context, i.e., the user interface • Thus develop separately: • a common set of functionalities and • the interface layer for an application Metadata use in the Statistical Value Chain

  8. Metadata Technical Structure Categories Three categories proposed: • Simple Metadata Entities (SME) • Binary Relationships (BR) • Clustered Metadata Entities (CME) Metadata use in the Statistical Value Chain

  9. simple key variable number of attributes appropriate for vertical type storage Example 1 Example 2 Entity NACE user name Entity element 2122 gpongas Attribute name English label phone no Attribute value “Mining” 430139 Simple Metadata Entities (SME) Metadata use in the Statistical Value Chain

  10. Examples of SMEs • SDDS documents • Dublin Core • Classifications • Keywords • Administrative entities • Programs • Publications Metadata use in the Statistical Value Chain

  11. Binary Relationships (BR) Two types: • Between two different entities • correspondence tables, access rights definitions • Inside the same entity • thesauri, classification hierarchies, links between regulations, statistical documents Example Relationship id UN thesaurus First entity id EUROPE First entity role Parent Second entity id FR Second entity role Child Reason of link Broader term Metadata use in the Statistical Value Chain

  12. Clustered Metadata Entities (CME) • Complex entities characterised by variable keys’ cardinality and references to other entities of type CME, SME and BR • Description techniques • XML schema is appropriate Metadata use in the Statistical Value Chain

  13. Examples • SDMX, Gesmes definitions • Dataset definitions • Annotations to dataset cells • Confidentiality definitions linked to datasets Metadata use in the Statistical Value Chain

  14. Metadata in the various steps of the SVC Metadata use in the Statistical Value Chain

  15. Collection Metadata • Mostly of type BR and SME • Among others they contain: • source agencies • data files descriptions • codelists • validation rules linked to initial data checks Metadata use in the Statistical Value Chain

  16. Editing, Imputation and Processing Metadata • More complex than the collection metadata (more CME entities needed) • Among others they contain: • Dataset definitions • Formulas, programs, scripts • Conditional and ordinary annotations • Dissemination feeding information Metadata use in the Statistical Value Chain

  17. Dissemination Metadata • The most complex metadata types are located here. • They contain almost all the previously described metadata plus their own • Reasons for this complexity • Dissemination contains all the statistical domains • It must cover all user types • It has tight delivery deadlines • It must offer navigation presentation and extraction facilities of great friendliness Metadata use in the Statistical Value Chain

  18. Among others dissemination metadata contain • Sitemap description • Release calendars • Dataset links to publication tables • Questionnaires definitions linked to datasets • Units of measurement • Ready made queries Metadata use in the Statistical Value Chain

  19. Conclusion Separation of • statistical notions (context) and structure (functionality) of metadata gives • minimisation of structural metadata types consequently it makes easier to • build and implement a complex statistical (metadata and data) system Metadata use in the Statistical Value Chain

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