1. Implementing a New Classification Management System at Statistics New Zealand Andrew Hancock, Statistics New Zealand Arofan Gregory, Metadata Technology North America

2. Background and Overview In 2010, SNZ started a ten-year program to modernize their production of statistics Vision included moving away from paper- oriented production of classifications with slow revision cycles to a more dynamic system – Machine-processible formats – Rapid and flexible release capability Centralized management to support the production process – Wanted a collaborative management system, not just a repository

3. Modernization Objectives Replace legacy CARS repository Leverage a concept-oriented model (like GSIM) Reduce proliferation of classification versions Revise process for management, storage, and dissemination of classifications Standardize concepts and categories within SNZ

4. Aria “Aria” is the name chosen for the new classification management system – it means “concept” in Maori Under the development arrangement, Aria will be available for licensing by other statistical agencies – Currently, only SNZ requirements are implemented, but broader support is planned Implemented in Java for cross-platform use

5. Functionality Concept management Classifications Coding Concordances Downloads and Dissemination Search Capabilities “Statistical Standards”

6. Concept Management Concepts are very important in Aria – Associated with categories – Associated with levels – Can be related to other concepts to create concept systems

7. Concepts (2)

8. Classifications

9. Classifications (2)

10. Editing Functions Many typical functions: Split, Merge, Transfer, Restore, Takeover, Replace/Breakdown, etc. Supports “views” (subsets)

11. Coding

12. Concordances Target-view and source-view of concordances Can be auto-generated or created manually

13. Downloads and Dissemination Allows for many types of structured and documentary formats Allows for download of comparison views of classifications in several formats Access control hides classifications which are not available to particular users

14. Search Capabilities Search is implemented using SOLR Has Quick-Search capability Has full search capabilities

15. “Statistical Standards” SNZ has a concept of “standard” methods and ways of measuring certain concepts. It is possible to attach additional information to specific concepts to make this information easily available.

16. Underlying Models GSIM places huge emphasis on Concepts – Categories are the use of Concepts in GSIM – This is a very powerful feature of GSIM, and it allows many new functions to be realized within a metadata system SKOS provides an RDF-based, flexible approach to describing classifications – Very flexible – Supports inferencing based on known relationships Neuchatel provides an important model for many aspects of classifications Sufficient information is held to express classifications in DDI, SDMX, and other standard formats

17. Aspects of Modernization (1) Increased support for process-oriented classification management – A management system, not a repository – Supports workflow status and access control – Enables very flexible, frequent updates to classifications Supports granular re-use – Use of concepts and categories across entire collections of classifications – Allows for views - subsets of existing classifications (not new versions) – Reduces proliferation of classification versions

18. Aspects of Modernization (2) As classifications are versioned, concordances can be automatically generated – The system knows when classifications are split, joined, etc. Architecture is service-oriented – Easy to integrate with existing systems – Could fit into a CSPA architecture Uses the GSIM idea of Concepts to “know” more about places where classifications are used in data – New dissemination and production functionality can be supported

19. Thanks! If you are interested in having a more-detailed online demonstration of the system, please feel free to contact us: arofan.gregory@mtna.us