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Integrated metadata systems History Status Vision Roadmap

Integrated metadata systems History Status Vision Roadmap. Rune.Gloersen@ssb.no. Integrated Metadata Systems. Stove-piped statistical production (systems) with no, or at the best, encapsulated metainformation, represents our remains from the IT stone - age.

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Integrated metadata systems History Status Vision Roadmap

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  1. Integrated metadata systems HistoryStatusVisionRoadmap Rune.Gloersen@ssb.no

  2. Integrated Metadata Systems • Stove-piped statistical production (systems) with no, or at the best, encapsulated metainformation, represents our remains from the IT stone-age. • First steps towards the consciousness of metadata(structures) were taken some 20 years ago: • metadatadriven on-line systems • filedescription- and other archives of structured documentation • The technological evolution has been the driving force towards a vision of a coherent statistical (IT) system • However, the state-of-the-art-technology has also at all times represented one of the most important obstacles to success • in addition to the human- and organisational barriers that we also discuss

  3. Technological barriers • Lack of processor speed and data storage capacity • lack of access possibilities across different IT systems • Lack of database functionality and flexibility • Lack of awareness of metainformation as a whole in the IT industry (handling of technical meta-information at the most), i.e the kind of metainformation that was handled in the first datawarehouse solutions • Lack of (IT) standards, • but anyhow; why didn’t we achieve more when we had all our information systems within one mainframe ?  due to the human and organisational barriers ?

  4. Our current advantages • WWW • Open standards on • connectivity • LAN/WAN communication • database connectivity • standardised exchange of data on • protocol level • syntactic level • Object orientation • Web services !But what about the semantic level ?

  5. A vision for a coherent statistical system • The basic architecture of a coherent statistical system is formed by the structure, content and handling of metainformation • The IT system will never reflect anything else but the level of standardisation and coordination of the statistical production within the organisation • NSI’s must take into account all statistical IT systems currently running, having been developed over the past 20 years, which would need to fit into a new or upgraded system • A coherent statistical system based on integration of what you already have, or convert everything to a new (gigantic) system ?

  6. A vision for a coherent statistical system Design and planning Evaluation Objective Content Population Sample Collection methods Process methods Dissemi- nation Know- ledge Input data Input data Stat. data Knowledge base Operation • Expert knowledge: • Guidelines • Articles • Methods • People Local metadata Establish population & sample Data collect- ion & Edit Estimation Aggregation Presentation Dissemination Local prod.data Local prod.data Global metadata Classifications Standards -Datadoc -Stat.Activities -Stat.doc -Quality decl. -Structured metadata Populations Observation register Dissemination database Datawarehouse Source: Bo Sundgren

  7. Metadata Local metadata Local metadata Local metadata Question- naire repository File descript. Content (Quality) declaration Variable definitions Macro database Classifications Statistical activities

  8. Metadata Local metadata Local metadata Local metadata Question- naire repository File descript. Content (Quality) declaration Census/ Survey Variable definitions Macro database Classifications Statistical activities

  9. Metadata Local metadata Local metadata Local metadata Question- naire repository File descript. Content (Quality) declaration What information is needed to establish consistent links between the components of your (structured) metainformation system ? Variable definitions Macro database Classifications Statistical activities

  10. Metadata Local metadata Local metadata Local metadata Question- naire repository File descript. Content (Quality) declaration Variable definitions Macro database Classifications Statistical activities

  11. Metadata Local metadata Local metadata Local metadata Question- naire repository File descript. Content (Quality) declaration XML Variable definitions Macro database Classifications Statistical activities

  12. Metadata components Local metadata Local metadata Local metadata Content (Quality) declaration Question- naire repository File descript. Variable definitions Statistical activities Classifications Macro database

  13. Metadata components Three layered model Local metadata Local metadata Local metadata Content (Quality) declaration Metamodel Question- naire repository File descript. Linking/Mapping Variable definitions Metadata Statistical activities Classifications Data Macro database

  14. Metadata components Process Local metadata Local metadata Local metadata Collection Content (Quality) declaration Question- naire repository Data Editing File descript. Estimation Linking/Mapping Variable definitions Statistical activities Aggregation Classifications Macro database Dissemination

  15. Metadata components Different domains Local metadata Local metadata Local metadata Domain n Content (Quality) declaration Question- naire repository File descript. Linking/Mapping Domain 2 Variable definitions Statistical activities Classifications Macro database Domain 1

  16. Metadata components Local metadata Local metadata Local metadata End user needs Content (Quality) declaration Question- naire repository File descript. Access Variable definitions Statistical activities Classifications Macro database

  17. Non-structured metainformation • Text • Text-mining • Knowledge systems • Challenge, and upcoming reality:How shall we be able to store, retrieve and maintain the knowledge of the organisation much more independent of their (shifting) staff ?

  18. Metadata in the statistical production • Data input • Data throughput • Data dissemination

  19. Data collection Paper Questionnaires Electronic Questionnaires P Internal Business Systems BS ELQ I Mapping between statistical and in-house data definitions Optical char. recognition, intrepretation verifiying www.ssb.no XML Questionnaire generation OCR NSI CRDS Data Definitions Questions Rules/Checks Questionnaires Central Raw Data Storage Metadata Subject matter systems Links to a (national) repository of Data definitions/Questionnaires Linked to Business Register

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