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Statistical databases in theory and practice Part III: Designing statistical databases

Statistical databases in theory and practice Part III: Designing statistical databases. Bo Sundgren 2008-02-11. Conceptual data model and relational data model in normalised form. Concept modelling. Define concepts and relations between them Conceptual models and data models

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Statistical databases in theory and practice Part III: Designing statistical databases

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  1. Statistical databases in theory and practicePart III: Designing statistical databases Bo Sundgren 2008-02-11

  2. Conceptual data model and relational data model in normalised form.

  3. Concept modelling • Define concepts and relations between them • Conceptual models and data models • Visualise models graphically

  4. Rent-A-Video: first object graph

  5. Rent-A-Video: elaborated object graph

  6. Rent-A-Video: further aspects

  7. one-to-one, symbolised by “arrow-to-arrow” one-to-many, symbolised by “arrow-to-fork” many-to-one, symbolised by “fork-to-arrow” many-to-many, symbolised by “fork-to-fork” Relations between two object types Note: The relation is usually not a flow relation! (But you should tell what kind of relation it is.)

  8. Object graphs: another example

  9. Concept modelling: Exercises • B2B • The customers of companies are companies • Companies have employees (persons) • B2C • The customers of companies are consumers (persons) • Companies have employees (persons) • B2B+B2C • The customers of companies are companies or consumers (persons) • Companies have employees (persons) • Hint: There are two basic object types, COMPANY and PERSON in all three examples

  10. Different roles of concept modelling • Clarifying a small number of related concepts • Information model for an application • defining meaning • basis for data design • Corporate information model • for more efficient communication between people • basis for system integration

  11. Concept model ---> Data model

  12. Concept model ---> Star/cube model

  13. Star model for Data Warehouse

  14. Multidimensional model (cube model)

  15. Modelling the contents and structure of official statisticsOr: How to design ”correct” and globally consistent SDMX Data Structure DefinitionsOr: Navigating in a space of statistical surveys of societyOr: Reality as a statistical construction Bo Sundgren, Statistics Sweden ICES-III, Montreal, June 18-21, 2007

  16. What can a statistical agency do, in order to help a user - find potentially relevant statistical data? - judge the relevance of data retrieved? • Provide overviews of available data • Provide search tools • Provide informative metadata

  17. Conceptual navigation: contents exploration and searching for statistics • A conceptual model of societyas reflected by official statistics

  18. Statistics Canada: Agents, Events, Things

  19. Contents By Example (based on a simple generic model) Complex objects Utilities Actors

  20. Everything ”clickable” OBJECT VARIABLE Righthand click Lefthand click • Select: • object • variable • Retrieve metadata: • definition • value set, classification • questionnaire • quality declaration • survey documentation

  21. UNESCO model version 1 (to be revised)

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