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Data Management

Data Management. David Nathan & Peter Austin & Robert Munro. This section. Data management Properties of data Relational data model XML Example. something happened. . representations, lists, summaries, analyses. something inscribed. cleaned up, selected, analysed.

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Data Management

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  1. Data Management David Nathan & Peter Austin & Robert Munro

  2. This section • Data management • Properties of data • Relational data model • XML • Example

  3. something happened  representations, lists, summaries, analyses something inscribed cleaned up, selected, analysed you applied knowledge, made decisions archived, presented, published NOT OF INTEREST! recapitulates  representations, eg transcription, annotation recording you applied knowledge, techniques made decisions, applied linguistic knowledge FOCUS OF INTEREST! archived & ... ?? something happened Workflows - description vs documentation Description Documentation

  4. Choosing values/priorities • Standards & compliance • Adeptness with tools • Modelling of phenomena, architecture of data • Dissemination/publishing • Preserving • Ethics, responsibility, protocol • Range, comprehensiveness • Intellectual rigour • Which are priorities? • Which are dispensible?

  5. Data should be: • explicit • consistent • robust • meaningful • conventional • adaptable, convertible, machine readable etc • useful!

  6. “Portability” • Bird and Simons 2003: language documentation data needs to have integrity, flexibility, longevity

  7. “Portability” • complete • explicit • documented • preservable • transferable • accessible • adaptable • not technology-specific • (also appropriate, accurate, useful etc!!)

  8. Data management • the way that data is structured is also information, that may be complex • properly structured data allows: • usage including manipulation, conversion, derivation • preservation • machine readability

  9. Data management systems • a data management system is a system you design for storing data and metadata: • information about content and structures • relationship between units of information • it is not necessarily tied to any particular software, or even a computer

  10. Naive managment using filenames • a (too) simple management system: • information about a recording is captured in the filenames: 1st_int_john_5Aug.wav market_conv_mj.wav …. • what does ‘int’ mean? • what information about the recording is missing?

  11. Data modeling • World/universe • Domain • Relevant • entities • properties • relationships • We also need formal ways to represent these

  12. Data modeling • data modelling is the process of designing your data management system: • what information do you need to record? • what are the units of information? • what are their properties (attributes)? • what are the relationships between the units of information? • how is the information etc likely to change in the future? • how can all this be represented?

  13. Data management • two well-known formats for structured data: • relational database • eXtensible Markup Language (XML) • these are methods, not softwares or hardwares • any system for well-structured data could be OK, but generally: • smaller community of users so less tools and support • ... so errors more likely

  14. Databases • Note that database has 3 senses: • a body of related information • type of software (eg Oracle, Access, Filemaker) • a model for the domain of information (ie. formulation of entities and relationships)

  15. Relational format • Uses tables • Table rows represent entities in a domain • Table columns represent properties/attributes of entities • Each cell represents one atomic unit of data • The order of rows and columns has no significance

  16. TABLE NAME field name Representing a relational design • simplest example

  17. Representing a relational design • less trivial entity TABLE NAME field 1 field 2

  18. CONTINENT name COUNTRY name Representing a relational design • less trivial domain = one to many

  19. AUTHOR ..... SUBJECT name ..... name Non-trivial domains • non-trivial domains have many-to-many relationships

  20. From model to implementation • implementing table relationships CONTINENT COUNTRY name name id id continent_id

  21. Designing a database • Determine the domain, entities and relationships • Experiment with scenarios • Any non-trivial model will evolve as it is thought out and tested • Normalisation is the process of refining models

  22. Practical example • Create a database model for some audio metadata

  23. What does all this achieve? • conceptual/intellectual validity • scalable, searchable, modular • machine readable • in fact, portable: • complete • explicit • documented • preservable • transferable • accessible • adaptable • not technology-specific

  24. Stop here!

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