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Dataspaces: Fundamentals, Principles, and Techniques


A dataspace is an emerging approach to data management which recognises that in large-scale integration scenarios, involving thousands of data sources, it is difficult and expensive to obtain an upfront unifying schema across all sources. Data is integrated on an “as-needed” basis with the labour-intensive aspects of data integration postponed until they are required. Dataspaces reduce the initial effort required to set up data integration by relying on automatic matching and mapping generation techniques. This results in a loosely integrated set of data sources. When tighter semantic integration is required, it can be achieved in an incremental “pay-as-you-go” fashion by detailed mappings between the required data sources. This chapter introduces dataspaces and the fundamentals of “best-effort” data management.


  • Dataspaces
  • Best-effort information
  • Approximation
  • Incremental data management

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Correspondence to Edward Curry .

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Curry, E. (2020). Dataspaces: Fundamentals, Principles, and Techniques. In: Real-time Linked Dataspaces. Springer, Cham.

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