Advertisement

Dataspaces: Fundamentals, Principles, and Techniques

  • Edward CurryEmail author
Open Access
Chapter

Abstract

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.

Keywords

Dataspaces Best-effort information Approximation Incremental data management 

Copyright information

© The Author(s) 2020

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.National University of Ireland GalwayGalwayIreland

Personalised recommendations