Chapter 7: dataspaces

  • Authors:

  • Cornelia Hedeler;Khalid Belhajjame;Norman W. Paton;Alessandro Campi;Alvaro A. A. Fernandes;Suzanne M. Embury

  • Affiliations:

  • School of Computer Science, University of Manchester, UK;School of Computer Science, University of Manchester, UK;School of Computer Science, University of Manchester, UK;Dipartimento di Elettronica e Informatzione, Politecnico di Milano, Italy;School of Computer Science, University of Manchester, UK;School of Computer Science, University of Manchester, UK

  • Venue:
  • Search Computing
  • Year:
  • 2010

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Abstract

The vision of dataspaces is to provide various of the benefits of classical data integration, but with reduced up-front costs, combined with opportunities for incremental refinement, enabling a “pay as you go” approach. As such, dataspaces join a long stream of research activities that aim to build tools that simplify integrated access to distributed data. To address dataspace challenges, many different techniques may need to be considered: data integration from multiple sources, machine learning approaches to resolving schema heterogeneity, integration of structured and unstructured data, management of uncertainty, and query processing and optimization. Results that seek to realize the different visions exhibit considerable variety in their contexts, priorities and techniques. This chapter presents a classification of the key concepts in the area, encouraging the use of consistent terminology, and enabling a systematic comparison of proposals. This chapter also seeks to identify common and complementary ideas in the dataspace and search computing literatures, in so doing identifying opportunities for both areas and open issues for further research.