DSToolkit: an architecture for flexible dataspace management

  • Authors:

  • Cornelia Hedeler;Khalid Belhajjame;Lu Mao;Chenjuan Guo;Ian Arundale;Bernadette Farias Lóscio;Norman W. Paton;Alvaro A. A. Fernandes;Suzanne M. Embury

  • Affiliations:

  • School of Computer Science, The University of Manchester, Manchester, UK;School of Computer Science, The University of Manchester, Manchester, UK;School of Computer Science, The University of Manchester, Manchester, UK;School of Computer Science, The University of Manchester, Manchester, UK;School of Computer Science, The University of Manchester, Manchester, UK;Centro de Informatica Cidade Universitria, Universidade Federal de Pernambuco, Recife, PE, Brasil;School of Computer Science, The University of Manchester, Manchester, UK;School of Computer Science, The University of Manchester, Manchester, UK;School of Computer Science, The University of Manchester, Manchester, UK

  • Venue:
  • Transactions on Large-Scale Data- and Knowledge-Centered Systems V
  • Year:
  • 2012

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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. Combining this with opportunities for incremental refinement enables a ‘pay-as-you-go' approach to data integration, resulting in simplified integrated access to distributed data. It has been speculated that model management could provide the basis for Dataspace Management, however, this has not been investigated until now. Here, we present DSToolkit, the first dataspace management system that is based on model management, and therefore, benefits from the flexibility provided by the approach for the management of schemas represented in heterogeneous models, supports the complete dataspace lifecycle, which includes automatic initialisation, maintenance and improvement of a dataspace, and allows the user to provide feedback by annotating result tuples returned as a result of queries the user has posed. The user feedback gathered is utilised for improvement by annotating, selecting and refining mappings. Without the need for additional feedback on a new data source, these techniques can also be applied to determine its perceived quality with respect to already gathered feedback and to identify the best mappings over all sources including the new one.