Parallel bulk-loading of spatial data

  • Authors:
  • Apostolos Papadopoulos;Yannis Manolopoulos

  • Affiliations:
  • Department of Informatics, Aristotle University of Thessaloniki, 54006 Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, 54006 Thessaloniki, Greece

  • Venue:
  • Parallel Computing - Special issue: High performance computing with geographical data
  • Year:
  • 2003

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Abstract

Spatial database systems have been introduced in order to support non-traditional data types and more complex queries. Although bulk-loading techniques for access methods have been studied in the spatial database literature, parallel bulk-loading has not been addressed in a parallel spatial database context. Therefore, we study the problem of parallel bulk-loading, assuming that an R-tree like access method need to be constructed, from a spatial relation that is distributed to a number of processors. Analytical cost models and experimental evaluation based on real-life and synthetic datasets demonstrate that the index construction time can be reduced considerably by exploiting parallelism. I/O costs, CPU time and communication costs are taken into consideration in order to investigate the efficiency of the proposed algorithm.