Bulk-Loading xBR\(^+\)-trees

  • George Roumelis
  • Michael VassilakopoulosEmail author
  • Antonio Corral
  • Yannis Manolopoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9893)


Spatial indexes are important in spatial databases for efficient execution of queries involving spatial constraints. The xBR\(^+\)-tree is a balanced disk-resident quadtree-based index structure for point data, which is very efficient for processing such queries. Bulk-loading refers to the process of creating an index from scratch as a whole, when the dataset to be indexed is available beforehand, instead of creating (loading) the index gradually, when the dataset items are available one-by-one. In this paper, we present an algorithm for bulk-loading xBR\(^+\)-trees for big datasets residing on disk, using a limited amount of RAM. Moreover, using real and artificial datasets of various cardinalities, we present an experimental comparison of this algorithm vs. the algorithm loading items one-by-one, regarding performance (I/O and execution time) and the characteristics of the xBR\(^+\)-trees created. We also present experimental results regarding the efficiency of bulk-loaded xBR\(^+\)-trees vs. xBR\(^+\)-trees where items are loaded one-by-one for query processing.


Spatial indexes Bulk-loading xBR\(^+\)-trees Query processing 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • George Roumelis
    • 1
  • Michael Vassilakopoulos
    • 2
    Email author
  • Antonio Corral
    • 3
  • Yannis Manolopoulos
    • 1
  1. 1.Department of InformaticsAristotle University of ThessalonikiThessalonikiGreece
  2. 2.Department of Electrical and Computer EngineeringUniversity of ThessalyVolosGreece
  3. 3.Department of InformaticsUniversity of AlmeriaAlmeriaSpain

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