Bulk-loading and bulk-insertion algorithms for \(\hbox {xBR}^{+}\hbox {-trees}\) in Solid State Drives

Spatial indexes are important in spatial databases for efficient execution of queries involving spatial constraints. The \(\hbox {xBR}^{+}\hbox {-tree}\) is a balanced, disk-resident, Quadtree-based index for point data, which is very efficient for processing spatial 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. Bulk insertion refers to the process of updating an existing index by inserting a large batch of new data, treating the items of this batch as a whole and not by inserting these items one-by-one. In this paper, we modify previous bulk-loading and bulk-insertion algorithms for \(\hbox {xBR}^{+}\hbox {-trees}\) to achieve higher performance by taking advantage of the special features of Solid State Drives (SSDs). SSDs have attracted database developers, mainly due to their higher read performance (thanks to their internal parallelism) than Hard Disk Drives. Using real and artificial datasets of various cardinalities, we experimentally compare the modified algorithms against their predecessors and show that the modified algorithms are clear winners regarding performance.