An efficient algorithm for bulk-loading xBR+-trees
A major part of the interface to a database is made up of the queries that can be addressed to this database and answered (processed) in an efficient way, contributing to the quality of the developed software. Efficiently processed spatial queries constitute a fundamental part of the interface to spatial databases due to the wide area of applications that may address such queries, like geographical information systems (GIS), location-based services, computer visualization, automated mapping, facilities management, etc. Another important capability of the interface to a spatial database is to offer the creation of efficient index structures to speed up spatial query processing. 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, when the dataset to be indexed is available beforehand, instead of creating the index gradually (and more slowly), when the dataset elements are inserted 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 main memory. The resulting tree is not only built fast, but exhibits high performance in processing a broad range of spatial queries, where one or two datasets are involved. To justify these characteristics, using real and artificial datasets of various cardinalities, first, we present an experimental comparison of this algorithm vs. a previous version of the same algorithm and STR, a popular algorithm of bulk-loading R-trees, regarding tree creation time and the characteristics of the trees created, and second, we experimentally compare the query efficiency of bulk-loaded xBR+-trees vs. bulk-loaded R-trees, regarding I/O and execution time. Thus, this paper contributes to the implementation of spatial database interfaces and the efficient storage organization for big spatial data management. © 2017 Elsevier B.V.