Bulk Insertions into xBR+ -trees
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URI: http://hdl.handle.net/10835/5280
DOI: https://doi.org/10.1007/978-3-319-66854-3_14
DOI: https://doi.org/10.1007/978-3-319-66854-3_14
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Roumelis, George; Vassilakopoulos, Michael; Corral Liria, Antonio Leopoldo; Manolopoulos, YannisFecha
2017Resumen
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. Bulk insertion is related to bulk loading, which refers to the process of creating a non-existing index from scratch, when the dataset to be indexed is available beforehand. The xBR + -tree is a balanced, disk-resident, Quadtree-based index for point data, which is very efficient for processing spatial queries. In this paper, we present the first algorithm for bulk insertion into xBR+ -trees. This algorithm incorporates extensions of techniques that we have recently developed for bulk loading xBR+ -trees. Moreover, using real and artificial datasets of various cardinalities, we present an experimental comparison of this algorithm vs. inserting items one-by-one for updating xBR+ -trees, regarding performance (I/O and execution time) and the characteristics of the resulting trees. We also presen...