Abstract
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 present experimental results regarding the query-processing efficiency of xBR\(^{+}\)-trees built by bulk insertions vs. xBR\(^{+}\)-trees built by inserting items one-by-one.
M. Vassilakopoulos, A. Corral and Y. Manolopoulos—Work funded by the MINECO research project [TIN2013-41576-R].
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Notes
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Retrieved from http://spatialhadoop.cs.umn.edu/datasets.html.
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Roumelis, G., Vassilakopoulos, M., Corral, A., Manolopoulos, Y. (2017). Bulk Insertions into xBR\(^{+}\)-trees. In: Ouhammou, Y., Ivanovic, M., Abelló, A., Bellatreche, L. (eds) Model and Data Engineering. MEDI 2017. Lecture Notes in Computer Science(), vol 10563. Springer, Cham. https://doi.org/10.1007/978-3-319-66854-3_14
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