Abstract
Efficient query processing in spatial databases is of vital importance for numerous modern applications. In most cases, such processing is accomplished by taking advantage of spatial indexes. The xBR\(^+\)-tree is an index for point data which has been shown to outperform indexes belonging to the R-tree family. On the other hand, Solid-State Drives (SSDs) are secondary storage devices that exhibit higher (especially read) performance than Hard Disk Drives and nowadays are being used in database systems. Regarding query processing, the higher performance of SSDs is maximized when large sequences of queries (batch queries) are executed by exploiting the massive I/O advantages of SSDs. In this paper, we present algorithms for processing common spatial (point-location, window and distance-range) batch queries using xBR\(^+\)-trees in SSDs. Moreover, utilizing small and large datasets, we experimentally study the performance of these new algorithms against processing of batch queries by repeatedly applying existing algorithms for these queries. Our experiments show that, even when the existing algorithms take advantage of LRU buffering that minimizes disk accesses, the new algorithms prevail performance-wise.
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Acknowledgments
Work of Antonio Corral, Michael Vassilakopoulos and Yannis Manolopoulos funded by the MINECO research project [TIN2017-83964-R].
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Roumelis, G., Vassilakopoulos, M., Corral, A., Fevgas, A., Manolopoulos, Y. (2018). Spatial Batch-Queries Processing Using xBR\(^+\)-trees in Solid-State Drives. In: Abdelwahed, E., Bellatreche, L., Golfarelli, M., Méry, D., Ordonez, C. (eds) Model and Data Engineering. MEDI 2018. Lecture Notes in Computer Science(), vol 11163. Springer, Cham. https://doi.org/10.1007/978-3-030-00856-7_20
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