Abstract
Based on the problem that existing learned indexes are difficult to adjust dynamically with data changes, a learned index Structure for dynamical uneven spatial data (LIDUSA) is presented in our paper. To handle with the problem of poor KNN query performance on sparse regions, LIDUSA could dynamically adjust data layout by merging and splitting corresponding grid cells, and relearn mapping function of this region to make data points stored in adjacent sparse grid cell also stored in neighboring disk pages. It combines the advantage of tree-shaped indexes, which could be adjusted dynamically, and that of learned indexes. In this paper, extensive experiments are conducted on real-world dataset and synthetic datasets. From experiment results, it could be seen that LIDUSA is twice as fast as other existing indexes in the scenario of KNN query, which will greatly extend the applicable scope of learned indexes.
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This work is partially supported by Science and Technology Planning Project of Fujian Province under Grant No. 2020H0023.
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Zhang, Z., Wang, Y., Zhu, S. (2022). LIDUSA – A Learned Index Structure for Dynamical Uneven Spatial Data. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13157. Springer, Cham. https://doi.org/10.1007/978-3-030-95391-1_46
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DOI: https://doi.org/10.1007/978-3-030-95391-1_46
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