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Spatial Queries Based on Learned Index

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Spatial Data and Intelligence (SpatialDI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12567))

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Abstract

With the popularity of location-based services, the scale of spatial data is increasing. Spatial indexes play an important role in spatial databases, and their performance determines the efficiency of data access and query processing. Most of the traditional spatial indexes divide data space or data objects without considering the distribution characteristics of data. In this paper, we design a spatial index structure, named learned Hilbert Model (HM) index. We combine the Hilbert space-filling curve and the two-stage model to build the spatial index. We propose algorithms for point query and range query according to data distribution rules. Experimental results show that the learned HM index can reduce the storage cost by 99% compared with R-tree and Grid Index. Point query efficiency is 40% higher than R-tree and 51% higher than Grid Index. The efficiency of range query is up to 50% higher than R-tree and 57% higher than Grid Index.

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Acknowledgement

This work is supported by NSFC under grants 61972198, Natural Science Foundation of Jiangsu Province of China under grants BK20191273 and the Foundation of Graduate Innovation Center in Nanjing University of Areonautics and Astronautics under grants KFJJ20191604.

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Authors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Ning Wang & Jianqiu Xu

Authors
  1. Ning Wang
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  2. Jianqiu Xu
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Corresponding author

Correspondence to Jianqiu Xu .

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Editors and Affiliations

  1. Renmin University of China, Beijing, China

    Xiaofeng Meng

  2. Microsoft Research Asia, Beijing, China

    Xing Xie

  3. Shenzhen University, Shenzhen, China

    Yang Yue

  4. Chinese Academy of Sciences, Beijing, China

    Zhiming Ding

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Wang, N., Xu, J. (2021). Spatial Queries Based on Learned Index. In: Meng, X., Xie, X., Yue, Y., Ding, Z. (eds) Spatial Data and Intelligence. SpatialDI 2020. Lecture Notes in Computer Science(), vol 12567. Springer, Cham. https://doi.org/10.1007/978-3-030-69873-7_18

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  • DOI: https://doi.org/10.1007/978-3-030-69873-7_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69872-0

  • Online ISBN: 978-3-030-69873-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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