Learned Index for Spatial Queries

Haixin Wang, Xiaoyi Fu, Jianliang Xu, Hua Lu

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


With the pervasiveness of location-based services (LBS), spatial data processing has received considerable attention in the research of database system management. Among various spatial query techniques, index structures play a key role in data access and query processing. However, existing spatial index structures (e.g., R-tree) mainly focus on partitioning data space or data objects. In this paper, we explore the potential to construct the spatial index structure by learning the distribution of the data. We design a new data-driven spatial index structure, namely learned Z-order Model (ZM) index, which combines the Z-order space filling curve and the staged learning model. Experimental results on both real and synthetic datasets show that our learned index significantly reduces the memory cost and performs more efficiently than R-tree in most scenarios.
Original languageEnglish
Title of host publication20th IEEE International Conference on Mobile Data Management (MDM)
Number of pages6
Publication date2019
ISBN (Print)978-1-7281-3364-5
ISBN (Electronic)978-1-7281-3363-8
Publication statusPublished - 2019
Externally publishedYes
Event20th IEEE International Conference on Mobile Data Management (MDM) - Hong Kong, Hong Kong
Duration: 10 Jun 201913 Jun 2019


Conference20th IEEE International Conference on Mobile Data Management (MDM)
Country/TerritoryHong Kong
CityHong Kong


  • Learned index
  • Z order curve
  • Learned ZM index

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