Learned Index for Spatial Queries

Haixin Wang, Xiaoyi Fu, Jianliang Xu, Hua Lu

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

Abstract

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.
OriginalsprogEngelsk
Titel20th IEEE International Conference on Mobile Data Management (MDM)
Antal sider6
ForlagIEEE
Publikationsdato2019
Sider569-574
ISBN (Trykt)978-1-7281-3364-5
ISBN (Elektronisk)978-1-7281-3363-8
DOI
StatusUdgivet - 2019
Udgivet eksterntJa
Begivenhed20th IEEE International Conference on Mobile Data Management (MDM) - Hong Kong, Hong Kong
Varighed: 10 jun. 201913 jun. 2019

Konference

Konference20th IEEE International Conference on Mobile Data Management (MDM)
LandHong Kong
ByHong Kong
Periode10/06/201913/06/2019

Citer dette

Wang, H., Fu, X., Xu, J., & Lu, H. (2019). Learned Index for Spatial Queries. I 20th IEEE International Conference on Mobile Data Management (MDM) (s. 569-574). IEEE. https://doi.org/10.1109/MDM.2019.00121