@inproceedings{4610496bba3d4421aeb4db583955edd3,
title = "Learned index for spatial queries",
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.",
keywords = "Learned index, Learned ZM index, Z Order Curve",
author = "Haixin Wang and Xiaoyi Fu and Jianliang Xu and Hua Lu",
year = "2019",
month = jun,
doi = "10.1109/MDM.2019.00121",
language = "English",
isbn = "978-1-7281-3364-5",
series = "Proceedings - IEEE International Conference on Mobile Data Management",
publisher = "IEEE",
pages = "569--574",
booktitle = "Proceedings - 2019 20th International Conference on Mobile Data Management, MDM 2019",
address = "United States",
note = "20th International Conference on Mobile Data Management, MDM 2019 ; Conference date: 10-06-2019 Through 13-06-2019",
}