Learned Index for Spatial Queries

Wang, Haixin; Fu, Xiaoyi; Xu, Jianliang; Lu, Hua

doi:10.1109/mdm.2019.00121

Cited by 57 publications

(37 citation statements)

References 12 publications

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“…We decided not to evaluate against these because Flood already showed consistent superiority over them [30]. We also do not evaluate against other learned multi-dimensional indexes because they are either optimized for disk [25,46] or optimize only based on the data distribution, not the query workload [9,44] (see §7).…”

Section: Implementation and Setupmentioning

confidence: 99%

“…Past work has also aimed to improve traditional indexing techniques by learning the data distribution. The ZM-index [44] combines the standard Z-order space-filling curve [29] with the RMI from [23] by mapping multi-dimensional values into a single-dimensional space, which is then learnable using models. The ML-index [9] combines the ideas of iDistance [18] and the RMI to support range and KNN queries.…”

Section: Related Workmentioning

confidence: 99%

“…To address the shortcomings of traditional indexes, recent work has proposed the idea of learned multi-dimensional indexes [9,25,30,44,46]. In particular, Flood [30] is a in-memory multi-dimensional index that automatically optimizes its structure to achieve high performance on a particular dataset and workload.…”

Section: Introductionmentioning

confidence: 99%

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Tsunami

et al. 2020

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Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse. Techniques to accelerate the execution of filter expressions include clustered indexes, specialized sort orders (e.g., Z-order), multi-dimensional indexes, and, for high selectivity queries, secondary indexes. However, these schemes are hard to tune and their performance is inconsistent. Recent work on learned multi-dimensional indexes has introduced the idea of automatically optimizing an index for a particular dataset and workload. However, the performance of that work suffers in the presence of correlated data and skewed query workloads, both of which are common in real applications. In this paper, we introduce Tsunami, which addresses these limitations to achieve up to 6X faster query performance and up to 8X smaller index size than existing learned multi-dimensional indexes, in addition to up to 11X faster query performance and 170X smaller index size than optimally-tuned traditional indexes.

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Section: Implementation and Setupmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Tsunami

et al. 2020

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“…LISA [24] is a disk-based learned multi-dimensional index. In [41], the Z-order space filling curve has been incorporated with the staged learning model to build a multi-dimensional index. Other recent works are: [3,5,11,32,33].…”

Section: Part 2: Learned Multi-dimensional Indexesmentioning

confidence: 99%

A Tutorial on Learned Multi-dimensional Indexes

Al‐Mamun

Aref

2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

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Recently, Machine Learning (ML, for short) has been successfully applied to database indexing. Initial experimentation on Learned Indexes has demonstrated better search performance and lower space requirements than their traditional database counterparts. Numerous attempts have been explored to extend learned indexes to the multi-dimensional space. This makes learned indexes potentially suitable for spatial databases. The goal of this tutorial is to provide up-to-date coverage of learned indexes both in the single and multi-dimensional spaces. The tutorial covers over 25 learned indexes. The tutorial navigates through the space of learned indexes through a taxonomy that helps classify the covered learned indexes both in the single and multi-dimensional spaces. CCS CONCEPTS • Database Systems → Indexing; • Machine Learning → ML for Systems.

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“…However, to the best of our knowledge, both of these learned models can be only applied in single dimensional data. As a result, the current spatial learned indexes either transform multi-dimensional data into one-dimensional data before introducing the learned model as a foundation [7], [8] or apply a learned model on every single dimension [6]. For this reason, the question then arises, "Is there a learned model that can be directly applied to spatial (multi-dimensional) data and achieve better performance?…”

Section: Introductionmentioning

confidence: 99%

Spatial Interpolation-based Learned Index for Range and kNN Queries

Zhang¹,

Ray²,

Lu³

et al. 2021

Preprint

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A corpus of recent work has revealed that the learned index can improve query performance while reducing the storage overhead. It potentially offers an opportunity to address the spatial query processing challenges caused by the surge in location-based services. Although several learned indexes have been proposed to process spatial data, the main idea behind these approaches is to utilize the existing one-dimensional learned models, which requires either converting the spatial data into onedimensional data or applying the learned model on individual dimensions separately. As a result, these approaches cannot fully utilize or take advantage of the information regarding the spatial distribution of the original spatial data. To this end, in this paper, we exploit it by using the spatial (multi-dimensional) interpolation function as the learned model, which can be directly employed on the spatial data. Specifically, we design an efficient SPatial inteRpolation functIon based Grid index (SPRIG) to process the range and kNN queries. Detailed experiments are conducted on real-world datasets, and the results indicate that our proposed learned index can significantly improve the performance in comparison with the traditional spatial indexes and a state-ofthe-art multi-dimensional learned index.

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Learned Index for Spatial Queries

Cited by 57 publications

References 12 publications

Tsunami

Tsunami

A Tutorial on Learned Multi-dimensional Indexes

Spatial Interpolation-based Learned Index for Range and kNN Queries

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