poster

Spatial embedding: a generic machine learning model for spatial query optimization

Authors:

Alberto Belussi,

Sara Migliorini,

Ahmed EldawyAuthors Info & Claims

SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems

Article No.: 26, Pages 1 - 4

https://doi.org/10.1145/3557915.3560960

Published: 22 November 2022 Publication History

Abstract

Machine learning and deep learning techniques are increasingly applied to produce efficient query optimizers, in particular in regards to big data systems. The optimization of spatial operations is even more challenging due to the inherent complexity of such kind of operations, like spatial join, range queries, and the peculiarities of spatial data. Even though a few ML-based spatial query optimizers have been proposed in literature, their design limits their use, since each one is tailored for a specific collection of datasets, a specific operation, or specific a hardware. Changes to any of these will require building and training a completely new model which entails collecting a new very large training data to obtain a good model

This paper proposes a new approach for ML-based query optimization which exploits the use of the novel notion of spatial embedding for overcoming these limitations. In particular, a preliminary model is defined which captures the relevant features of spatial datasets, independently from the operation to be optimized and in an unsupervised manner. Given that, a specialized model for the optimization of each spatial operation can be trained by using spatial embeddings as input, so the cost of building the first model can be amortized and a smaller training set is required for the specialized ones.

References

[1]

W. Aref and H. Samet. 1994. A Cost Model for Query Optimization Using R-Trees. In ACMGIS. ACM, 60--67.

[2]

Alberto Belussi and Christos Faloutsos. 1998. Self-spacial Join Selectivity Estimation Using Fractal Concepts. ACM TIS 16, 2 (1998), 161--201.

[3]

Alberto Belussi, Sara Migliorini, and Ahmed Eldawy. 2018. Detecting Skewness of Big Spatial Data in SpatialHadoop (SIGSPATIAL '18). 432--435.

[4]

A Belussi, S Migliorini, and A Eldawy. 2020. A Cost Model for Spatial Join Operations in SpatialHadoop. GeoInformatica 24, 4 (2020), 1021--1059.

Digital Library

[5]

Ahmed Eldawy et al. 2021. Beast: Scalable Exploratory Analytics on Spatio-temporal Data. In CIKM. ACM.

[6]

A. Eldawy and M. F. Mokbel. 2015. SpatialHadoop: A MapReduce framework for spatial data. In ICDE. 1352--1363.

[7]

Puloma Katiyar, Tin Vu, Sara Migliorini, Alberto Belussi, and Ahmed Eldawy. 2020. SpiderWeb: A Spatial Data Generator on the Web. In SIGSPATIAL. ACM.

[8]

Andreas Kipf, Thomas Kipf, Bernhard Radke, Viktor Leis, Peter A. Boncz, and Alfons Kemper. 2019. Learned Cardinalities: Estimating Correlated Joins with Deep Learning. In CIDR.

[9]

Sanjay Krishnan, Zongheng Yang, Ken Goldberg, Joseph M. Hellerstein, and Ion Stoica. 2018. Learning to Optimize Join Queries With Deep Reinforcement Learning. CoRR abs/1808.03196 (2018).

[10]

Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther. 2016. Autoencoding beyond Pixels Using a Learned Similarity Metric (ICML'16). JMLR.org, 1558--1566.

Digital Library

[11]

Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, and Tim Kraska. 2021. Bao: Learning to Steer Query Optimizers. SIGMOD.

[12]

Ryan Marcus and Olga Papaemmanouil. 2018. Deep Reinforcement Learning for Join Order Enumeration. In aiDM@SIGMOD. ACM, 3:1--3:4.

[13]

Ryan C. Marcus et al. 2019. Neo: A Learned Query Optimizer. PVLDB 12, 11 (2019), 1705--1718.

Digital Library

[14]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. CoRR abs/1301.3781 (2013).

[15]

Samriddhi Singla and Ahmed Eldawy. 2020. Flexible Computation of Multidimensional Histograms. In SpatialGems (Seattle, Washington, USA). ACM.

[16]

Tin Vu, Alberto Belussi, Sara Migliorini, and Ahmed Eldawy. 2020. Using Deep Learning for Big Spatial Data Partitioning. ACM Trans. Spatial Algorithms Syst. 7, 1 (2020), 3:1--3:37.

[17]

Tin Vu, Alberto Belussi, Sara Migliorini, and Ahmed Eldawy. 2021. A Learned Query Optimizer for Spatial Join (SIGSPATIAL '21). 458--467.

[18]

T. Vu, S. Migliorini, A. Eldawy, and A. Belussi. 2019. Spatial Data Generators. In 1st ACM SIGSPATIAL Int. Workshop on Spatial Gems (SpatialGems 2019). 7.

[19]

Zongheng Yang et al. 2020. NeuroCard: One Cardinality Estimator for All Tables. PVLDB 14, 1 (2020), 61--73.

[20]

J. Yu, J. Wu, and M. Sarwat. 2015. GeoSpark: a cluster computing framework for processing large-scale spatial data. In SIGSPATIAL. 70:1--70:4.

Cited By

Belussi AGarofolo DMigliorini S(2024)Augmentation Techniques for Balancing Spatial Datasets in Machine and Deep Learning ApplicationsProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691230(91-101)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691230
Belussi AMigliorini SEldawy A(2024)A Generic Machine Learning Model for Spatial Query Optimization based on Spatial EmbeddingsACM Transactions on Spatial Algorithms and Systems10.1145/365763310:4(1-33)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3657633
Qi JLi ZTanin EDamiani MRenz MEldawy AKröger PNascimento M(2023)MaaSDB: Spatial Databases in the Era of Large Language Models (Vision Paper)Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625597(1-4)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3589132.3625597

Index Terms

Spatial embedding: a generic machine learning model for spatial query optimization
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Information systems
  1. Data management systems
    1. Database management system engines

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Published In

cover image ACM Conferences

SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems

November 2022

806 pages

ISBN:9781450395298

DOI:10.1145/3557915

General Chairs:
Matthias Renz
Kiel University, Germany
,
Mohamed Sarwat
Wherobots Inc. / Arizona State University

Copyright © 2022 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 22 November 2022

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SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems

November 1 - 4, 2022

Washington, Seattle

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Belussi AGarofolo DMigliorini S(2024)Augmentation Techniques for Balancing Spatial Datasets in Machine and Deep Learning ApplicationsProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691230(91-101)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691230
Belussi AMigliorini SEldawy A(2024)A Generic Machine Learning Model for Spatial Query Optimization based on Spatial EmbeddingsACM Transactions on Spatial Algorithms and Systems10.1145/365763310:4(1-33)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3657633
Qi JLi ZTanin EDamiani MRenz MEldawy AKröger PNascimento M(2023)MaaSDB: Spatial Databases in the Era of Large Language Models (Vision Paper)Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625597(1-4)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3589132.3625597

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