research-article

SLAM: Efficient Sweep Line Algorithms for Kernel Density Visualization

Authors:

Byron Choi, and

Jianliang XuAuthors Info & Claims

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

June 2022

Pages 2120 - 2134

https://doi.org/10.1145/3514221.3517823

Published: 11 June 2022 Publication History

Abstract

Kernel Density Visualization (KDV) has been extensively used in a wide range of applications, including traffic accident hotspot detection, crime hotspot detection, disease outbreak detection, and ecological modeling. However, KDV is a computationally expensive operation, which is not scalable to large datasets (e.g., million-scale data points) and high resolution sizes (e.g., 1920 x 1080). To significantly improve the efficiency for generating KDV, we develop two efficient Sweep Line AlgorithMs (SLAM), which can theoretically reduce the time complexity for generating KDV. By incorporating the resolution-aware optimization (RAO) into SLAM, we can further achieve the lowest time complexity for generating KDV. Our extensive experiments on four large-scale real datasets (up to 4.33 million data points) show that all our methods can achieve one to two-order-of-magnitude speedup in many test cases and efficiently support KDV with exploratory operations (e.g., zooming and panning) compared with the state-of-the-art solutions.

Supplementary Material

MP4 File (SLAM_presentation_v5.mp4)

Complexity-Optimized Algorithms for generating exact kernel density visualization (KDV), without increasing the space complexity.

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Cited By

Chan TZang RZhu BU LWu DXu J(2024)LION: Fast and High-Resolution Network Kernel Density VisualizationProceedings of the VLDB Endowment10.14778/3648160.364816817:6(1255-1268)Online publication date: 3-May-2024
https://dl.acm.org/doi/10.14778/3648160.3648168
Chan THou U LChoi BXu JCheng R(2023)Kernel Density Visualization for Big Geospatial Data: Algorithms and Applications2023 24th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM58254.2023.00046(231-234)Online publication date: Jul-2023
https://doi.org/10.1109/MDM58254.2023.00046
Chan TU LPeng YChoi BXu J(2022)Fast network k-function-based spatial analysisProceedings of the VLDB Endowment10.14778/3551793.355183615:11(2853-2866)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.14778/3551793.3551836

Index Terms

SLAM: Efficient Sweep Line Algorithms for Kernel Density Visualization

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cover image ACM Conferences

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

June 2022

2597 pages

ISBN:9781450392495

DOI:10.1145/3514221

General Chair:
Zachary Ives
University of Pennsylvania (USA)
,
Program Chairs:
Angela Bonifati
Lyon 1 University (France)
,
Amr El Abbadi
University of California, Santa Barbara (USA)

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Published: 11 June 2022

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Hong Kong RGC Projects
the Science and Technology Development Fund Macau
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University of Macau
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SIGMOD/PODS '22: International Conference on Management of Data

June 12 - 17, 2022

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Cited By

Chan TZang RZhu BU LWu DXu J(2024)LION: Fast and High-Resolution Network Kernel Density VisualizationProceedings of the VLDB Endowment10.14778/3648160.364816817:6(1255-1268)Online publication date: 3-May-2024
https://dl.acm.org/doi/10.14778/3648160.3648168
Chan THou U LChoi BXu JCheng R(2023)Kernel Density Visualization for Big Geospatial Data: Algorithms and Applications2023 24th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM58254.2023.00046(231-234)Online publication date: Jul-2023
https://doi.org/10.1109/MDM58254.2023.00046
Chan TU LPeng YChoi BXu J(2022)Fast network k-function-based spatial analysisProceedings of the VLDB Endowment10.14778/3551793.355183615:11(2853-2866)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.14778/3551793.3551836

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