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SLAM: Efficient Sweep Line Algorithms for Kernel Density Visualization
Chan, Tsz Nam1; Leong Hou, U.2; Choi, Byron1; Xu, Jianliang1
2022-06-10
Source PublicationProceedings of the ACM SIGMOD International Conference on Management of Data
Pages2120-2134
AbstractKernel 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.
Keywordexploratory operations hotspot detection kernel density visualization reduce the time complexity SLAM
DOI10.1145/3514221.3517823
URLView the original
Language英語English
Scopus ID2-s2.0-85130334524
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Document TypeConference paper
CollectionFaculty of Science and Technology
Affiliation1.Hong Kong Baptist University, Hong Kong, Hong Kong
2.University of Macau, State Key Laboratory of Internet of Things for Smart City, Macao
Recommended Citation
GB/T 7714
Chan, Tsz Nam,Leong Hou, U.,Choi, Byron,et al. SLAM: Efficient Sweep Line Algorithms for Kernel Density Visualization[C],2022:2120-2134.
APA Chan, Tsz Nam,Leong Hou, U.,Choi, Byron,&Xu, Jianliang.(2022).SLAM: Efficient Sweep Line Algorithms for Kernel Density Visualization.Proceedings of the ACM SIGMOD International Conference on Management of Data,2120-2134.
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