TY - JOUR
T1 - LION
T2 - 50th International Conference on Very Large Data Bases, VLDB 2024
AU - Chan, Tsz Nam
AU - Zang, Rui
AU - Zhu, Bojian
AU - U, Leong Hou
AU - Wu, Dingming
AU - Xu, Jianliang
N1 - Funding information:
This work was supported by the Natural Science Foundation of China under grants 62202401 and 62372308, the Natural Science Foundation of Guangdong Province of China under grant 2023A1515011619, the Science and Technology Development Fund Macau SAR (0052/2023/RIA1, 0031/2022/A, 001/2024/SKL), the Research Grant of University of Macau (MYRG2022-00252-FST), Wuyi University Hong Kong and Macau joint Research Fund (2021WGALH14), and the Hong Kong RGC Project GRF 12202221
Publisher Copyright:
© 2024, VLDB Endowment. All rights reserved.
PY - 2024/2
Y1 - 2024/2
N2 - Network Kernel Density Visualization (NKDV) has often been used in a wide range of applications, e.g., criminology, transportation science, and urban planning. However, NKDV is computationally expensive, which cannot be scalable to large-scale datasets and high resolution sizes. Although a recent work, called aggregate distance augmentation (ADA), has been developed for improving the efficiency to generate NKDV, this method is still slow and does not take the resolution size into account for optimizing the efficiency. In this paper, we develop a new solution, called LION, which can reduce the worst-case time complexity for generating high-resolution NKDV, without increasing the space complexity. Experiment results on four large-scale location datasets verify that LION can achieve 2.86x to 35.36x speedup compared with the state-of-the-art ADA method.
AB - Network Kernel Density Visualization (NKDV) has often been used in a wide range of applications, e.g., criminology, transportation science, and urban planning. However, NKDV is computationally expensive, which cannot be scalable to large-scale datasets and high resolution sizes. Although a recent work, called aggregate distance augmentation (ADA), has been developed for improving the efficiency to generate NKDV, this method is still slow and does not take the resolution size into account for optimizing the efficiency. In this paper, we develop a new solution, called LION, which can reduce the worst-case time complexity for generating high-resolution NKDV, without increasing the space complexity. Experiment results on four large-scale location datasets verify that LION can achieve 2.86x to 35.36x speedup compared with the state-of-the-art ADA method.
UR - http://www.scopus.com/inward/record.url?scp=85190664074&partnerID=8YFLogxK
U2 - 10.14778/3648160.3648168
DO - 10.14778/3648160.3648168
M3 - Conference article
AN - SCOPUS:85190664074
SN - 2150-8097
VL - 17
SP - 1255
EP - 1268
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
IS - 6
Y2 - 26 August 2024 through 30 August 2024
ER -
