LION: Fast and High-Resolution Network Kernel Density Visualization

Tsz Nam Chan, Rui Zang, Bojian Zhu, Leong Hou U, Dingming Wu*, Jianliang Xu

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1255-1268
Number of pages14
JournalProceedings of the VLDB Endowment
Volume17
Issue number6
DOIs
Publication statusPublished - Feb 2024
Event50th International Conference on Very Large Data Bases, VLDB 2024 - Guangzhou, China
Duration: 25 Aug 202429 Aug 2024

Scopus Subject Areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

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