TY - JOUR
T1 - SWS: A Complexity-Optimized Solution for Spatial-Temporal Kernel Density Visualization
T2 - 48th International Conference on Very Large Data Bases, VLDB 2022
AU - Chan, Tsz Nam
AU - Ip, Pak Lon
AU - U, Leong Hou
AU - Choi, Byron
AU - Xu, Jianliang
N1 - Funding Information:
ACKNOWLEDGMENTS This work was supported by the National Key Research and Development Plan of China (No.2019YFB2102100), the Science and Technology Development Fund Macau (SKL-IOTSC-2021-2023, 0015/2019/AKP), University of Macau (MYRG2019-00119-FST), IRCMs/19-20/H01, RIF R2002-20F, Research Grants Council of Hong Kong (projects 12201518, 12202221, 12201520, C6030-18GF), and Guangdong Basic and Applied Basic Research Foundation (Project No. 2019B1515130001).
Publisher Copyright:
© 2021, VLDB Endowment. All rights reserved.
PY - 2021/12
Y1 - 2021/12
N2 - Spatial-temporal kernel density visualization (STKDV) has been extensively used in a wide range of applications, e.g., disease outbreak analysis, traffic accident hotspot detection, and crime hotspot detection. While STKDV can provide accurate and comprehensive data visualization, computing STKDV is time-consuming, which is not scalable to large-scale datasets. To address this issue, we develop a new sliding-window-based solution (SWS), which theoretically reduces the time complexity for generating STKDV, without increasing the space complexity. Moreover, we incorporate SWS with the progressive visualization framework, which can continuously output partial visualization results to users (from coarse to fine), until users satisfy the visualization. Our experimental studies on five large-scale datasets show that SWS achieves 1.71x to 24x speedup compared with the state-of-the-art methods.
AB - Spatial-temporal kernel density visualization (STKDV) has been extensively used in a wide range of applications, e.g., disease outbreak analysis, traffic accident hotspot detection, and crime hotspot detection. While STKDV can provide accurate and comprehensive data visualization, computing STKDV is time-consuming, which is not scalable to large-scale datasets. To address this issue, we develop a new sliding-window-based solution (SWS), which theoretically reduces the time complexity for generating STKDV, without increasing the space complexity. Moreover, we incorporate SWS with the progressive visualization framework, which can continuously output partial visualization results to users (from coarse to fine), until users satisfy the visualization. Our experimental studies on five large-scale datasets show that SWS achieves 1.71x to 24x speedup compared with the state-of-the-art methods.
UR - http://vldb.org/pvldb/vol15-volume-info/
UR - http://www.scopus.com/inward/record.url?scp=85130366911&partnerID=8YFLogxK
U2 - 10.14778/3503585.3503591
DO - 10.14778/3503585.3503591
M3 - Conference article
AN - SCOPUS:85130366911
SN - 2150-8097
VL - 15
SP - 814
EP - 827
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
IS - 4
Y2 - 5 September 2022 through 9 September 2022
ER -