research-article

Finding attribute-aware similar regions for data analysis

Authors:

Christian S. Jensen,

Tao GuoAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 12, Issue 11

Pages 1414 - 1426

https://doi.org/10.14778/3342263.3342277

Published: 01 July 2019 Publication History

Abstract

With the proliferation of mobile devices and location-based services, increasingly massive volumes of geo-tagged data are becoming available. This data typically also contains non-location information. We study how to use such information to characterize a region and then how to find a region of the same size and with the most similar characteristics. This functionality enables a user to identify regions that share characteristics with a user-supplied region that the user is familiar with and likes. More specifically, we formalize and study a new problem called the attribute-aware similar region search (ASRS) problem. We first define so-called composite aggregators that are able to express aspects of interest in terms of the information associated with a user-supplied region. When applied to a region, an aggregator captures the region's relevant characteristics. Next, given a query region and a composite aggregator, we propose a novel algorithm called DS-Search to find the most similar region of the same size. Unlike any previous work on region search, DS-Search repeatedly discretizes and splits regions until an split region either satisfies a drop condition or it is guaranteed to not contribute to the result. In addition, we extend DS-Search to solve the ASRS problem approximately. Finally, we report on extensive empirical studies that offer insight into the efficiency and effectiveness of the paper's proposals.

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Cited By

Jin JSong YKan DZhang BLyu YZhang JLu H(2024)Learning context-aware region similarity with effective spatial normalization over Point-of-Interest dataInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10367361:3Online publication date: 2-Jul-2024
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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 12, Issue 11

July 2019

543 pages

ISSN:2150-8097

Editors:
Lei Chen,
Fatma Özcan

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VLDB Endowment

Publication History

Published: 01 July 2019

Published in PVLDB Volume 12, Issue 11

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Cited By

Jin JSong YKan DZhang BLyu YZhang JLu H(2024)Learning context-aware region similarity with effective spatial normalization over Point-of-Interest dataInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10367361:3Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103673
Fang LKou ZZhou YZhang YYuan G(2023)In Search of the Max Coverage Region in Road NetworksRemote Sensing10.3390/rs1505128915:5(1289)Online publication date: 26-Feb-2023
https://doi.org/10.3390/rs15051289
R. Ramya Dr. S. Kannan (2022)Tracking Challenges in Online Social Environment using Deep Learning TechniquesInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT228121(162-170)Online publication date: 1-Feb-2022
https://doi.org/10.32628/CSEIT228121
Taniguchi RAmagata DHara T(2022)Retrieving Top-N Weighted Spatial k-cliques2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10021071(4952-4961)Online publication date: 17-Dec-2022
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Li JXu M(2021)A parametric approximation algorithm for spatial group keyword queriesIntelligent Data Analysis10.3233/IDA-19507125:2(305-319)Online publication date: 1-Jan-2021
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Zhao XBi XZeng XZhang YFang Q(2020)EDense: a convolutional neural network with ELM-based dense connectionsNeural Computing and Applications10.1007/s00521-020-05181-235:5(3651-3663)Online publication date: 29-Jul-2020
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