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PPQ-Trajectory : spatio-temporal quantization for querying in large trajectory repositories
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Wang, Shuang and Ferhatosmanoglu, Hakan (2021) PPQ-Trajectory : spatio-temporal quantization for querying in large trajectory repositories. Proceedings of the VLDB Endowment, 14 (2). pp. 215-227. doi:10.14778/3425879.3425891 ISSN 2150-8097.
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Official URL: https://doi.org/10.14778/3425879.3425891
Abstract
We present PPQ-trajectory, a spatio-temporal quantization based solution for querying large dynamic trajectory data. PPQ-trajectory includes a partition-wise predictive quantizer (PPQ) that generates an error-bounded codebook with autocorrelation and spatial proximity-based partitions. The codebook is indexed to run approximate and exact spatio-temporal queries over compressed trajectories. PPQ-trajectory includes a coordinate quadtree coding for the codebook with support for exact queries. An incremental temporal partition-based index is utilised to avoid full reconstruction of trajectories during queries. An extensive set of experimental results for spatio-temporal queries on real trajectory datasets is presented. PPQ-trajectory shows significant improvements over the alternatives with respect to several performance measures, including the accuracy of results when the summary is used directly to provide approximate query results, the spatial deviation with which spatio-temporal path queries can be answered when the summary is used as an index, and the time taken to construct the summary. Superior results on the quality of the summary and the compression ratio are also demonstrated.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Spatial systems , Temporal databases , Data mining | ||||||
Journal or Publication Title: | Proceedings of the VLDB Endowment | ||||||
Publisher: | ACM | ||||||
ISSN: | 2150-8097 | ||||||
Official Date: | October 2021 | ||||||
Dates: |
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Volume: | 14 | ||||||
Number: | 2 | ||||||
Page Range: | pp. 215-227 | ||||||
DOI: | 10.14778/3425879.3425891 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 26 October 2020 | ||||||
Date of first compliant Open Access: | 27 October 2020 | ||||||
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