research-article

PPQ-trajectory: spatio-temporal quantization for querying in large trajectory repositories

Authors:

Hakan FerhatosmanogluAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 14, Issue 2

Pages 215 - 227

https://doi.org/10.14778/3425879.3425891

Published: 01 October 2020 Publication History

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.

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Cited By

Wang SEravci BGuliyev RFerhatosmanoglu HFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message PropagationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614955(2626-2636)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614955
Zhang DChang ZYang DLi DTan KChen KChen G(2023)SQUID: subtrajectory query in trillion-scale GPS databaseThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-022-00777-732:4(887-904)Online publication date: 19-Jan-2023
https://dl.acm.org/doi/10.1007/s00778-022-00777-7
Zhao YChen GJia Z(2022)TODProceedings of the VLDB Endowment10.14778/3570690.357070316:3(546-560)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.14778/3570690.3570703
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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 14, Issue 2

October 2020

167 pages

ISSN:2150-8097

Editors:
Xin Luna Dong
Amazon
,
Felix Naumann
HPI, University of Potsdam

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VLDB Endowment

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Published: 01 October 2020

Published in PVLDB Volume 14, Issue 2

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Cited By

Wang SEravci BGuliyev RFerhatosmanoglu HFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message PropagationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614955(2626-2636)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614955
Zhang DChang ZYang DLi DTan KChen KChen G(2023)SQUID: subtrajectory query in trillion-scale GPS databaseThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-022-00777-732:4(887-904)Online publication date: 19-Jan-2023
https://dl.acm.org/doi/10.1007/s00778-022-00777-7
Zhao YChen GJia Z(2022)TODProceedings of the VLDB Endowment10.14778/3570690.357070316:3(546-560)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.14778/3570690.3570703
Wang SSun YMusco CBao ZLi GLi ZIdreos SSrivastava D(2021)Public Transport PlanningProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457247(1906-1919)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457247
Wang SBao ZCulpepper JCong G(2021)A Survey on Trajectory Data Management, Analytics, and LearningACM Computing Surveys10.1145/344020754:2(1-36)Online publication date: 5-Mar-2021
https://dl.acm.org/doi/10.1145/3440207
Zheng JWang SJin CGao MZhou ANi L(2021)Trajectory Similarity Search with Multi-level SemanticsAlgorithms and Architectures for Parallel Processing10.1007/978-3-030-95391-1_38(602-619)Online publication date: 3-Dec-2021
https://dl.acm.org/doi/10.1007/978-3-030-95391-1_38

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