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- W3210143417 abstract "Due to the rapid growth in location-based services throughout the years, extremely large volumes of trajectory data are generated and stored every day. One major challenge is how to efficiently process and analyze this data at scale. One crucial operation in numerous applications, e.g., shipping, transport planning etc., is the join operation on trajectories which takes two or more trajectory datasets and determines where the trajectories are spatially and temporally close.In this paper, we develop a distributed approach to answer trajectory join queries for large trajectory datasets. Our technique differs from other approaches by using a sequence of line segments to represent trajectories rather than a sequence of points. As we show in extensive experiments, doing so improves the efficiency and scalability of our approach by accelerating up to 43x compared to state of the art." @default.
- W3210143417 created "2021-11-08" @default.
- W3210143417 creator A5041993379 @default.
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- W3210143417 date "2021-09-01" @default.
- W3210143417 modified "2023-09-23" @default.
- W3210143417 title "Distributed Spatiotemporal Distance Join for Trajectory Data" @default.
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- W3210143417 doi "https://doi.org/10.1109/escience51609.2021.00021" @default.
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