Abstract
The popularity of mobile terminals has given rise to an extremely large number of trajectories of moving objects. As a result, it is critical to provide effective and efficient query operations on large-scale trajectory data for further retrieval and analysis. Considering data partition has a great influence on processing large-scale data, we present a time-based partitioning technique on trajectory data. This partitioning technique can be applied on the distributed framework to improve the performance of range queries on massive trajectory data. Furthermore, the proposed method adopts time-based hash strategy to ensure both the partition balancing and less partitioning time. Especially, existing trajectory data are not required to be repartitioned when new data arrive. Extensive experiments on three real data sets demonstrated that the performance of the proposed technique outperformed other partitioning techniques.
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Acknowledgments
This study is supported by the National Natural Science Foundation of China (No. 61462017, 61363005, U1501252), Guangxi Natural Science Foundation of China (No. 2017GXNSFAA198035), and Guangxi Cooperative Innovation Center of Cloud Computing and Big Data.
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Yue, Z., Zhang, J., Zhang, H., Yang, Q. (2018). Time-Based Trajectory Data Partitioning for Efficient Range Query. In: Liu, C., Zou, L., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10829. Springer, Cham. https://doi.org/10.1007/978-3-319-91455-8_3
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DOI: https://doi.org/10.1007/978-3-319-91455-8_3
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