Trajectory visualization based on multi-level spatio-temporal data organization model

Zhixin Yao; Jianqin Zhang; Taizeng Li; Ying Ding

doi:10.1117/12.2674426

28 April 2023 Trajectory visualization based on multi-level spatio-temporal data organization model

Zhixin Yao, Jianqin Zhang, Taizeng Li, Ying Ding

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 126260F (2023) https://doi.org/10.1117/12.2674426
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

Current trajectory data grows rapidly in a dynamic and streaming form, and unreasonable data organization causes the problems of skewed data storage and high overhead, as well as slow retrieval speed and page lag during visualization. To achieve effective spatial data organization, this paper proposes a data storage model with multi-level spatio-temporal organization. Spatially, the trajectory data is partitioned based on Hilbert curve, combined with pre-partitioning mechanism to solve the storage skewing problem of distributed database HBase; temporally, borrowing from the organization of spatio-temporal cube, the spatio-temporal hybrid coding is constructed by using the method of slicing by day and minute system coding to solve the retrieval of trajectory data into maps. The experiment proves that the organization model can effectively improve the data storage and retrieval efficiency, enhance the overall effect of trajectory visualization, and provide effective technical support for data mining and analysis.

Citation Download Citation

Zhixin Yao, Jianqin Zhang, Taizeng Li, and Ying Ding "Trajectory visualization based on multi-level spatio-temporal data organization model", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 126260F (28 April 2023); https://doi.org/10.1117/12.2674426

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