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Contact Query Processing Based on Spatiotemporal Trajectory

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Spatial Data and Intelligence (SpatialDI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13887))

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Abstract

Due to the prevalence of location-based devices, user trajectories are widely available in daily life, and when an infectious disease outbreak occurs, contact tracking can be achieved by examining the trajectories of confirmed patients to identify other trajectories of direct or indirect contact. In this paper, we propose a generalized trajectory contact search (TCS) query that models the contact tracking problem and other similar trajectory-based problems. In addition, we propose a new method for building spatio-temporal indexes and an algorithm for DBSCAN clustering based on spatio-temporal lattices to find all contact trajectories, which iteratively performs a distance-based contact search to find all contact trajectories. The algorithm, which is able to downscale the location and time of trajectories into a one-dimensional data and maintain the spatio-temporal proximity of the data, reduces the dimensionality of the search and improves the time and space efficiency. Extensive experiments on large-scale real-world data demonstrate the effectiveness of our proposed solution compared to the baseline algorithm.

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Correspondence to Zhiming Ding .

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Zhang, S., Ding, Z. (2023). Contact Query Processing Based on Spatiotemporal Trajectory. In: Meng, X., et al. Spatial Data and Intelligence. SpatialDI 2023. Lecture Notes in Computer Science, vol 13887. Springer, Cham. https://doi.org/10.1007/978-3-031-32910-4_11

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  • DOI: https://doi.org/10.1007/978-3-031-32910-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-32909-8

  • Online ISBN: 978-3-031-32910-4

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