Abstract
In this paper, we proposed an efficient R-tree construction method by bulk loading over spatial-temporal data stream. The core idea is to partition spatial-temporal data stream into time windows and construct an R-tree for each time window. In each time window, we parallelized space partitioning and data stream reception during R-tree construction; and then we adopted sorting-based bulk loading scheme to optimize R-tree construction, which avoided unnecessary synchronization overhead and accelerated the R-tree construction. In addition, to reduce the sorting cost of R-tree bulk loading, sampling-based space partitioning scheme was introduced. Theoretical analysis and experiments demonstrated the effectiveness of our proposed method.
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Zhang, T., Yang, L., Shen, D., Fan, Y. (2019). An Efficient In-Memory R-Tree Construction Scheme for Spatio-Temporal Data Stream. In: Liu, X., et al. Service-Oriented Computing – ICSOC 2018 Workshops. ICSOC 2018. Lecture Notes in Computer Science(), vol 11434. Springer, Cham. https://doi.org/10.1007/978-3-030-17642-6_22
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DOI: https://doi.org/10.1007/978-3-030-17642-6_22
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