Abstract
Spatial data, in particular, spherical data, are essential for earth science, space science, astronomy, and any domains where observed objects are often located on the surface of the unit sphere. The volume challenge of such data and increasing importance for fine-granularity queries demand a framework that can handle big spherical data with advanced spherical index support. In this paper, we present Spatial-HTM, a MapReduce-based system with the Hierarchical Triangular Mesh (HTM) index support, and implement range querying of arbitrary convexes through the HTM preprocessing, operation, and storage modules of Spatial-HTM. The experiments show that Spatial-HTM outperforms Hadoop and SpatialHadoop with the Grid index in terms of both throughput and the number of returned points.
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Yan, J., Zuo, H., Zhao, Y., Li, Y. (2018). Spatial-HTM: A MapReduce-Based System for Querying Spatial Data with the Hierarchical Triangular Mesh. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10962. Springer, Cham. https://doi.org/10.1007/978-3-319-95168-3_45
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DOI: https://doi.org/10.1007/978-3-319-95168-3_45
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