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An effective spatial join method for blockchain-based geospatial data using hierarchical quadrant spatial LSM+ tree

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Abstract

The prevention of forgery and alternation of important data of blockchain technology is contributing widely to the expanding usage of this technology to areas and industries such as real estate and agriculture. Despite the high utilization of the blockchain, its write-intensive feature causes a large amount of disk I/Os when trying to index and process queries over the data. Among previous studies, the hierarchical quadrant spatial LSM tree (i.e., HQ-sLSM tree) was proposed as an effective structure to index large amounts of geospatial point data from the blockchain and process queries while triggering a low number of disk I/Os. However, geospatial data exist in forms such as lines and polygons inside cadastral maps and survey information. In this paper, we propose an extended version of the HQ-sLSM tree which indexes geospatial line and polygon data. The extended tree, named the HQ-sLSM\(^{+}\) tree, inherits and adapts some common features and the low disk I/O algorithms of the original HQ-sLSM tree, fitting them to the line and polygon data types. Furthermore, an algorithm to process the spatial join query over two HQ-sLSM\(^{+}\) trees is proposed. A concept of a spatial join filter is introduced to access disk components efficiently. Experiments confirmed that the number of disk I/Os triggered when spatially joining two HQ-sLSM\(^{+}\) trees was much less compared to existing baseline index trees such as the R-tree and the LSM R-tree.

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Acknowledgments

This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) through the Korean Government [Ministry of Science and ICT (MSIT)], Development of High-Speed Analysis of Distributed Large-Scale Data for Wide Usability of Various Industries, under Grant 2021-0-00180, and in part by the Korea Institute for Advanced of Technology (KIAT) funded by the Korean Government [Ministry of Trade, Industry and Energy (MOTIE)] through the Human Resource Development (HRD) Program for Industrial Innovation under Grant P0020535.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sogang University, Seoul, 121-742, Republic of Korea

    Junghyun Lee, Taehyeon Kwon & Sungwon Jung

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  1. Junghyun Lee
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Contributions

T.K and J.L. wrote the main manuscript text. T.K revised the paper. J.L. improved and implemented the HQ-sLSM+ tree indexing method and its spatial join algorithm. J.L. also did the extensive experiments for the performance analysis given in the paper. T.K. developed the initial version of HQ-sLSM+ tree indexing scheme and its spatial join algorithm. S.J. proposed the basic idea of HQ-sLSM+ tree indexing scheme and its spatial join algorithm. S.J. guided J.L and T.K for the development of HQ-sLSM+ tree indexing scheme and its spatial join algorithm. S.J. also guided J.L. and T.K in writing this paper. All authors reviewed the manuscript.

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Correspondence to Sungwon Jung.

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Lee, J., Kwon, T. & Jung, S. An effective spatial join method for blockchain-based geospatial data using hierarchical quadrant spatial LSM+ tree. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06134-5

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