Abstract
Nowadays, we enter the big data era. The amount of vector data is growing explosively. There is an urgent need for efficient storage method of vector big data. A cloud storage strategy of vector data based on HBase is proposed in this paper. Firstly, quadtree decomposition method is applied to build multi-level grid index and Hilbert space filling curve is applied to partition vector data. Secondly, vector element unique identifier is designed based on multi-level grid code and Hilbert sequence code. It is treated as RowKey of vector element in HBase. Thirdly, the storage rule of vector data is designed in detail. Finally, two contrast experiments are used to verify good feasibility and high efficiency of this proposed method.
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Acknowledgments
This work was supported by Natural Science Foundation of China (Project No. 41401462) and Scientific and technological Project of Zhengzhou (No. 112PPTGY225). The Authors would like to thank the anonymous reviewers for their valuable comments, which greatly helped us to clarify and improve the contents of paper.
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Zhu, R., Cheng, J., Fan, J., Chen, K. (2017). Research on Cloud Storage of Vector Data Based on HBase. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-10-3969-0_52
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DOI: https://doi.org/10.1007/978-981-10-3969-0_52
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