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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References
Progress SURVEYING AND MAPPING navigation and geographic information science and technology - to celebrate the “Science of Surveying and Mapping Technology” founded 30 years. Surv. Map. Sci. Technol. 2014(5): 441–449 (2014)
Wang, Y.-J., Sun, W., Zhou, S.: Key technologies of distributed storage for cloud computing. Software 23(4), 962–986 (2012)
Wang, Y.: Several key technologies of geographic information service based on Hadoop cloud computing platform. Ph.D. thesis, Graduate School of Chinese Academy of Sciences (2011)
Cary, A., Sun, Z., Hristidis, V., Rishe, N.: Experiences on processing spatial data with MapReduce. In: Winslett, M. (ed.) SSDBM 2009. LNCS, vol. 5566, pp. 302–319. Springer, Heidelberg (2009). doi:10.1007/978-3-642-02279-1_24
Jerome, D., Cyrille, B., Flanvien, M.: Simple method for the estimation of the short-term of GNSS on-board clocks. In: Proceedings of 42nd Annual Precise Time and Time Interval (PT-TI) Meeting, pp. 215–223. The Institute of Navigation, Virginia (2010)
Lars, G.: HBase: The Definitive Guide, pp. 319–323. O’Reilly Media, Newton (2011)
Zheng, K., Fu, Y.: Vector’s spatial data storage model based on HBase and GeoTools. Comput. Appl. Softw. 2015(3), 23–26 (2015)
Han, H., Cheng, C.Q., Wang, Y., et al.: Rapid collection method of multi-source data based on global subdivision grid. Geomat. World 2014(6), 6–11 (2014)
Lu, F., Zhou, C.: A GIS spatial indexing approach based on Hilbert ordering code. Comput.-Aided Des. Comput. Graph. 13(5), 424–429 (2001)
Wang, Y., Meng, K.: Spatial partitioning of massive data based on Hilbert spatial ordering code. Geomat. Inf. Sci. Wunan Univ. 32(7), 650–653 (2007)
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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