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Information 2018, 9(5), 116; https://doi.org/10.3390/info9050116

Vector Spatial Big Data Storage and Optimized Query Based on the Multi-Level Hilbert Grid Index in HBase

1
School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China
2
School of Architectural and Surveying & Mapping Engineering, JiangXi University of Science and Technology, 86 Hongqi Avenue, Ganzhou 341000, China
3
Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, China
4
Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Received: 16 March 2018 / Revised: 28 April 2018 / Accepted: 4 May 2018 / Published: 9 May 2018
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Abstract

Faced with the rapid growth of vector data and the urgent requirement of low-latency query, it has become an important and timely challenge to effectively achieve the scalable storage and efficient access of vector big data. However, a systematic method is rarely seen for vector polygon data storage and query taking spatial locality into account in the storage schema, index construction and query optimization. In the paper, we focus on the storage and topological query of vector polygon geometry data in HBase, and the rowkey in the HBase table is the concatenation of the Hilbert value of the grid cell to which the center of the object entity’s MBR belongs, the layer identifier and the order code. Then, a new multi-level grid index structure, termed Q-HBML, that incorporates the grid-object spatial relationship and a new Hilbert hierarchical code into the multi-level grid, is proposed for improving the spatial query efficiency. Finally, based on the Q-HBML index, two query optimization strategies and an optimized topological query algorithm, ML-OTQ, are presented to optimize the topological query process and enhance the topological query efficiency. Through four groups of comparative experiments, it has been proven that our approach supports better performance. View Full-Text
Keywords: cloud computing; HBase; vector big data; spatial index; spatial query cloud computing; HBase; vector big data; spatial index; spatial query
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Jiang, H.; Kang, J.; Du, Z.; Zhang, F.; Huang, X.; Liu, R.; Zhang, X. Vector Spatial Big Data Storage and Optimized Query Based on the Multi-Level Hilbert Grid Index in HBase. Information 2018, 9, 116.

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