基于HBase的矢量空間數(shù)據(jù)存取關鍵技術研究
[Abstract]:With the development of information technology and spatial information acquisition technology, the development of global information technology and the wide application of GIS (Geographic Information system), spatial data is growing rapidly. In the face of the increasing amount of spatial data, the traditional spatial data management scheme faces the bottleneck of high concurrent reading and writing and expansibility. Cloud computing can meet the needs of massive data storage, big data parallel processing, high concurrent retrieval and so on. In view of the many advantages of cloud computing technology, this paper focuses on how to use cloud computing technology to access mass vector space data. This paper focuses on the research and design of vector spatial data storage model, spatial index construction, data organization scheme, data import, spatial query strategy and attribute SQL query on HBase. This paper focuses on the following aspects: (1) introduction to the research background of vector spatial data cloud storage and retrieval and analysis of related theory and technology. This paper describes the research background and significance of cloud access for massive spatial data storage, analyzes the general situation of cloud computing at home and abroad, the research status quo of cloud access of spatial data and the shortcomings of current research. Combined with the characteristics of Map Reduce parallel computing framework, the feasibility of vector spatial data parallel processing in Map Reduce is analyzed. The advantages of distributed database HBase and SQL On Hadoop related cloud computing technology in storing and managing massive vector spatial data are discussed. (2) the vector spatial data storage model based on HBase and the integration of No SQL model and relational model are constructed. Vector spatial data management scheme. According to the characteristics of vector spatial data, combined with the HBase data model, the vector spatial data storage model is designed, and the multilevel grid index is designed by using the quadtree hierarchical partition technology. Combined with the clustering characteristics of spatial information multilevel grid coding and Hilbert space filling curve, the vector spatial data identification coding is designed according to HBase database Row Key storage rules, according to HBase database storage rules and Phoenix operation structured data characteristics. This paper proposes and designs a vector spatial data management scheme which integrates No SQL model and relational model. (3) A vector spatial data storage strategy and a parallel spatial index strategy are designed. Combined with the characteristics of Map Reduce parallel processing, this paper discusses and designs the input scheme of single machine importing vector spatial data and Map Reduce parallel processing vector spatial data, and designs a parallel spatial index scheme based on Map Reduce. (4) according to the multi-level grid index strategy, we design a parallel spatial index scheme. Spatial query strategy is designed. According to the characteristics of different spatial query operators, multilevel grid index and HBase scanning query data, the optimization strategy of spatial query operator is designed and implemented. There are three spatial query optimization strategies: merging trellis coding optimizing query strategy and restricting scanning column cluster optimizing data filtering strategy. Finally, the prototype system of vector spatial data access based on HBase is designed and implemented. The grid index and multilevel grid index are implemented. The efficiency of spatial query between grid index and multilevel grid index is compared. The validity of multilevel grid index is verified. Based on the multilevel grid index, the effectiveness of three spatial query optimization strategies, namely spatial query operator optimization strategy, combined grid coding optimization query strategy and restricted scan column cluster optimization data filtering strategy, is verified.
【學位授予單位】:江西理工大學
【學位級別】:碩士
【學位授予年份】:2017
【分類號】:P208
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