【摘要】：隨著信息技術(shù)和空間信息獲取技術(shù)的發(fā)展、全球信息化推進(jìn)和GIS(地理信息系統(tǒng))的廣泛應(yīng)用,空間數(shù)據(jù)高速增長(zhǎng)。面對(duì)日益增長(zhǎng)的海量空間數(shù)據(jù),傳統(tǒng)的空間數(shù)據(jù)管理方案面臨高并發(fā)讀寫以及擴(kuò)展性等等瓶頸。而云計(jì)算高擴(kuò)展的存儲(chǔ)能力以及強(qiáng)大的計(jì)算能力則可以滿足海量數(shù)據(jù)存儲(chǔ)、大數(shù)據(jù)并行處理、高并發(fā)檢索等方面的需求。鑒于云計(jì)算技術(shù)的諸多優(yōu)點(diǎn),論文針對(duì)如何利用云計(jì)算技術(shù)實(shí)現(xiàn)對(duì)海量矢量空間數(shù)據(jù)的存取展開研究。重點(diǎn)對(duì)云平臺(tái)下矢量空間數(shù)據(jù)的存儲(chǔ)模型、空間索引構(gòu)建、數(shù)據(jù)組織方案、數(shù)據(jù)的導(dǎo)入、空間查詢策略以及在HBase上進(jìn)行屬性SQL查詢等進(jìn)行了研究與設(shè)計(jì)。論文圍繞以下幾個(gè)方面開展工作:(1)矢量空間數(shù)據(jù)云存儲(chǔ)與檢索研究背景介紹及相關(guān)理論技術(shù)分析。論文闡述了海量空間數(shù)據(jù)存儲(chǔ)云存取的研究背景以及意義;分析了當(dāng)前國(guó)內(nèi)外云計(jì)算概況及空間數(shù)據(jù)云存取的研究現(xiàn)狀以及當(dāng)前研究的不足;結(jié)合Map Reduce并行計(jì)算框架特性深入分析了Map Reduce的矢量空間數(shù)據(jù)并行處理可行性,并探討分布式數(shù)據(jù)庫(kù)HBase以及SQL On Hadoop相關(guān)云計(jì)算技術(shù)存儲(chǔ)和管理海量矢量空間數(shù)據(jù)的優(yōu)勢(shì)。(2)構(gòu)建了基于HBase的矢量空間數(shù)據(jù)存儲(chǔ)模型以及No SQL模型與關(guān)系模型一體化的矢量空間數(shù)據(jù)的管理方案。針對(duì)矢量空間數(shù)據(jù)的特點(diǎn),結(jié)合HBase數(shù)據(jù)模型,設(shè)計(jì)了矢量空間數(shù)據(jù)存儲(chǔ)模型,并采用四叉樹層次剖分技術(shù)設(shè)計(jì)了多級(jí)格網(wǎng)索引;結(jié)合空間信息多級(jí)格網(wǎng)編碼和Hilbert空間填充曲線的聚類特性,設(shè)計(jì)了符合HBase數(shù)據(jù)庫(kù)Row Key存儲(chǔ)規(guī)則的矢量空間數(shù)據(jù)標(biāo)識(shí)編碼;根據(jù)HBase數(shù)據(jù)庫(kù)存儲(chǔ)規(guī)則以及Phoenix操作結(jié)構(gòu)化數(shù)據(jù)特性,提出并設(shè)計(jì)了No SQL模型與關(guān)系模型一體化的矢量空間數(shù)據(jù)的管理方案。(3)設(shè)計(jì)了矢量空間數(shù)據(jù)入庫(kù)以及并行構(gòu)建空間索引策略。結(jié)合Map Reduce并行處理特性討論并設(shè)計(jì)了單機(jī)導(dǎo)入和基于Map Reduce并行處理矢量空間數(shù)據(jù)的入庫(kù)方案以及基于Map Reduce設(shè)計(jì)了并行構(gòu)建空間索引方案。(4)根據(jù)多級(jí)網(wǎng)格索引策略設(shè)計(jì)了空間查詢策略。根據(jù)不同空間查詢算子、多級(jí)網(wǎng)格索引特點(diǎn)以及HBase掃描查詢數(shù)據(jù)特性,設(shè)計(jì)并實(shí)現(xiàn)了空間查詢算子優(yōu)化策略、合并網(wǎng)格編碼優(yōu)化查詢策略以及限制掃描列簇優(yōu)化數(shù)據(jù)過(guò)濾策略等三種空間查詢優(yōu)化策略。最后設(shè)計(jì)并實(shí)現(xiàn)了基于HBase的矢量空間數(shù)據(jù)存取原型系統(tǒng),實(shí)現(xiàn)了網(wǎng)格索引以及多級(jí)網(wǎng)格索引,通過(guò)網(wǎng)格索引與多級(jí)網(wǎng)格索引空間查詢效率對(duì)比實(shí)驗(yàn),驗(yàn)證了多級(jí)網(wǎng)格索引的有效性。并基于多級(jí)網(wǎng)格索引,驗(yàn)證了空間查詢算子優(yōu)化策略、合并網(wǎng)格編碼優(yōu)化查詢策略以及限制掃描列簇優(yōu)化數(shù)據(jù)過(guò)濾策略等三種空間查詢優(yōu)化策略的有效性。
[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(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P208

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 吳黎兵;邱鑫;葉璐瑤;王曉棟;聶雷;;基于Hadoop的SQL查詢引擎性能研究[J];華中師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年02期

2 李德仁;;展望大數(shù)據(jù)時(shí)代的地球空間信息學(xué)[J];測(cè)繪學(xué)報(bào);2016年04期

3 張葉;許國(guó)艷;花青;;基于HBase的矢量空間數(shù)據(jù)存儲(chǔ)與訪問(wèn)優(yōu)化[J];計(jì)算機(jī)應(yīng)用;2015年11期

4 宋寶燕;王俊陸;王妍;;基于范德蒙碼的HDFS優(yōu)化存儲(chǔ)策略研究[J];計(jì)算機(jī)學(xué)報(bào);2015年09期

5 鄭坤;付艷麗;;基于HBase和GeoTools的矢量空間數(shù)據(jù)存儲(chǔ)模型研究[J];計(jì)算機(jī)應(yīng)用與軟件;2015年03期

6 李清泉;李德仁;;大數(shù)據(jù)GIS[J];武漢大學(xué)學(xué)報(bào)(信息科學(xué)版);2014年06期

7 孟輝;朱美正;張鋒葉;;基于Hadoop的矢量空間數(shù)據(jù)庫(kù)技術(shù)[J];計(jì)算機(jī)與現(xiàn)代化;2014年02期

8 尹芳;馮敏;諸云強(qiáng);劉睿;;基于開源Hadoop的矢量空間數(shù)據(jù)分布式處理研究[J];計(jì)算機(jī)工程與應(yīng)用;2013年16期

9 陳崇成;林劍峰;吳小竹;巫建偉;連惠群;;基于NoSQL的海量空間數(shù)據(jù)云存儲(chǔ)與服務(wù)方法[J];地球信息科學(xué)學(xué)報(bào);2013年02期

10 林德根;梁勤歐;;云GIS的內(nèi)涵與研究進(jìn)展[J];地理科學(xué)進(jìn)展;2012年11期

相關(guān)博士學(xué)位論文 前1條

1 范建永;基于Hadoop的云GIS若干關(guān)鍵技術(shù)研究[D];解放軍信息工程大學(xué);2013年

相關(guān)碩士學(xué)位論文 前2條

1 祝若鑫;云計(jì)算環(huán)境下的空間矢量數(shù)據(jù)存儲(chǔ)與管理[D];解放軍信息工程大學(xué);2015年

2 丁琛;基于HBase的空間數(shù)據(jù)分布式存儲(chǔ)和并行查詢算法研究[D];南京師范大學(xué);2014年

，

本文編號(hào)：2139716