本文關(guān)鍵詞： 空間大數(shù)據(jù) 空間子域 鄰域空間操作 空間劃分　出處：《浙江大學(xué)》2017年博士論文　論文類型：學(xué)位論文

【摘要】：空天地立體觀測(cè)與移動(dòng)互聯(lián)網(wǎng)技術(shù)的蓬勃發(fā)展帶來(lái)爆炸式增長(zhǎng)的空間大數(shù)據(jù),迫使空間分析計(jì)算模式從集中式處理、單人機(jī)交互向高擴(kuò)展性、高效性、數(shù)據(jù)多源性方向轉(zhuǎn)變。利用云計(jì)算資源實(shí)現(xiàn)空間大數(shù)據(jù)并行化處理是完成這一模式轉(zhuǎn)變的重要途徑。云環(huán)境下的并行計(jì)算范式本質(zhì)上是一種單指令多數(shù)據(jù)流并行,該范式要求將數(shù)據(jù)集劃分成獨(dú)立的無(wú)共享的部分并行處理。然而,空間數(shù)據(jù)具有組織異構(gòu)、分布不均衡、實(shí)體關(guān)聯(lián)性強(qiáng)等特點(diǎn),這導(dǎo)致空間數(shù)據(jù)無(wú)法直接分割以適應(yīng)云環(huán)境下的并行計(jì)算范式,傳統(tǒng)的并行空間計(jì)算方法大多面向特定的應(yīng)用場(chǎng)景,缺乏對(duì)空間實(shí)體關(guān)聯(lián)關(guān)系及分布特征的考慮,未能形成包括空間大數(shù)據(jù)組織存儲(chǔ)、劃分計(jì)算、效率優(yōu)化等在內(nèi)的并行計(jì)算方法體系。針對(duì)以上問(wèn)題,本文開(kāi)展了顧及空間子域分布特征的空間大數(shù)據(jù)并行計(jì)算方法體系研究,提出了不同空間子域分布特征的空間操作數(shù)據(jù)劃分策略與并行化方法,以實(shí)際空間計(jì)算場(chǎng)景為例,采用真實(shí)的空間大數(shù)據(jù)集對(duì)本文提出的方法進(jìn)行了正確性與高效性驗(yàn)證,為云環(huán)境下億級(jí)空間大數(shù)據(jù)計(jì)算提供了方法支撐與案例借鑒。本文的研究?jī)?nèi)容概括如下:(1)遵循云環(huán)境下并行計(jì)算范式要求,設(shè)計(jì)了空間數(shù)據(jù)云存儲(chǔ)組織與并行空間計(jì)算統(tǒng)一流程表達(dá)方法,研究了面向數(shù)據(jù)劃分的空操作分類及其空間子域分布特征,提出了空間子域的任務(wù)計(jì)算量評(píng)估方法,形成完整的并行空間計(jì)算方法體系。(2)在上述基礎(chǔ)上,針對(duì)本地空間操作的特性,設(shè)計(jì)了基于默認(rèn)子域和基于格網(wǎng)子域的兩種通用本地空間操作數(shù)據(jù)劃分方法,基于這兩種方法實(shí)現(xiàn)了空間頻率圖和多級(jí)金字塔矢量圖的并行繪制,以十億級(jí)全球興趣點(diǎn)與百萬(wàn)級(jí)矢量多邊形為測(cè)試數(shù)據(jù)對(duì)比驗(yàn)證了方法的適用性與高效性。(3)提出了鄰域空間操作的三種規(guī)則空間子域分布形態(tài):范圍分布的規(guī)則空間子域、范圍時(shí)空分布的規(guī)則空間子域、異構(gòu)數(shù)據(jù)疊加誤差導(dǎo)致的規(guī)則空間子域,對(duì)上述三種子域分布特性的空間操作分別設(shè)計(jì)了并行化方法,分別以空間距離連接、時(shí)空熱點(diǎn)分析、大規(guī)模三維地表表面積計(jì)算為案例驗(yàn)證了方法的適用性與高效性。(4)針對(duì)空間子域不規(guī)則分布的鄰域空間操作的特點(diǎn),以K鄰近連接為例,提出了基于格網(wǎng)均勻擴(kuò)張的不規(guī)則空間子域范圍確定方法和基于Voronoi的不規(guī)則空間子域范圍確定方法,在此基礎(chǔ)上實(shí)現(xiàn)了 K鄰近連接的并行化算法,通過(guò)性能實(shí)驗(yàn)對(duì)比了兩種方法的適用性與高效性。
[Abstract]:The booming development of space space stereoscopic observation and mobile Internet technology has brought explosive growth of space big data, forcing the spatial analysis and calculation model from centralized processing, single man-machine interaction to high scalability and efficiency. Using cloud computing resources to realize spatial big data parallelization is an important way to accomplish this pattern transformation. The parallel computing paradigm in cloud environment is essentially a single instruction multi-data flow parallelism. However, spatial data has the characteristics of heterogeneous organization, uneven distribution, strong entity correlation and so on. As a result, spatial data can not be partitioned directly to adapt to the parallel computing paradigm in the cloud environment. Most of the traditional parallel spatial computing methods are oriented to specific application scenarios, and lack of consideration of spatial entity association and distribution features. The parallel computing method system including spatial big data organization storage, partition calculation, efficiency optimization and so on has not been formed. In view of the above problems, this paper has carried out the research on the spatial big data parallel computing method system, which takes into account the spatial subdomain distribution characteristics. In this paper, the partition strategy and parallelization method of spatial operation data with different spatial subdomain distribution characteristics are proposed. Taking the actual spatial computing scene as an example, the correctness and efficiency of the proposed method are verified by using the real spatial big data set. It provides method support and case reference for big data computing in the cloud environment. The research contents of this paper are summarized as follows: 1) following the requirements of parallel computing paradigm in cloud environment. The unified flow representation method of cloud storage organization and parallel spatial computing for spatial data is designed. The spatial operation classification and its spatial subdomain distribution characteristics are studied, and the task computation evaluation method based on spatial subdomain is proposed. On the basis of the above, two general local spatial operation data partitioning methods based on default subdomain and grid subdomain are designed according to the characteristics of local space operation. Based on these two methods, the parallel rendering of spatial frequency map and multi-level pyramid vector graph is realized. The applicability and efficiency of the method are verified by comparing the 1 billion level global interest points with the million-level vector polygons. (3) three kinds of regular space subdomain distributions of neighborhood space operations are proposed: the regular space subdomains of the range distribution. The rule space subdomain of the scope space-time distribution and the rule space subdomain caused by the error of heterogeneous data superposition are designed to parallelize the spatial operations of the distribution characteristics of the above three subdomains, respectively, which are connected by space distance and analyzed by space-time hot spots. Large scale 3D surface area calculation is a case study to verify the applicability and efficiency of the method. Aiming at the characteristics of the spatial operation of the irregular distribution of the spatial subdomain, the paper takes K neighborhood connection as an example. In this paper, a method of determining the subdomain range of irregular space based on uniform expansion of grid and a method of determining subdomain range of irregular space based on Voronoi are proposed. On this basis, the parallel algorithm of K-adjacent connection is implemented. The applicability and efficiency of the two methods are compared by performance experiments.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.13;P208

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