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首页> 外文期刊>GIScience & remote sensing >Accelerating the computation of multi-scale visual curvature for simplifying a large set of polylines with Hadoop
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Accelerating the computation of multi-scale visual curvature for simplifying a large set of polylines with Hadoop

机译：加快多尺度视觉曲率的计算，以使用Hadoop简化大量折线

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摘要

Over the years, the vast majority of curvature-based simplification algorithms for vector data have employed pseudo-curvatures, rather than real curvatures. This is because the vector data in the field of Geographical Information Science (GIScience) is usually represented in the form of polylines, but polylines do not meet the requirements of traditional curvature calculation. This situation has been improved since the multi-scale visual curvature (MVC) was proposed. However, due to the high complexity and huge computation needed for the algorithm, it is difficult to make effective use of MVC in GIScience. In this paper, the MVC algorithm is used to simplify big vector data in a Hadoop-based cloud. The main challenge is that both the data and computation are simultaneously intensive. An accelerated MVC algorithm for simplification is proposed in this paper. This algorithm is performed by adopting a two-level acceleration approach: (1) a simplified calculation method of MVC for the vector data in GIScience, and (2) a parallelization strategy for the MVC algorithm in the Hadoop-based cloud. When simplifying big vector data in gigabyte (GB) size, the execution time is reduced to less than 2.2% of the original time. The proposed accelerated MVC algorithm has great potential in many GIScience applications, including map generalization, DEM simplification, and spatial-temporal data compression.
机译：多年来，绝大多数基于曲率的矢量数据简化算法都采用伪曲率，而不是实际曲率。这是因为地理信息科学（GIScience）领域中的矢量数据通常以折线的形式表示，但是折线不能满足传统曲率计算的要求。由于提出了多尺度视曲率（MVC），这种情况已得到改善。然而，由于该算法需要很高的复杂度和庞大的计算量，因此难以在GIS科学中有效地使用MVC。在本文中，MVC算法用于简化基于Hadoop的云中的大矢量数据。主要的挑战是数据和计算都同时密集。提出了一种简化的加速MVC算法。该算法通过采用两级加速方法来执行：（1）GIScience中矢量数据的MVC简化计算方法，以及（2）基于Hadoop的云中MVC算法的并行化策略。当简化千兆字节（GB）大小的大矢量数据时，执行时间减少到少于原始时间的2.​​2％。所提出的加速MVC算法在许多GIS科学应用中具有巨大的潜力，包括地图综合，DEM简化和时空数据压缩。

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  • 来源
    《GIScience & remote sensing》 |2015年第3期|315-331|共17页
  • 作者

    Gao Peichao; Liu Zhao; Han Fei; Tang Lei; Xie Meihui;

  • 作者单位

    Tsinghua Univ, Dept Civil Engn, Inst Geomat, Beijing 100084, Peoples R China;

    Tsinghua Univ, Dept Civil Engn, Inst Geomat, Beijing 100084, Peoples R China;

    Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China;

    Univ Arizona, Dept Geog & Dev, Tucson, AZ 85721 USA;

    Tsinghua Univ, Dept Civil Engn, Inst Geomat, Beijing 100084, Peoples R China;

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