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ISPRS Int. J. Geo-Inf. 2018, 7(1), 26; https://doi.org/10.3390/ijgi7010026

Approach to Accelerating Dissolved Vector Buffer Generation in Distributed In-Memory Cluster Architecture

College of Electronic Science, National University of Defense Technology, Changsha 410073, China
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Received: 6 November 2017 / Revised: 9 January 2018 / Accepted: 11 January 2018 / Published: 15 January 2018
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Abstract

The buffer generation algorithm is a fundamental function in GIS, identifying areas of a given distance surrounding geographic features. Past research largely focused on buffer generation algorithms generated in a stand-alone environment. Moreover, dissolved buffer generation is data- and computing-intensive. In this scenario, the improvement in the stand-alone environment is limited when considering large-scale mass vector data. Nevertheless, recent parallel dissolved vector buffer algorithms suffer from scalability problems, leaving room for further optimization. At present, the prevailing in-memory cluster-computing framework—Spark—provides promising efficiency for computing-intensive analysis; however, it has seldom been researched for buffer analysis. On this basis, we propose a cluster-computing-oriented parallel dissolved vector buffer generating algorithm, called the HPBM, that contains a Hilbert-space-filling-curve-based data partition method, a data skew and cross-boundary objects processing strategy, and a depth-given tree-like merging method. Experiments are conducted in both stand-alone and cluster environments using real-world vector data that include points and roads. Compared with some existing parallel buffer algorithms, as well as various popular GIS software, the HPBM achieves a performance gain of more than 50%. View Full-Text
Keywords: data partition; Hilbert curve; buffer analysis; Spark data partition; Hilbert curve; buffer analysis; Spark
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Shen, J.; Chen, L.; Wu, Y.; Jing, N. Approach to Accelerating Dissolved Vector Buffer Generation in Distributed In-Memory Cluster Architecture. ISPRS Int. J. Geo-Inf. 2018, 7, 26.

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