A Parallel Approach for Accurate and High Performance Gridding of 3D Point Data


KIPS Transactions on Computer and Communication Systems, Vol. 3, No. 8, pp. 251-260, Aug. 2014
10.3745/KTCCS.2014.3.8.251, Full Text:

Abstract

3D point data is utilized in various industry domains for its high accuracy to the surface information of an object. It is substantially utilized in geography for terrain scanning and analysis. Generally, 3D point data need to be changed by Gridding which produces a regularly spaced array of z values from irregularly spaced xyz data. But it requires long processing time and high resource cost to interpolate grid coordination. Kriging interpolation in Gridding has attracted because Kriging interpolation has more accuracy than other methods. However it haven``t been used frequently since a processing is complex and slow. In this paper, we presented a parallel Gridding algorithm which contains Kriging and an application of grid data structure to fit MapReduce paradigm to this algorithm. Experiment was conducted for 1.6 and 4.3 billions of points from Airborne LiDAR files using our proposed MapReduce structure and the results show that the total execution time is decreased more than three times to the convention sequential program on three heterogenous clusters.


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Cite this article
[IEEE Style]
C. S. Lee, P. U. M. Rizki and H. Z. Lee, "A Parallel Approach for Accurate and High Performance Gridding of 3D Point Data," KIPS Transactions on Computer and Communication Systems, vol. 3, no. 8, pp. 251-260, 2014. DOI: 10.3745/KTCCS.2014.3.8.251.

[ACM Style]
Chang Seop Lee, Permata Ur Miftahur Rizki, and Hee Zin Lee. 2014. A Parallel Approach for Accurate and High Performance Gridding of 3D Point Data. KIPS Transactions on Computer and Communication Systems, 3, 8, (2014), 251-260. DOI: 10.3745/KTCCS.2014.3.8.251.