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ISPRS Int. J. Geo-Inf. 2018, 7(7), 265; https://doi.org/10.3390/ijgi7070265

Model of Point Cloud Data Management System in Big Data Paradigm

1
Faculty of Technical Sciences, University of Novi Sad, Novi Sad 106314, Serbia
2
Faculty of Architecture, Civil Engineering and Geodesy, University of Banja Luka, Banja Luka 78000, The Republic of Srpska, Bosnia and Herzegovina
*
Author to whom correspondence should be addressed.
Received: 30 April 2018 / Revised: 26 June 2018 / Accepted: 3 July 2018 / Published: 9 July 2018
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Abstract

Modern geoinformation technologies for collecting and processing data, such as laser scanning or photogrammetry, can generate point clouds with billions of points. They provide abundant information that can be used for different types of analysis. Due to its characteristics, the point cloud is often viewed as a special type of geospatial data. In order to efficiently manage such volumes of data, techniques based on a computer cluster have to be used. The Apache Spark framework has proven to be a solution for efficient processing of large volumes of data. This paper thoroughly examines the representation of point cloud data type using Apache Spark constructs. The common operations over point clouds, range queries and k-nearest neighbors queries (kNN) are implemented using Apache Spark DataFrame Application Programming Interface (API). It enabled the design of point cloud related user defined types (UDT) and user defined functions (UDF). The structure of the point cloud for efficient storing in Big Data key-value stores was analyzed and described. The methods presented in this paper were compared to PostgreSQL RDBMS, and the results were discussed. View Full-Text
Keywords: point cloud; Geospaial Big Data; Apache Spark SQL point cloud; Geospaial Big Data; Apache Spark SQL
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Pajić, V.; Govedarica, M.; Amović, M. Model of Point Cloud Data Management System in Big Data Paradigm. ISPRS Int. J. Geo-Inf. 2018, 7, 265.

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