Model of Point Cloud Data Management System in Big Data Paradigm

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...

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Bibliographic Details
Published in:ISPRS international journal of geo-information 2018-07, Vol.7 (7), p.265
Main Authors: Pajić, Vladimir, Govedarica, Miro, Amović, Mladen
Format: Article
Language:English
Subjects:
Apache Spark SQL
Application programming interface
Batch processing
Big Data
Classification
Cloud computing
Data
Data management
Data processing
Data storage
Distributed processing
Frameworks
Geospaial Big Data
Lasers
Library collections
Photogrammetry
point cloud
Queries
Spatial data
Three dimensional models
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Summary: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.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi7070265