Spatio-temporal types and data analysis in Big Data paradigm

Authors

  • Mladen Amović University of Banja Luka, Faculty of Architecture, Civil Engineering and Geodesy, Bosnia and Herzegovina, mladen.amovic@aggf.unibl.org
  • Miro Govedarica University of Novi Sad, Faculty of Technical Sciences, Serbia, miro@uns.ac.rs https://orcid.org/0000-0003-1698-0800
  • Vladimir Pajić University of Novi Sad, Faculty of Technical Sciences, Serbia, pajicv@uns.ac.rs
  • Slavko Vasiljević University of Banja Luka, Faculty of Architecture, Civil Engineering and Geodesy, Bosnia and Herzegovina, slavko.vasiljevic@aggf.unibl.org

DOI:

https://doi.org/10.7251/AGGPLUS1503066A

Keywords:

Geospatial data, Big Data, Apache Spark SQL, Data Frames, OGC

Abstract

The model for managing large volumes of spatio-temporal data is implemented in Apache Spark platform for storing and processing large sets of data. The algorithms for processing spatio-temporal data are defined according to the rules of Spark SQL programming model and relational operations on dataframes (specialized system of data frames) using domain specific language (domain-specific-language → DSL). With the introduction of spatio-temporal data types, a standardized approach to a Big Data paradigm is enabled.

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Published

2015-12-30

How to Cite

[1]
M. Amović, M. Govedarica, V. Pajić, and S. Vasiljević, “Spatio-temporal types and data analysis in Big Data paradigm”, AGG+, vol. 3, no. 1, pp. 066-075, Dec. 2015.

Issue

Vol. 3 No. 1 (2015): AGG+ Journal for Architecture, Civil Engineering, Geodesy and Related Scientific Fields

Section

Geodesy