TY - JOUR
T1 - Developing the Raster Big Data Benchmark: A Comparison of Raster Analysis on Big Data Platforms
AU - Haynes, David
AU - Mitchell, Philip M.
AU - Shook, Eric
N1 - KAUST Repository Item: Exported on 2020-11-25
Acknowledgements: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number T32CA163184 (Michele Allen, MD, MS; PI). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
PY - 2020/11/19
Y1 - 2020/11/19
N2 - Technologies around the world produce and interact with geospatial data instantaneously, from mobile web applications to satellite imagery that is collected and processed across the globe daily. Big raster data allow researchers to integrate and uncover new knowledge about geospatial patterns and processes. However, we are at a critical moment, as we have an ever-growing number of big data platforms that are being co-opted to support spatial analysis. A gap in the literature is the lack of a robust assessment comparing the efficiency of raster data analysis on big data platforms. This research begins to address this issue by establishing a raster data benchmark that employs freely accessible datasets to provide a comprehensive performance evaluation and comparison of raster operations on big data platforms. The benchmark is critical for evaluating the performance of spatial operations on big data platforms. The benchmarking datasets and operations are applied to three big data platforms. We report computing times and performance bottlenecks so that GIScientists can make informed choices regarding the performance of each platform. Each platform is evaluated for five raster operations: pixel count, reclassification, raster add, focal averaging, and zonal statistics using three raster different datasets.
AB - Technologies around the world produce and interact with geospatial data instantaneously, from mobile web applications to satellite imagery that is collected and processed across the globe daily. Big raster data allow researchers to integrate and uncover new knowledge about geospatial patterns and processes. However, we are at a critical moment, as we have an ever-growing number of big data platforms that are being co-opted to support spatial analysis. A gap in the literature is the lack of a robust assessment comparing the efficiency of raster data analysis on big data platforms. This research begins to address this issue by establishing a raster data benchmark that employs freely accessible datasets to provide a comprehensive performance evaluation and comparison of raster operations on big data platforms. The benchmark is critical for evaluating the performance of spatial operations on big data platforms. The benchmarking datasets and operations are applied to three big data platforms. We report computing times and performance bottlenecks so that GIScientists can make informed choices regarding the performance of each platform. Each platform is evaluated for five raster operations: pixel count, reclassification, raster add, focal averaging, and zonal statistics using three raster different datasets.
UR - http://hdl.handle.net/10754/666094
UR - https://www.mdpi.com/2220-9964/9/11/690
U2 - 10.3390/ijgi9110690
DO - 10.3390/ijgi9110690
M3 - Article
SN - 2220-9964
VL - 9
SP - 690
JO - ISPRS International Journal of Geo-Information
JF - ISPRS International Journal of Geo-Information
IS - 11
ER -