Developing the Raster Big Data Benchmark: A Comparison of Raster Analysis on Big Data Platforms
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 pr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/9/11/690 |
| _version_ | 1827701927843987456 |
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| author | David Haynes Philip Mitchell Eric Shook |
| author_facet | David Haynes Philip Mitchell Eric Shook |
| author_sort | David Haynes |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:45:08Z |
| format | Article |
| id | doaj.art-c161b11650e543c18de53f266bf462b5 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T14:45:08Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-c161b11650e543c18de53f266bf462b52023-11-20T21:30:45ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-11-0191169010.3390/ijgi9110690Developing the Raster Big Data Benchmark: A Comparison of Raster Analysis on Big Data PlatformsDavid Haynes0Philip Mitchell1Eric Shook2Institute for Health Informatics, University of Minnesota, Minneapolis, MN 55455, USAAli I. Al-Naimi Petroleum Engineering Research Center, King Abdullah University of Science and Technology, Thuwal 23955, Saudi ArabiaGeography Environment and Society, University of Minnesota, Minneapolis, MN 55455, USATechnologies 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.https://www.mdpi.com/2220-9964/9/11/690geospatialcomputationspatial benchmarkcybergis |
| spellingShingle | David Haynes Philip Mitchell Eric Shook Developing the Raster Big Data Benchmark: A Comparison of Raster Analysis on Big Data Platforms ISPRS International Journal of Geo-Information geospatial computation spatial benchmark cybergis |
| title | Developing the Raster Big Data Benchmark: A Comparison of Raster Analysis on Big Data Platforms |
| title_full | Developing the Raster Big Data Benchmark: A Comparison of Raster Analysis on Big Data Platforms |
| title_fullStr | Developing the Raster Big Data Benchmark: A Comparison of Raster Analysis on Big Data Platforms |
| title_full_unstemmed | Developing the Raster Big Data Benchmark: A Comparison of Raster Analysis on Big Data Platforms |
| title_short | Developing the Raster Big Data Benchmark: A Comparison of Raster Analysis on Big Data Platforms |
| title_sort | developing the raster big data benchmark a comparison of raster analysis on big data platforms |
| topic | geospatial computation spatial benchmark cybergis |
| url | https://www.mdpi.com/2220-9964/9/11/690 |
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