Evaluation of MapReduce for Gridding LIDAR Data
The MapReduce programming model, introduced by Google, has become popular over the past few years as a mechanism for processing large amounts of data, using shared-nothing parallelism. In this paper, we investigate the use of MapReduce technology for a local gridding algorithm for the generation of...
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| creator | Krishnan, S Baru, C Crosby, C |
| description | The MapReduce programming model, introduced by Google, has become popular over the past few years as a mechanism for processing large amounts of data, using shared-nothing parallelism. In this paper, we investigate the use of MapReduce technology for a local gridding algorithm for the generation of Digital Elevation Models (DEM). The local gridding algorithm utilizes the elevation information from LIDAR (Light, Detection, and Ranging) measurements contained within a circular search area to compute the elevation of each grid cell. The method is data parallel, lending itself to implementation using the MapReduce model. Here, we compare our initial C++ implementation of the gridding algorithm to a MapReduce-based implementation, and present observations on the performance (in particular, price/performance) and the implementation complexity. We also discuss the applicability of MapReduce technologies for related applications. |
| doi_str_mv | 10.1109/CloudCom.2010.34 |
| format | Conference Proceeding |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Arrays Digital elevation models Gridding Inference algorithms Laser radar LIDAR MapReduce Merging Programming Surface topography |
| title | Evaluation of MapReduce for Gridding LIDAR Data |
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