Geographical information system parallelization for spatial big data processing: a review
文献类型：期刊论文
| 作者 | Zhao, Lingjun1; Chen, Lajiao1; Ranjan, Rajiv1; Choo, Kim-Kwang Raymond1; He, Jijun1 |
| 刊名 | Cluster Computing
 |
| 出版日期 | 2016 |
| 卷号 | 19期号:1页码:139-152 |
| 关键词 | BIOMASS BURNING EMISSIONS IVORY-COAST FIRE COUNTS CARBON MODIS SAVANNA CLIMATE VARIABILITY EURASIA FLUXES |
| 通讯作者 | Chen, Lajiao (chenlj@radi.ac.cn) |
| 英文摘要 | With the increasing interest in large-scale, high-resolution and real-time geographic information system (GIS) applications and spatial big data processing, traditional GIS is not efficient enough to handle the required loads due to limited computational capabilities.Various attempts have been made to adopt high performance computation techniques from different applications, such as designs of advanced architectures, strategies of data partition and direct parallelization method of spatial analysis algorithm, to address such challenges. This paper surveys the current state of parallel GIS with respect to parallel GIS architectures, parallel processing strategies, and relevant topics. We present the general evolution of the GIS architecture which includes main two parallel GIS architectures based on high performance computing cluster and Hadoop cluster. Then we summarize the current spatial data partition strategies, key methods to realize parallel GIS in the view of data decomposition and progress of the special parallel GIS algorithms. We use the parallel processing of GRASS as a case study. We also identify key problems and future potential research directions of parallel GIS. © 2015, Springer Science+Business Media New York. |
| 学科主题 | Computer Science |
| 类目[WOS] | Computer Science, Information Systems ; Computer Science, Theory & Methods |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20154801633055 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39338]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China 2. University of Chinese Academy of Sciences, Beijing, China 3. University of Newcastle, Newcastle upon Tyne, United Kingdom 4. University of South Australia, Adelaide, Australia 5. Capital Normal University, Beijing, China |
| 推荐引用方式 GB/T 7714 | Zhao, Lingjun,Chen, Lajiao,Ranjan, Rajiv,et al. Geographical information system parallelization for spatial big data processing: a review[J]. Cluster Computing,2016,19(1):139-152. |
| APA | Zhao, Lingjun,Chen, Lajiao,Ranjan, Rajiv,Choo, Kim-Kwang Raymond,&He, Jijun.(2016).Geographical information system parallelization for spatial big data processing: a review.Cluster Computing,19(1),139-152. |
| MLA | Zhao, Lingjun,et al."Geographical information system parallelization for spatial big data processing: a review".Cluster Computing 19.1(2016):139-152. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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