A MapReduce approach to Gi*(d) spatial statistic | |
Liu Y(刘岩) ; Wu KC(吴开超) ; Wang SW(王少文) | |
2010 | |
会议名称 | CODATA2010 国际会议 |
会议日期 | 2010 |
中文摘要 | Managing and analyzing massive spatial datasets as supported by GIS and spatial analysis is becoming crucial to geospatial problem-solving and decision-making. MapReduce provides a data-centric computational model through which highly scalable spatial analysis computation can be achieved. However, it is challenging to leverage multi-dimensional spatial characteristics on the horizontally-partitioned and transparently managed MapReduce data system for improving the computational performance of spatial analysis. This paper tackles this challenge through the development of MapReduce-based computation of Gi*(d) -- a spatial statistic for detecting local clustering. Without exploiting spatial characteristics, Gi*(d) computation for a particular location requires pair-wise distance calculation for all points of a given dataset. A spatial locality-based storage and indexing strategy is developed to associate spatial locality with storage locality on MapReduce platform. Based on a spatial indexing method, unnecessary map tasks can be eliminated for a MapReduce job, thus significantly improving the overall computation performance. To leverage underlying parallelism on storage nodes, an application-level load balancing mechanism is developed to produce even loads among map tasks based on adaptive spatial domain decomposition. Experiments show the effectiveness of the developed storage and indexing strategy with different distance parameter settings. Significant reduction on execution time for all-point computation is observed through the use of the application-level load balancing mechanism. |
收录类别 | EI收录 |
会议录 | ACM HPDGIS 2010
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学科主题 | 信息科学与系统科学 |
语种 | 中文 |
内容类型 | 会议论文 |
源URL | [http://ircnic.ac.cn/handle/311056/1792] ![]() |
专题 | 计算机网络信息中心_中国科学院计算机网络信息中心(2012年前)_会议论文 |
推荐引用方式 GB/T 7714 | Liu Y,Wu KC,Wang SW. A MapReduce approach to Gi*(d) spatial statistic[C]. 见:CODATA2010 国际会议. 2010. |
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