Spatial scene similarity assessment on Hadoop

Authors:
Danhuai Guo;Kaichao Wu;Jianhui Li;Yuwei Wang
Affiliations:
Chinese Academy of Sciences, Zhongguanchun, Beijing, China;Chinese Academy of Sciences, Zhongguanchun, Beijing, China;Chinese Academy of Sciences, Zhongguanchun, Beijing, China;Chinese Academy of Sciences, Zhongguanchun, Beijing, China and Graduate University of the Chinese Academy of Sciences, Beijing, China
Venue:
Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems
Year:
2010

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Abstract

Spatial Scene Similarity Assessment (SSSA) is an essential problem in spatial analysis, spatial query, and map generalization, etc. In SSSA, spatial scene similarity needs to be compared between query spatial scene and each candidate spatial scene. The computational complexity of spatial scene comparison often cannot be resolved by sequential computing model. In this paper, we analyze the computational complexity of SSSA and develop a parallel processing method and associated algorithms for SSSA based on Hadoop. The COOT (Cell Object Overlay Times) is proposed as a data locality strategy. The experiment results demonstrate that MapReduce on Hadoop significantly improve SSSA in computing performance and data processing capability.