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ISPRS Int. J. Geo-Inf. 2017, 6(6), 165; doi:10.3390/ijgi6060165

A High Performance, Spatiotemporal Statistical Analysis System Based on a Spatiotemporal Cloud Platform

1
Yunnan Provincial Geomatics Centre, Kunming 650034, Yunnan, China
2
Department of Geoinformation Science, Kunming University of Science and Technology, Kunming 650504, Yunnan, China
3
Department of Geography, Eastern China Normal University, Shanghai 200062, China
4
School of Remote Sensing Information and Engineering, Wuhan University, Wuhan 430071, Hubei, China
5
Beijing Yunhe Spatiotemporal Tehnology Co. Ltd, Beijing 100080, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Ozgun Akcay and Wolfgang Kainz
Received: 9 February 2017 / Revised: 17 May 2017 / Accepted: 24 May 2017 / Published: 6 June 2017
View Full-Text   |   Download PDF [5248 KB, uploaded 6 June 2017]   |  

Abstract

With the increase in size and complexity of spatiotemporal data, traditional methods for performing statistical analysis are insufficient for meeting real-time requirements for mining information from Big Data, due to both data- and computing-intensive factors. To solve the Big Data challenges in geostatistics and to support decision-making, a high performance, spatiotemporal statistical analysis system (Geostatistics-Hadoop) is proposed in this paper. The proposed system has several features: (1) Hadoop is enhanced to handle spatial data in a native format and execute a number of parallelized spatial analysis algorithms to solve practical geospatial analysis problems; (2) the Oozie-based workflow system is utilized to ease the operation and sharing of spatial analysis services; and (3) a private cloud platform based on Eucalyptus is leveraged to provide on-the-fly and elastic computing resources. Experimental results show that Geostatistics-Hadoop efficiently conducts rapid information mining and analysis of big spatiotemporal data sets, with the support of elastic computing resources from a cloud platform. The adoption of cloud computing and the Hadoop cluster to parallelize statistical calculations significantly improves the performance of Big Data analyses. View Full-Text
Keywords: spatiotemporal cloud platform; high-performance spatiotemporal statistical analysis system; Hadoop spatiotemporal cloud platform; high-performance spatiotemporal statistical analysis system; Hadoop
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MDPI and ACS Style

Jin, B.; Song, W.; Zhao, K.; Wei, X.; Hu, F.; Jiang, Y. A High Performance, Spatiotemporal Statistical Analysis System Based on a Spatiotemporal Cloud Platform. ISPRS Int. J. Geo-Inf. 2017, 6, 165.

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