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A Map-Reduce-enabled SOLAP cube for large-scale remotely sensed data aggregation

Li, Jiyuan, Meng, Lingkui, Wang, Frank Z., Zhang, Wen, Cai, Yang (2014) A Map-Reduce-enabled SOLAP cube for large-scale remotely sensed data aggregation. Computers & Geosciences, 70 . pp. 110-119. ISSN 0098-3004. (doi:10.1016/j.cageo.2014.05.008) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:69682)

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Spatial On-Line Analytical Processing (SOLAP) is a powerful decision support systems tool for exploring the multidimensional perspective of spatial data. In recent years, remotely sensed data have been integrated into SOLAP cubes, and this improvement has advantages in spatio-temporal analysis for environment monitoring. However, the performance of aggregations in SOLAP still faces a considerable challenge from the large-scale dataset generated by Earth observation. From the perspective of data parallelism, a tile-based SOLAP cube model, the so-called Tile Cube, is presented in this paper. The novel model implements Roll-Up/Drill-Across operations in the SOLAP environment based on Map-Reduce, a popular data-intensive computing paradigm, and improves the throughput and scalability of raster aggregation. Therefore, the long time-series, wide-range and multi-view analysis of remotely sensed data can be processed in a short time. The Tile Cube prototype was built on Hadoop/Hbase, and drought monitoring is used as an example to illustrate the aggregations in the model. The performance testing indicated the model can be scaled along with both the data growth and node growth. It is applicable and natural to integrate the SOLAP cube with Map-Reduce. Factors that influence the performance are also discussed, and the balance of them will be considered in future works to make full use of data locality for model optimisation.

Item Type: Article
DOI/Identification number: 10.1016/j.cageo.2014.05.008
Uncontrolled keywords: SOLAP, Spatio-temporal cube, Data-intensive computing, Cyber GIS, Environment monitoring
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Frank Wang
Date Deposited: 19 Oct 2018 09:56 UTC
Last Modified: 17 Aug 2022 11:02 UTC
Resource URI: (The current URI for this page, for reference purposes)
Wang, Frank Z.:

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