A distributed geospatial data storage and processing framework for large-scale WebGIS

With the rapid growth of geospatial data and concurrent users, the state-of-the-art WebGIS cannot support massive data storage and processing due to poor scalability of underlying centralized systems (e.g., native file systems and SDBMS). In this paper, we propose a novel distributed geospatial data...

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Bibliographic Details
Published in2012 20th International Conference on Geoinformatics pp. 1 - 7
Main Authors Yunqin Zhong, Jizhong Han, Tieying Zhang, Jinyun Fang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2012
Subjects
Artificial neural networks
cloud infrastructure
Computational modeling
data management
geospatial computation
Hadoop
spatial cloud computing
Vectors
WebGIS
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Summary:With the rapid growth of geospatial data and concurrent users, the state-of-the-art WebGIS cannot support massive data storage and processing due to poor scalability of underlying centralized systems (e.g., native file systems and SDBMS). In this paper, we propose a novel distributed geospatial data storage and processing framework for large-scale WebGIS. Our proposal contains three significant characteristics. Firstly, a scalable cloud-based architecture is designed to provide elastic storage and computation resources of shared-nothing commodity cluster for WebGIS. Secondly, we present efficient geospatial data placement and geospatial data access refinement schemes to improve I/O efficiency. Thirdly, we propose MapReduce based localized geospatial computing model for parallel processing of massive geospatial data, which improves geospatial computation performance. We have implemented a prototype named VegaCI on top of the emerging Hadoop cloud platform. Comprehensive experiments demonstrate that our proposal is efficient and applicable in practical large-scale WebGIS.
ISBN:1467311030
9781467311038
ISSN:2161-024X
DOI:10.1109/Geoinformatics.2012.6270347