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

Abstract

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.