Geoprocessing in Cloud Computing platforms – a comparative analysis

Yue, Peng; Zhou, Hongxiu; Gong, Jianya; Hu, Lei

doi:10.1080/17538947.2012.748847

Cited by 64 publications

(21 citation statements)

References 12 publications

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“…Increasingly, distributed systems such as Cloud Computing (Cui, Wu, and Zhang 2010;Liu et al 2009;Yang et al 2011aYang et al , 2011bYang, Xu, and Nebert 2013;Liu et al 2013;Huang et al 2013;Wen et al 2013;Kim and Tsou 2013;Yue et al 2013), cyber-GIS (Wang 2010;Wang et al 2013), or spatial cyber-infrastructure (Wright and Wang 2011) have been suggested as a solution to overcome the scalability and performance problems of the Web-based GIS systems. Cloud computing is a recent paradigm developed to search, access, and utilize large volumes of geospatial data for many geospatial science applications.…”

Section: Other Query Processing Strategiesmentioning

confidence: 99%

A parallel approach for improving Geo-SPARQL query performance

Zhao

Zhang

Anselin

et al. 2014

International Journal of Digital Earth

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Geospatial Semantic Web promises better retrieval geospatial information for Digital Earth systems by explicitly representing the semantics of data through ontologies. It also promotes sharing and reuse of geospatial data by encoding it in Semantic Web languages, such as RDF, to form geospatial knowledge base. For many applications, rapid retrieval of spatial data from the knowledge base is critical. However, spatial data retrieval using the standard Semantic Web query language -Geo-SPARQL -can be very inefficient because the data in the knowledge base are no longer indexed to support efficient spatial queries. While recent research has been devoted to improving query performance on general knowledge base, it is still challenging to support efficient query of the spatial data with complex topological relationships. This research introduces a query strategy to improve the query performance of geospatial knowledge base by creating spatial indexing on-the-fly to prune the search space for spatial queries and by parallelizing the spatial join computations within the queries. We focus on improving the performance of Geo-SPARQL queries on knowledge bases encoded in RDF. Our initial experiments show that the proposed strategy can greatly reduce the runtime costs of Geo-SPARQL query through on-the-fly spatial indexing and parallel execution.

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Section: Other Query Processing Strategiesmentioning

confidence: 99%

A parallel approach for improving Geo-SPARQL query performance

Zhao

Zhang

Anselin

et al. 2014

International Journal of Digital Earth

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“…It provides scalable storage to manage and organize continuously increasing geospatial data, elastically changing its processing capability to data parallel computing with an effective paradigm to integrate many heterogeneous geocomputing resources [12,13]. Thus far, the advancements of cloud-enabled geocomputing involve dealing with the intensities of data, computation, concurrent access, and spatiotemporal patterns [14]. To date, the integration of advanced big data processing methods and practical geocomputing scenarios is still in its infancy and is a very active research field.…”

Section: Cloud-based Big Geo-data Processingmentioning

confidence: 99%

An Effective NoSQL-Based Vector Map Tile Management Approach

Wan

Huang

Xia

2016

IJGI

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Within a digital map service environment, the rapid growth of Spatial Big-Data is driving new requirements for effective mechanisms for massive online vector map tile processing. The emergence of Not Only SQL (NoSQL) databases has resulted in a new data storage and management model for scalable spatial data deployments and fast tracking. They better suit the scenario of high-volume, low-latency network map services than traditional standalone high-performance computer (HPC) or relational databases. In this paper, we propose a flexible storage framework that provides feasible methods for tiled map data parallel clipping and retrieval operations within a distributed NoSQL database environment. We illustrate the parallel vector tile generation and querying algorithms with the MapReduce programming model. Three different processing approaches, including local caching, distributed file storage, and the NoSQL-based method, are compared by analyzing the concurrent load and calculation time. An online geological vector tile map service prototype was developed to embed our processing framework in the China Geological Survey Information Grid. Experimental results show that our NoSQL-based parallel tile management framework can support applications that process huge volumes of vector tile data and improve performance of the tiled map service.

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“…In the catalog service, a standard publish-find-bind service framework are implemented which has been defined by OGC web service architecture. Data server deals with the WMS, WCS and WFS [11].…”

Section: Introductionmentioning

confidence: 99%