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Lessons Learnt from the Development of GIS Application on Azure Cloud Platform
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1006/jmbi.1990.9999openalex-snapshot
Basic Local Alignment Search Tool
inferredcites →https://doi.org/10.1007/978-3-642-33299-9_1openalex-snapshot
The Fourth Paradigm – Data-Intensive Scientific Discovery
inferredcites →https://doi.org/10.1016/j.future.2008.12.001openalex-snapshot
Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility
inferredcites →https://doi.org/10.1109/cloud.2011.21openalex-snapshot
A Scalable Communication Runtime for Clouds
inferredcites →https://doi.org/10.1109/cloudcom.2010.104openalex-snapshot
Performing Large Science Experiments on Azure: Pitfalls and Solutions
inferredcites →https://doi.org/10.1109/gce.2008.4738445openalex-snapshot
Cloud Computing and Grid Computing 360-Degree Compared
inferredcites →https://doi.org/10.1109/ipdps.2010.5470418openalex-snapshot
eScience in the cloud: A MODIS satellite data reprojection and reduction pipeline in the Windows Azure platform
inferredcites →https://doi.org/10.1109/ipdpsw.2012.128openalex-snapshot
AzureBench: Benchmarking the Storage Services of the Azure Cloud Platform
inferredcites →https://doi.org/10.1109/ipdpsw.2012.180openalex-snapshot
A System for GIS Polygonal Overlay Computation on Linux Cluster - An Experience and Performance Report
inferredcites →https://doi.org/10.1109/mcse.2010.20openalex-snapshot
Scientific Computing in the Cloud
inferredcites →https://doi.org/10.1109/mcse.2010.70openalex-snapshot
Scientific Computing in the Cloud
inferredcites →https://doi.org/10.1145/1851476.1851539openalex-snapshot
Migrating a (large) science database to the cloud
inferredcites →https://doi.org/10.1145/1851476.1851540openalex-snapshot
Reshaping text data for efficient processing on Amazon EC2
inferredcites →https://doi.org/10.1145/1851476.1851542openalex-snapshot
A perspective on scientific cloud computing
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-319-17885-1_1574openalex-snapshot
Polygonal Overlay Computation on Cloud, Hadoop, and MPI
inferred← citeshttps://doi.org/10.1007/s10586-015-0428-xopenalex-snapshot
Improving the performance of GIS polygon overlay computation with MapReduce for spatial big data processing
inferred← citeshttps://doi.org/10.1016/bs.adcom.2021.09.006openalex-snapshot
Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications
inferred← citeshttps://doi.org/10.1016/j.cag.2015.02.005openalex-snapshot
A modular software architecture for processing of big geospatial data in the cloud
inferred← citeshttps://doi.org/10.1016/j.cageo.2017.11.011openalex-snapshot
Arc4nix: A cross-platform geospatial analytical library for cluster and cloud computing
inferred← citeshttps://doi.org/10.1080/17538947.2021.1889056openalex-snapshot
Implementation of serverless cloud GIS platform for land valuation
inferred← citeshttps://doi.org/10.1109/aero.2013.6497163openalex-snapshot
Integrating remotely sensed and ground observations for modeling, analysis, and decision support
inferred← citeshttps://doi.org/10.1109/dsd.2017.49openalex-snapshot
A Scalable Cloud Computing Infrastructure for Geospatial Data Analytics for Change Detection
inferred← citeshttps://doi.org/10.1109/icsa.2017.42openalex-snapshot
Towards a Reference Architecture for Cloud-Based Plant Genotyping and Phenotyping Analysis Frameworks
inferred← citeshttps://doi.org/10.1109/ipdpsw.2013.174openalex-snapshot
Efficient Parallel and Distributed Algorithms for GIS Polygonal Overlay Processing
inferred← citeshttps://doi.org/10.1109/ipdpsw.2013.254openalex-snapshot
MapReduce Algorithms for GIS Polygonal Overlay Processing
inferred← citeshttps://doi.org/10.1109/ipdpsw.2013.261openalex-snapshot
AzureBOT: A Framework for Bag-of-Tasks Applications on the Azure Cloud Platform
inferred← citeshttps://doi.org/10.1109/sc.companion.2012.315openalex-snapshot
Crayons: An Azure Cloud Based Parallel System for GIS Overlay Operations
inferred← citeshttps://doi.org/10.1109/sc.companion.2012.316openalex-snapshot
Poster: Crayons: An Azure Cloud Based Parallel System for GIS Overlay Operations
inferred← citeshttps://doi.org/10.1109/tbdata.2018.2871062openalex-snapshot
Parallel Grid-Based Colocation Mining Algorithms on GPUs for Big Spatial Event Data
inferred← citeshttps://doi.org/10.1109/telsiks46999.2019.9002327openalex-snapshot
A Cloud-Based Real-Time Weather Forecasting Application
inferred← citeshttps://doi.org/10.1145/2534921.2535837openalex-snapshot
GPGPU-accelerated interesting interval discovery and other computations on GeoSpatial datasets
inferred← citeshttps://doi.org/10.1145/2766196.2766200openalex-snapshot
A vision for GPU-accelerated parallel computation on geo-spatial datasets
inferred← citeshttps://doi.org/10.1177/09722629211015600openalex-snapshot
Scope of Cloud Computing in Business: A Compendious and Methodical Analysis of Trends in Publications and Patents
inferred← citeshttps://doi.org/10.3233/978-1-61499-322-3-81openalex-snapshot
Using Clouds for Technical Computing
inferred← citeshttps://doi.org/10.48550/arxiv.1501.01323openalex-snapshot
Recent Developments in Cloud Based Systems: State of Art
inferred← citeshttps://doi.org/10.48550/arxiv.1911.11181openalex-snapshot
Bivariate, Cluster and Suitability Analysis of NoSQL Solutions for Different Application Areas
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