IN51C-1871
GSKY: A distributed geospatial data server Pablo Rozas Larraondo*, Joseph Antony, Sean Pringle, Chris Allen, Jon Smillie, Ben Evans
Is designed to:
gsky Distributed Geospatial Data Server
• Scale: Distributed solution that scales in data volume and concurrent users. • Work with raw data: Scientific formats such as NetCDF, HDF or geoTIFF. • Compute on-the-fly: No caching, no precomputed products. On-demand transformation and aggregation. • Standards: Implements OGC standards.
The National Computational Infrastructure (NCI), hosted at the Australian National University (ANU), has over 10 Petabytes of nationally significant research data collections. These collections, which comprise a variety of environmental data, imagery and modelling, are now made available via a highly distributed geospatial data server, called GSKY (pronounced [jee-skee]).
Real-time Visualisation and Analytics Framework
Typical data wrangling problems such as handling different file formats and data types, or harmonising coordinate projections and spatio-temporal resolutions, can now be handled transparently by GSKY. It is agnostic of any underlying file format or data organisation. This is achieved by decoupling the data ingestion and indexing process as an independent service. This service crawls collections either locally or remotely by extracting, storing and indexing all spatio-temporal metadata associated with each individual record. GSKY has functionality for specifying how ingested data should be aggregated, transformed and presented. It presents an OGC standards-compliant interface, allowing ready accessibility for users of the data via Web Map Services (WMS) and Web Processing Services (WPS).
A Distributed and Scalable Design
GSKY supports on demand processing of large geospatial data products such as satellite earth observation data as well as numerical simulation output, allowing interactive data exploration using flexible user-defined functions. It dynamically and e f fi c i e n t l y d i s t r i b u t e s t h e r e q u i s i t e computations among cloud nodes providing a scalable analysis framework.
*Contact:
[email protected]
ERA-Interim temperature combined with MODIS NDVI.
Himawari 8 infrared band in 3D view.
Polygon drill WPS comparing the evolution of vegetation for two areas.
MODIS Fractional Cover Index.
National Computational Infrastructure Australian National University
nci.org.au
