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INFORMATION
GDI-Grid – Geospatial infrastructure Grid
using the grid for Flood Risk Assessment
Spatial data has become a major asset for science
and industry in the past decades. With
the advent of widespread mainstream applications
for spatial data infrastructures, demand
for spatial information is on a sharp rise. Technological
advance in data acquisition provides
Motivation
Using established standards and middleware solution, the
Grid offers highly-scalable, secure and comprehensive storage
and computing capabilities by interconnecting distributed
resources. It seems natural to connect SDIs and the
Grid and thus bring resource consumers and resource providers
together. The main goal of “GDI Grid” is to close
this gap between spatial data infrastructures (SDI) based
on OGC web services and the Grid. For this, it is necessary
to combine the current base technologies of SDI and
Grid middleware to enable seamless processing of spatial
data in Grids.
Challenges
Current SDI applications and the Globus Grid middleware
both make heavy use of Web Service technologies. While
Globus employs the Web Service Resource Framework
(WSRF) standard and deploys additional security mechanism,
conventional SDI applications normally adhere to one
of the numerous OGC Web Service (OWS) standards.
Coupling these standards and finding common security
mechanisms is one of the major challenges in GDI-Grid.
The project is working on an extension to the OGC standards,
which implements an authentication mechanism
for Web Service clients. With this solution, Grid-compatible
authentication is possible for OGC Web Services, while
backwards compatibility is preserved.
FORSCHUNGSZENTRUM L3S L3S RESEARCH CENTER
researchers and companies with much more
detailed raw data, but also greatly increases
storage and processing demands. vast amounts
of information from the spatial domain must
be stored and processed into more meaningful
representations.
The gDI-grid project strives towards interoperation
between spatial data infrastructure, geo
information systems and grid computing by creating
an interface for high-performance computing
of geospatial data. using the infrastructure
provided by the D-grid and grid-enabled workflows,
geospatial simulation and data analysis will
be dramatically accelerated.
The implementation will be validated using the following
three example scenarios:
• spatial simulation of flood disasters
• noise dispersion simulation
• real-time route optimization for disaster management
In current development, a focus is set on a scenario for flood
simulation. In response to the frequent flooding hazards
in Europe, the European Union has released a set of laws
requiring that the probability and foreseen consequences
of flooding to be determined. By the year 2013, all states
of the EU have to provide flood risk maps for all river basins
and stretches of coastal line. For this purpose, flow models
such as two-dimensional hydrodynamic simulations, are
applied to estimate the flood inundation areas. With storage
resources and processing power, the Grid will help in
realizing these simulations.