A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit <a rel="external noopener" href="http://staff.icar.cnr.it/folino/papers/fgcs09.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
A grid portal for solving geoscience problems using distributed knowledge discovery services
<span title="">2010</span>
<i title="Elsevier BV">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/idqeix2ukbd5fbgjjdkjmaj3ka" style="color: black;">Future generations computer systems</a>
</i>
Preserved Fulltext
This paper describes our research effort to employ Grid technologies to enable the development of geoscience applications by integrating workflow technologies with data mining resources and a portal framework in unique work environment called MOSÈ. Using MOSÈ, a user can easily compose and execute geo-workflows for analyzing and managing natural disasters such as landslides, earthquakes, floods, wildfires, etc.. MOSÈ is designed to be applicable both for the implementation of responses
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.future.2009.08.002">doi:10.1016/j.future.2009.08.002</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3xgmt7f4lnaufikhbztxch57qq">fatcat:3xgmt7f4lnaufikhbztxch57qq</a>
</span>
more »
... s when emergencies occur and for disaster prevention. It takes advantage of the standardized resource access and workflow support for loosely coupled software components provided by web/grid services technologies. The integration of workflows with data mining services significantly improves data analysis. Geospatial data management and mining are critical areas of modern-day geosciences research. An important challenge for geospatial information mining is the distributed nature of the data. MOSÈ provides knowledge discovery services based on the WEKA data mining library and novel distributed data mining algorithms for spatial data analysis. A P2P bio-inspired algorithm for distributed spatial clustering as an example of distributed knowledge discovery service for intensive data analysis is presented. A real case application for the analysis of landslide hazard areas in the Campania Region near the Sarno area shows the advantages of using the portal.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170705092215/http://staff.icar.cnr.it/folino/papers/fgcs09.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/24/a4/24a431c5e0aa7d1c1d523483594316e4c0a9bc88.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.future.2009.08.002">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="external alternate icon"></i>
elsevier.com
</button>
</a>