A grid portal for solving geoscience problems using distributed knowledge discovery services
A grid portal for solving geoscience problems using distributed knowledge discovery services
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DOI：
10.1016/j.future.2009.08.002
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
G. Folino;Agostino Forestiero;Giuseppe Papuzzo;G. Spezzano
中科院分区：
文献类型：
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作者：
G. Folino;Agostino Forestiero;Giuseppe Papuzzo;G. Spezzano
本文描述了我们的研究工作，通过将工作流技术与数据挖掘资源和称为MOSÈ的独特工作环境中的门户框架集成在一起，采用网格技术来实现地球科学应用程序的开发。使用MOSÈ，用户可以轻松地编写和执行地理工作流，以分析和管理自然灾害，如滑坡、地震、洪水、野火等。MOSÈ的设计既适用于在发生紧急情况时执行反应战略，也适用于预防灾害。它利用了web/网格服务技术提供的对松散耦合软件组件的标准化资源访问和工作流支持。工作流与数据挖掘服务的集成显著改善了数据分析。地理空间数据管理和挖掘是现代地球科学研究的重要领域。地理空间信息挖掘面临的一个重要挑战是数据的分布式特性。MOSÈ提供基于WEKA数据挖掘库和用于空间数据分析的新型分布式数据挖掘算法的知识发现服务。以分布式知识发现服务为例，提出了一种基于P2P的分布式空间聚类算法。在萨尔诺地区附近坎帕尼亚地区滑坡危险区分析的实际案例应用显示了使用该门户的优势。
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 response strategies 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.