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Adaptive processing of Earth Observation data on Cloud infrastructures based on workflow description

机译:根据工作流描述对云基础设施上的地球观测数据进行自适应处理

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The analysis of Earth Observation data is a challenging task due to the variety, velocity and volume of incoming data from various sources. As storing all the raw data is almost impossible, knowledge extraction would be a recommended approach in reducing data size without losing valuable information. For describing the complex processing required to extract knowledge we propose a flexible solution based on workflows and an adaptive execution platform. The main focus of this paper is the Executor component that is oriented on scalability and isolation from the virtual resources management that can be dedicated to a specific cloud infrastructure.
机译:由于来自各种来源的输入数据的多样性,速度和数量,对地球观测数据的分析是一项艰巨的任务。由于几乎不可能存储所有原始数据,因此在减少数据大小而又不丢失有价值信息的情况下,建议采用知识提取的方法。为了描述提取知识所需的复杂处理,我们提出了一种基于工作流和自适应执行平台的灵活解决方案。本文的主要重点是Executor组件,该组件面向可伸缩性和与虚拟资源管理的隔离,后者可专用于特定的云基础架构。

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