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

机译:基于工作流描述的云基础架构地球观测数据的自适应处理

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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传入数据的种类,速度和体积,对地球观测数据的分析是一个具有挑战性的任务。由于存储所有原始数据几乎是不可能的,知识提取将是推荐的方法,即在不失有价值的信息的情况下减少数据大小。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程和自适应执行平台的灵活解决方案。本文的主要焦点是索取组件,该组件以可伸缩性和隔离从虚拟资源管理中专用于特定的云基础架构。

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