面向领域的网格工作流科学合成方法

Jun Qin, T. Fahringer
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引用次数: 23

摘要

现有的基于知识的网格工作流语言和组合工具需要领域科学家的专业知识来实现工作流及其组件(活动)管理过程的自动化。到目前为止,语义工作流规范和管理还没有从一个通用的和集成的角度来解决。本文提出了一种新的面向领域的方法,该方法将数据含义和数据表示、活动功能(工作流活动的语义描述)和活动类型(工作流活动的语法描述)之间的关注点分离。这些分离是作为抽象网格工作流语言(AGWL)的一部分实现的,AGWL支持在高层次(语义)抽象上开发网格工作流。相应的工作流组合工具通过以下方式简化了网格工作流组合:(1)使用户能够在数据意义和活动功能的层面上组合网格工作流,从而屏蔽网格、任何特定实现技术(例如Web或网格服务)和任何特定数据表示的复杂性;(2)半自动数据流组合;(3)自动数据转换。我们已经将我们的方法作为ASKALON网格应用程序开发和计算环境的一部分来实现。我们通过将其应用于真实世界的气象工作流应用程序来证明我们的方法的有效性,并报告了一些初步结果。通过为其他科学领域开发相应的本体,我们的方法也可以适用于这些领域。
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A novel domain oriented approach for scientific Grid workflow composition
Existing knowledge based grid workflow languages and composition tools require sophisticated expertise of domain scientists in order to automate the process of managing workflows and its components (activities). So far semantic workflow specification and management has not been addressed from a general and integrated perspective. This paper presents a novel domain oriented approach which features separations of concerns between data meaning and data representation and between activity function (semantic description of workflow activities) and activity type (syntactic description of workflow activities). These separations are implemented as part of abstract grid workflow language (AGWL) which supports the development of grid workflows at a high level (semantic) of abstraction. The corresponding workflow composition tool simplifies grid workflow composition by (i) enabling users to compose grid workflows at the level of data meaning and activity function that shields the complexity of the grid, any specific implementation technology (e.g. Web or Grid service) and any specific data representation, (ii) semi-automatic data flow composition, and (iii) automatic data conversions. We have implemented our approach as part of the ASKALON grid application development and computing environment. We demonstrate the effectiveness of our approach by applying it to a real world meteorology workflow application and report some preliminary results. Our approach can also be adapted to other scientific domains by developing the corresponding ontologies for those domains.
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