SDCI Data: New Toolkit for Provenance Collection, Publishing, and Experience Reuse

SDCI 数据：用于来源收集、发布和体验重用的新工具包

• 批准号: 0721674
• 负责人: Beth Plale
• 金额: $ 43.3万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0721674&HistoricalAwards=false
• 关键词: SDCI; Data; New; Toolkit; Provenance

OCI - SDCI Data: New Toolkit for Provenance Collection, Publishing, and Experience ReuseAs research digital data collections created through computational science experiments proliferate, it becomes increasingly important to address the provenance issues of the data validity and quality: to record and manage information about where each data object originated, the processes applied to the data products, and by whom. The first outcome of this work is a provenance collection and experience reuse tool that makes minimal assumptions about the software environment and imposes minimal burden on the application writer. It stores and produces results in a form suitable for publication to a digital library. The provenance collection system is a standalone system that imposes a minimal burden on users to integrate it into their application framework and it exhibits good performance. A second outcome of the work is a recommender system for workflow completion that employs case-based reasoning to provenance collections in order to make suggestions to users about future workflow-driven investigations. The workflow completion tool builds on computer models of case-based reasoning to develop a support system that leverages the collective experience of the users of the provenance system to provide suggestions. As a key part of effectively evaluating aspects of the tool, this work builds a gigabyte benchmark database of real and synthetic provenance information. Real workflows are sought from the community, with synthetic extensions to the data set for completeness for purposes of testing. The software and database are available to the research community.

随着通过计算科学实验创建的研究数字数据收集的激增，解决数据有效性和质量的来源问题变得越来越重要：记录和管理有关每个数据对象的来源、应用于数据产品的过程以及由谁发起的信息。这项工作的第一个结果是一个来源收集和经验重用工具，它对软件环境做了最小的假设，并对应用程序编写人员施加了最小的负担。它以适合发布到数字图书馆的形式存储和生成结果。来源收集系统是一个独立的系统，用户将其集成到应用程序框架中的负担最小，并且具有良好的性能。该工作的第二个成果是工作流完成的推荐系统，该系统对来源集合采用基于案例的推理，以便向用户提供有关未来工作流驱动调查的建议。工作流完成工具建立在基于案例推理的计算机模型上，以开发一个支持系统，该系统利用来源系统用户的集体经验来提供建议。作为有效评估工具各方面的关键部分，本工作构建了一个千兆字节的真实和合成来源信息基准数据库。真实的工作流是从社区中寻找的，并对数据集进行了综合扩展，以确保测试的完整性。该软件和数据库可供研究界使用。