III: Medium: Provenance Analytics: Exploring Computational Tasks and their History

III：媒介：起源分析：探索计算任务及其历史

• 批准号: 0905385
• 负责人: Juliana Freire
• 金额: $ 95.75万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905385&HistoricalAwards=false
• 关键词: III; Medium; Provenance; Analytics; Exploring

"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."This proposal will address the provenance of computational processes and the data they manipulate. These are of fundamental importance in maintaining scientific process. Provenance (also referred to as audit trail, lineage, and pedigree) captures information about the steps used to generate a given data product. Such information provides documentation that is key to preserving the data and determining the data's quality and authorship as well as interpreting, reproducing, sharing and publishing results. It also seeks to produce algorithms and techniques for extracting and reusing useful knowledge embedded in workflow specifications. Workflows and workflow-based systems have proven to be successful in capturing computational tasks at various levels of detail and automatically record provenance information. They have recently emerged as an alternative to ad-hoc approaches to assembling computational tasks that are widely used in the scientific community. In order to effectively use provenance information and to deal with a potential information overload, we need novel tools and techniques that help users. The ability to explore the knowledge available in the provenance of computational tasks has the potential to foster large-scale collaborations, expedite scientific training in disciplinary and inter-disciplinary settings, as well as to reduce the lag time between data acquisition and scientific insight.

“该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。“这项提案将解决计算过程的起源及其操纵的数据。 这对于维持科学的发展具有重要意义。来源（也称为审计跟踪、世系和谱系）捕获有关用于生成给定数据产品的步骤的信息。这些信息提供的文件是保存数据、确定数据质量和作者以及解释、复制、分享和公布结果的关键。它还试图产生算法和技术，用于提取和重用嵌入在工作流规范中的有用知识。 工作流和基于工作流的系统已被证明是成功的捕获计算任务在各种层次的细节，并自动记录出处信息。 它们最近出现作为一种替代特设的方法来组装在科学界广泛使用的计算任务。 为了有效地使用出处信息和处理潜在的信息过载，我们需要新的工具和技术来帮助用户。探索计算任务起源中可用知识的能力有可能促进大规模合作，加快学科和跨学科环境中的科学培训，以及减少数据获取和科学见解之间的滞后时间。