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

“这项奖励是根据2009年美国复苏和再投资法案（公法111-5）资助的。”本提案将讨论计算过程的来源及其操作的数据。这些对于保持科学进程至关重要。来源（也称为审计跟踪、血统和系谱）捕获有关用于生成给定数据产品的步骤的信息。这些信息提供的文档是保存数据、确定数据质量和作者以及解释、复制、共享和发布结果的关键。它还寻求产生算法和技术，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在捕获各种详细级别的计算任务和自动记录来源信息方面是成功的。它们最近作为一种特别方法的替代方案出现，用于组装科学界广泛使用的计算任务。为了有效地使用来源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，并减少数据获取和科学见解之间的滞后时间。

"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.