III-COR-Medium: Providing Provenance through Workflows and Database Transformations

数据来源是处理科学信息及其他方面的一个基本问题。近年来，有两条研究路线与数据来源问题直接相关。其中之一是工作流中的溯源，重点是从标记不同模块执行到各种初始和派生数据集的事件日志中提取溯源。在另一项研究中，即数据库中的来源，重点是通过组成数据库视图的操作符传播来源，或者通过数据库内部和数据库之间的复制/剪切和粘贴操作传播来源。这两种工作采用不同的技术，乍一看，它们的结果似乎大不相同。然而，在许多科学应用程序中，数据库操作与工作流模块的执行共存，结果数据的来源应该将这两种处理集成到一个可用的范例中。通过分析工作流和数据库中数据来源的工作，pi确定了他们认为统一和集成这两种不同类型的数据来源的主要困难：(1)缺乏足够丰富的数据模型来捕捉数据结构与工作流结构之间的交互；(2)缺乏统一对待数据库操作符和工作流模块的高级规范框架。在这个项目中，pi的目标是克服这些困难，从而提供概念和工具，允许对科学数据的来源进行真正全面的方法。该项目的方法依赖于支持嵌套集合的数据模型和工作流规范的函数式语言方法。基于此，该项目旨在提供一个框架和工具，用于定义、管理和查询包括数据库操作在内的复杂科学工作流中的数据来源。该项目预计将影响生物信息学（通过宾夕法尼亚大学生物信息学中心和宾夕法尼亚大学基因组前沿研究所的跨学科合作）和系统信息学（通过对NSF AToL计划的贡献），以及正在进行的工作流程和业务流程中来源的标准化工作(例如。（BPEL）社区。该项目的成果通过直接合作和项目网站http://db.cis.upenn.edu/research/UNIPROVE.html作为出版物传播。

Data provenance is a fundamental issue in the processing of scientific information and beyond. Two lines of research have been pursued in recent years with direct bearing on the issues of data provenance. In one of them, provenance in workflows, the emphasis is on extracting provenance from logs of events marking the execution of different modules to various intial and derived datasets. In the other line of research, provenance in databases, the emphasis is on the propagation of provenance through the operators that make up database views, or on propagation of provenance through copy/cut-and-paste operations within and among databases. These two bodies of work employ different techniques and at first glance their results appear quite different. However, in many scientific applications, database manipulations co-exist with the execution of workflow modules, and the provenance of the resulting data should integrate both kinds of processing into a usable paradigm. By analyzing the work on data provenance in workflows and in databases, the PIs identify what they believe are the main difficulties in unifying and integrating these two different kinds of data provenance:(1) the lack of a data model that is rich enough to capture the interaction between the structure of the data and the structure of the workflow; and(2) the lack of a high-level specification framework in which database operators and workflow modules can be treated uniformly. In this project, the PIs aim to overcome these difficulties and thus provide concepts and tools that allow a truly comprehensive approach to the provenace of scientific data. The project's approach relies on a data model that supports nested collections and on a functional language approach to workflow specification. Based on this, the project aims to deliver a framework and tools for defining, managing and querying data provenance in complexscientific workflows that include database manipulations. The project is expected to impact bioinformatics (through interdisciplinary collaborations in the Penn Center for Bioinformatics and the Penn Genome Frontiers Institute) and phyloinformatics (through contributions to the NSF AToL program) as well as ongoing standardization work on provenance in workflows and in the business processes (eg., BPEL) community.The results of this project are disseminated as publications, through direct collaborations and through the project website: http://db.cis.upenn.edu/research/UNIPROVE.html.