III:Small:Enabling Technology for Best-Effort Data Integration Systems

III：小型：尽力而为数据集成系统的支持技术

• 批准号: 1018792
• 负责人: AnHai Doan
• 金额: $ 49.93万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018792&HistoricalAwards=false
• 关键词: III; Small; Enabling; Technology; Best

Over the past few decades, the problem of data integration (DI) has received significant attention. Much of the initial attention was directed at integrating business data. Such data typically requires exact integration, because anything less is clearly not usable. Today, such exact DI systems continue to play an important role. But they are ill-suited for many emerging domains, such as personal information management, building Web community portals, scientific data management, text management for business intelligence, public safety, and military intelligence analysis. First, they are typically constructed in ``one shot'' in that the system is substantially unusable until it is completed in all of its envisioned generality. Second, when presented with new data, these systems often incur long delays before making the data available to users. Third, they typically are not designed to benefit from user feedback, even though opportunities for such feedback often exist in today's Web 2.0 world. Fourth, exact DI systems provide little or no assistance in explaining answers to users. In response, this project explores a paradigm shift, from precise DI systems to best-effort ones. Instead of being constructed in one shot, these systems are constructed incrementally. Their data is always queryable some fashion. They tolerate mistakes in the data, and can leverage user feedback to improve over time. Finally, they can explain their answers to the users, thereby allowing them to understand, verify, and trust query results.To build best-effort DI systems, researchers will pursue the following technical thrusts. (1)Increasing support for incremental development through the specification and implementation of a declarative, semantically transparent extraction/integration language, together with an effective optimization and execution framework. (2) Leveraging the power of a user community through the design and implementation of techniques that allow users to correct errors in the extraction/integration process as they are encountered, that consistently propagates these corrections throughout the extracted and integrated data, and that use these corrections to improve the quality of extraction/integration modules. (3) Developing and implementing techniques to capture information that will help users reason about the system's data along with support for exploring the implications of this information. The team will combine the technology to build a prototype end-to-end best-effort DI system and evaluate the system on three real-world applications: the DBLife portal, the GLEON limnology project, and the madison.com Web portal.This research will be integrated with ongoing efforts in educating students on techniques for extracting and integrating structured data. Inclusion of underrepresented minorities in the projects will be continued. The results from this project will be incorporated into a textbook on data integration to be published in 2010-2011. The project will facilitate the widespread deployment of data integration systems, thus resulting in more effective information management and access for society. It will play an integral part in educating next-generation professional workers and researchers. The research will also help domain scientists in limnology in the context of the GLEON project. It also has the potential to help the developers of madison.com build a system of much greater use to the greater Madison community. Finally, data and system artifacts from the project will be disseminated broadly in the research community to significantly enhance the data management infrastructure for research and education.

在过去的几十年里，数据集成（DI）问题受到了广泛的关注。最初的注意力主要集中在集成业务数据上。这样的数据通常需要精确的集成，否则显然是不可用的。今天，这种精确的直接注入系统继续发挥重要作用。但是它们不适用于许多新兴领域，例如个人信息管理、构建Web社区门户、科学数据管理、用于商业智能的文本管理、公共安全和军事情报分析。首先，它们通常是在“一次性”中构建的，即系统在完成其所有设想的一般性之前基本上是不可用的。其次，当提供新数据时，这些系统在向用户提供数据之前通常会产生长时间的延迟。第三，它们通常不是为了从用户反馈中获益而设计的，尽管在当今的Web 2.0世界中经常存在这样的反馈机会。第四，精确的直接注入系统在向用户解释答案方面提供很少或根本没有帮助。为此，本项目探索了一种范式转变，即从精确的直接注入系统到尽力而为的系统。这些系统不是一次性构建，而是增量构建。他们的数据总是可以以某种方式查询。他们容忍数据中的错误，并可以利用用户反馈来改进。最后，他们可以向用户解释他们的答案，从而让用户理解、验证和信任查询结果。为了构建最佳DI系统，研究人员将追求以下技术重点。(1)通过声明性、语义透明的提取/集成语言的规范和实现，以及有效的优化和执行框架，增加对增量开发的支持。(2)通过设计和实现允许用户纠正提取/集成过程中遇到的错误的技术，利用用户社区的力量，在整个提取和集成数据中始终如一地传播这些纠正，并使用这些纠正来提高提取/集成模块的质量。(3)开发和实现捕获信息的技术，这些信息将帮助用户对系统数据进行推理，并支持探索这些信息的含义。该团队将结合该技术构建一个端到端的最佳DI系统原型，并在三个实际应用中评估该系统：DBLife门户、GLEON湖沼学项目和madison.com Web门户。这项研究将与教育学生提取和整合结构化数据技术的持续努力相结合。将继续将代表性不足的少数民族纳入项目。该项目的成果将被纳入将于2010-2011年出版的关于数据整合的教科书。该项目将促进数据集成系统的广泛部署，从而为社会带来更有效的信息管理和获取。它将在培养下一代专业工作者和研究人员方面发挥不可或缺的作用。这项研究还将在GLEON项目的背景下帮助湖泊学领域的科学家。它也有潜力帮助madison.com的开发者建立一个对更大的麦迪逊社区更有用的系统。最后，该项目的数据和系统工件将在研究界广泛传播，以显着增强研究和教育的数据管理基础设施。