III: Small: Towards Agile Information Integration for Large Scale-- Data Aware Indexing and Search over Unstructured Data

III：小：迈向大规模敏捷信息集成——非结构化数据的数据感知索引和搜索

• 批准号: 1018723
• 负责人: Kevin Chang
• 金额: $ 50万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018723&HistoricalAwards=false
• 关键词: III; Small; Towards; Agile; Information

This proposal aims to enable scalable and adaptable information integration over unstructured data at a large scale. There is a need to be able to do structured queries with unstructured data, for example in executing SQL queries over Web pages. This project will develop a new approach of "query push-down," distinctive from the conventional "data pull-up" techniques, as a promising direction for accomplishing agility in integration. The technical objectives will be driven by two application domains: Army land planning and the Illinois digital library. The team will develop query translation techniques that "pushes down" queries to a format that can be executed over unstructured document and feature indexes. This approach will eliminate expensive, inflexible, and often fragile extraction of unstructured data, enabling scalable and adaptable information integration through "best effort" semantics. In the query push-down approach, queries are no longer executed by the SQL-like Boolean semantics, but would rather take a maximum likelihood interpretation-- i.e., what are the most likely answers, by properly translating a given query, under the presence of uncertainty and lack of preciseness in data? The team will study the formalism that governs the principles of such probabilistic query execution, for achieving "best effort" with probabilities as a formal quality metric. Researchers will build the Data-oriented Content Query System , which will support users of Web data not only keywords but also data types to query for relevant values of their desired data in the contents of the corpus, by specifying flexible patterns and customizing scoring functions. Structured queries will be translated for executing in the system to access and integrate the unstructured contents in the corpus.The successful results in this proposed research will have significant impacts in two areas. The research community has observed the scalability limitation of the current integration schemes. These observations highlight the urgency of the proposed study for developing large-scale, agile integration techniques. This will formally advance the understanding of large-scale best-effort integration and develop a set of general techniques. Second, the development of the query system engine will provide access to the data-rich Web, with practical deployment at the Illinois Gateway of the UIUC digital library, which will improve students and faculty?s access to online scholarly and open information. Students will be directly involved in the research effort and new curricula are planned.

该提案旨在实现大规模非结构化数据的可扩展和适应性强的信息集成。需要能够对非结构化数据进行结构化查询，例如在Web页面上执行SQL查询。 本项目将开发一种新的方法“查询下推”，区别于传统的“数据上拉”技术，作为一个有前途的方向，实现敏捷的集成。 技术目标将由两个应用领域驱动：陆军土地规划和伊利诺伊州数字图书馆。该团队将开发查询翻译技术，将查询“下推”到可以在非结构化文档和特征索引上执行的格式。 这种方法将消除对非结构化数据的昂贵、不灵活且通常脆弱的提取，从而通过“尽力而为”语义实现可伸缩和可适应的信息集成。 在查询下推方法中，查询不再由类似SQL的布尔语义执行，而是采用最大似然解释-即，在存在不确定性和缺乏数据精确性的情况下，通过正确翻译给定的查询，最可能的答案是什么？ 该团队将研究管理这种概率查询执行的原则的形式主义，以实现“最大努力”的概率作为正式的质量度量。 研究人员将建立面向数据的内容查询系统，该系统将支持Web数据的用户不仅关键字，而且数据类型，以查询他们想要的数据在语料库的内容中的相关值，通过指定灵活的模式和定制的评分功能。 结构化查询将在系统中被翻译执行，以访问和集成语料库中的非结构化内容。研究界已经观察到当前集成方案的可扩展性限制。这些观察结果突出了开发大规模敏捷集成技术的紧迫性。 这将正式推进对大规模尽力而为集成的理解，并开发一套通用技术。第二，查询系统引擎的开发将提供对数据丰富的Web的访问，并在UIUC数字图书馆的伊利诺伊门户进行实际部署，这将提高学生和教师的素质。我们可以访问在线学术和开放信息。 学生将直接参与研究工作，并计划新的课程。