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

这一提议旨在实现大规模非结构化数据上的可扩展和可适应的信息集成。需要能够对非结构化数据进行结构化查询，例如在网页上执行SQL查询。该项目将开发一种新的“查询下推”方法，区别于传统的“数据下拉”技术，作为实现集成敏捷性的一个有前途的方向。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”到一种可以在非结构化文档和特征索引上执行的格式。这种方法将消除昂贵、僵化且往往脆弱的非结构化数据提取，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，在数据存在不确定性和缺乏精确度的情况下，通过正确翻译给定的查询，最有可能的答案是什么？该团队将研究支配这种概率查询执行原则的形式主义，以实现以概率作为正式质量度量的“最佳努力”。研究人员将建立面向数据的内容查询系统，通过指定灵活的模式和定制评分函数，支持Web数据用户不仅查询关键字，还支持数据类型在语料库内容中查询其所需数据的相关值。结构化查询将被翻译并在系统中执行，以访问和整合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。这些观察结果突显了拟议研究开发大规模、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。其次，查询系统引擎的开发将提供对数据丰富的Web的访问，并实际部署在伊利诺伊州门户的UIUC数字图书馆，这将改善S学生和教职员工获取在线学术和开放信息的机会。学生将直接参与研究工作，并计划新的课程。

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.