CAREER: Relaxed Content and Structure Queries over Heterogeneous Data

职业：对异构数据的宽松内容和结构查询

• 批准号: 0844935
• 负责人: Amelie Marian
• 金额: $ 49.98万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-15 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0844935&HistoricalAwards=false
• 关键词: CAREER; Relaxed; Content; Structure; Queries

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The traditional separation between the "structure-only" Database world and the "text-only" Information Retrieval world is fading. Databases now routinely include text components while documents are being augmented with structural information. The goal of this project is to design novel techniques and develop tools to efficiently query and retrieve relevant information in a heterogeneous data environment where flexibility in conditions on both the content, the structure of the data, and the response to a query is desirable. The first main contribution of the project is the design of quality scoring mechanisms that unify content and structure score in an integrated fashion. The scoring techniques take into account the similarity between the query and the answer to assign scores. The second main contribution of the project is the development of heterogeneous data index structures and query processing algorithms to efficiently identify exact and approximate query answers and provide the answers in the order of relevance to a query. The work resulting from this project will be evaluated through an in-depth study of the impact of the scoring strategies on answer quality and performance experiments on the query processing techniques. The results of this project are expected to enable users to identify the data that best fits their needs, in a variety of heterogeneous data environments, without requiring some preexisting knowledge of the underlying data schema or content. This project integrates research and education through curriculum development, student advising, and outreach to women in Computer Science. Results of this project, including publications, data sets and software will be made available on the project website (http://www.cs.rutgers.edu/~amelie/RelaxedQ/).

该奖项根据 2009 年美国复苏和再投资法案（公法 111-5）提供资金。“纯结构”数据库世界和“纯文本”信息检索世界之间的传统分离正在消失。现在，数据库通常包含文本组件，而文档则通过结构信息进行扩充。该项目的目标是设计新颖的技术并开发工具，以在异构数据环境中有效地查询和检索相关信息，其中内容、数据结构和查询响应的条件都需要灵活性。该项目的第一个主要贡献是设计质量评分机制，以集成的方式统一内容和结构评分。评分技术考虑查询和答案之间的相似性来分配分数。该项目的第二个主要贡献是开发异构数据索引结构和查询处理算法，以有效地识别精确和近似的查询答案，并按照与查询的相关性顺序提供答案。该项目的工作成果将通过深入研究评分策略对答案质量的影响以及对查询处理技术的性能实验进行评估。该项目的结果预计将使用户能够在各种异构数据环境中识别最适合其需求的数据，而无需预先了解底层数据模式或内容。该项目通过课程开发、学生咨询以及对计算机科学领域女性的推广将研究和教育融为一体。该项目的结果，包括出版物、数据集和软件，将在项目网站 (http://www.cs.rutgers.edu/~amelie/RelaxedQ/) 上提供。