SGER: Answering Approximate String Queries Using Variable-Length Grams

这个项目的目标是为VGRAM开发理论和实现基础，VGRAM是一种技术，它使用来自字符串集合的可变长度、高质量的gram来支持对集合的近似查询。研究计划包括四个任务：1)开发VGRAM自动确定最优grams集的方法，而不需要用户自定义参数；2)将VGRAM集成到关系数据库管理系统中以供采用；3)使用VGRAM支持文档中的近似关键字搜索；4)使用两个实际应用程序对VGRAM进行评估，一个用于集成有关家庭团聚的Web信息，一个用于集成医疗信息。由于近似字符串查询在数据集成和记录链接等许多应用中都需要用到，研究结果将对社会产生重大影响。该项目支持两名博士生在文本检索和数据库系统领域进行研究。本项目的出版物、技术报告、软件和实验数据将通过项目网站（http://flamingo.ics.uci.edu/）发布。

The goal of this project is to develop theory and implementation foundations for VGRAM, a technique that uses variable-length, high-quality grams from a collection of strings to support approximate queries on the collection. The research plan includes four tasks: 1)developing methods for VGRAM to decide an optimal set of grams automatically without requiring user-defined parameters, 2)integrating VGRAM into relational database management systems for adoption, 3) using VGRAM to support approximate keyword search in documents, and 4) evaluating VGRAM using two real applications, one for integrating Web information about family reunification and one for integrating medical information. The research results will have significant impacts on society as approximate string queries are needed in many applications, such as data integration and record linkage. This project supports two PhD students to pursue research in the areas of text retrieval and database systems. Publications, technical reports, software and experimental data from this project will be disseminated via the project web site (http://flamingo.ics.uci.edu/).