Supporting Keyword Search over Structured Data

使用户能够使用简单的关键字访问数据库，可以减轻他们的麻烦，掌握结构化查询语言和理解复杂的，可能是快速发展的数据库模式。尽管关键字搜索技术在Web竞技场中已经成熟，但是支持对结构化数据（例如存储在数据库和数据仓库中的数据）的关键字搜索提出了独特的挑战。当对结构化数据执行关键字搜索时，结果不再是现有的Web页面，而是通过组装来自（可能）不同表的关键字匹配元组而组成的“虚拟文档”。相应地，在搜索过程中必须探索的空间变得比在Web搜索中遇到的空间大得多。 本研究的长期目标是使关键字搜索成为数据库导航和探索的一种高效和有效的手段。为此，拟议的研究将集中在三个主题：提高搜索结果的质量，将领域知识纳入关键字查询处理，并支持关键字驱动的数据分析。我们期望提出一系列新的模型和算法，这些模型和算法将提高关键字搜索的功能、有效性和效率。预计研究结果将在商业智能和电子保健等广泛的场景中得到应用。

Enabling users to access databases using simple keywords can relieve them from the trouble of mastering a structured query language and understanding complex and possibly fast evolving database schemas. Although keyword search technology has matured in the Web arena, supporting keyword search over structured data, such as the data stored in databases and data warehouses, presents unique challenges. When performing keyword search over structured data, the results are no longer existing Web pages, but "virtual documents" composed by assembling the keyword-matching tuples from (potentially) different tables. Correspondingly, the space that must be explored during search becomes much larger than that encountered in Web search. The long-term objective of this research is to enable keyword search as an efficient and effective means of database navigation and exploration. To this end, the proposed research will focus on three topics: improving the quality of search results, incorporating domain knowledge into keyword query processing, and supporting keyword-driven data analytics. We expect to propose a series of novel models and algorithms that would improve the functionality, effectiveness, and efficiency of keyword search over the state-of-the-art methods. The results are expected to find applications in a wide spectrum of scenarios, such as business intelligence and e-health.