ITR: Knowledge Discovery in Historical Semistructured Data

科学家、工程师和决策者使用的绝大多数数据都存在于结构不佳的报告、备忘录和其他文件格式的集合中。存储设备的密度不断增加，价格不断下降，这使得永久存储科学家电子桌面上的所有此类数据成为可能。由此产生的Information History具有智能助手的功能，可以检测趋势和模式、建议潜在的合作者、发现来自不同来源的相关文档和数据等，从而显著提高信息使用的有效性。然而，目前的技术主要集中在完全结构化或完全非结构化数据库上，无法有效地适应从大型历史半结构化数据库中提取知识的需求。提出的研究目标是为历史半结构化数据库开发合适的知识发现问题的公式，并开发、实施和评估解决方案。研究重点是：(1)设计半结构化数据的知识发现算子及其代数；(2)制定实施这些操作符的有效方法；(3)确定增量式灵活结构合并方法；(4)采用差分处理。个人信息历史助理应用程序可作为本研究的试验台。

The vast majority of data used by scientists, engineers, and decision makers resides in a poorly structured collection of reports, memos, and other documents in a myriad of file formats. The increasing densities and falling prices of storage devices make it practical to store for perpetuity all such data that crosses a scientist's electronic desktop. The resulting Information History has the potential to serve as an intelligent assistant by detecting trends and patterns, suggesting potential collaborators, uncovering relevant documents and data from diverse sources, etc., resulting in dramatic increases in the effectiveness of information use. However, current technology, which focuses on either fully structured or completely unstructured databases, cannot be effectively adapted to extracting knowledge from a large historical semistructured database. The goal of the proposed research is to develop suitable formulations of the knowledge discovery problem for historical semistructured databases and to develop, implement, and evaluate solutions. The research thrusts are: (1) devising knowledge discovery operators for semistructured data and an algebra for them; (2) developing efficient methods for implementing these operators; (3) determining methods to incorporate structure incrementally and flexibly; and (4) incorporating differential processing. A Personal Information History Assistant application serves as a test-bed for this research.