Scalable Knowledge Discovery from Large Structural Databases

从大型结构数据库中发现可扩展的知识

• 批准号: 9615272
• 负责人: Lawrence Holder
• 金额: $ 30.57万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-03-01 至 2000-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9615272&HistoricalAwards=false
• 关键词: Scalable; Knowledge; Discovery; Large; Structural

The main objective of this project is to improve the scalability and effectiveness of knowledge discovery and data mining systems in order to handle large, structural databases (i.e., databases composed of parts and relations among parts). First, a state-of-the-art structural discovery system called Subdue will be integrated with one or more existing non-structural discovery systems. Parallel and distributed versions of both Subdue and the integrated discovery system will then be developed. Distributing both the data and the processing across several machines will afford the most scalability for the discovery systems, and distribution is essential for handling large databases. The integrated discovery system will then be applied to several large scientific databases. The results will be evaluated by domain experts and disseminated along with source code releases of all software to the scientific community. This research addresses a critical need to improve the scalability of existing knowledge discovery and data mining methods, especially in domains with richer data representations, will provide scalable discovery systems to the scientific community, and will increase the state of knowledge in the design of parallel and distributed intelligent systems.

该项目的主要目标是提高知识发现和数据挖掘系统的可扩展性和有效性，以便处理大型结构化数据库（即，由部件和部件之间的关系组成的数据库）。 首先，一个名为Subdue的最先进的结构化发现系统将与一个或多个现有的非结构化发现系统集成。 然后将开发Subdue和集成发现系统的并行和分布式版本。 将数据和处理分布在几台机器上将为发现系统提供最大的可伸缩性，而分布对于处理大型数据库是必不可少的。然后，综合发现系统将应用于几个大型科学数据库。 结果将由领域专家进行评估，并与所有软件的源代码版本一起沿着向科学界传播。 这项研究解决了一个关键的需要，以提高现有的知识发现和数据挖掘方法的可扩展性，特别是在域更丰富的数据表示，将提供可扩展的发现系统的科学界，并将增加并行和分布式智能系统的设计知识的状态。