Scalable Knowledge Discovery from Large Structural Databases

这个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，以便处理大型的结构化数据库(即由部件组成的数据库和部件之间的关系)。首先，一种名为SUBDUE的最先进的结构发现系统将与一个或多个现有的非结构发现系统集成。然后将开发制导和综合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，集成的发现系统将被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一起分发给科学界。这项研究解决了提高现有知识发现和数据挖掘方法的可扩展性的迫切需要，特别是在数据表示更丰富的领域，将为科学界提供可扩展的发现系统，并将在并行和分布式智能系统的设计中增加知识状态。

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.