CAREER: Ubiquitous Distributed Knowledge Discovery from Heterogeneous Data

本研究计画的目标是开发新一代的演算法与系统，以在普适计算环境下进行分散式与异质性资料的探勘。本研究涉及开发的分布式主成分分析（PCA），分布式随机投影技术，聚类算法的基础上，这些分布式表示建设技术，和分布式决策树学习技术的基础上傅立叶分析。这项研究是集成在数据挖掘领域通过主办本科生，K-12学生和教师，组织研讨会，保持虚拟存在，与代表性不足的群体合作与马里兰州巴尔的摩县施赖弗中心的大学，和研究/教学实验室的发展，促进教育。该项目的结果将提供新一代的数据挖掘算法，最大限度地减少维护无处不在的存在的成本。它还将提高不同层次的学生对数据挖掘教育重要性的认识。

The goal of this research project is to develop a new generation of algorithms and systems for mining distributed and heterogeneous data for ubiquitous computing environments. This research involves development of a distributed principal component analysis (PCA), distributed randomized projection techniques, clustering algorithms based on these distributed representation construction techniques, and a distributed decision tree learning technique based on Fourier analysis. This research is integrated with promotion of education in the data mining area through hosting undergraduates, K-12 students and teachers, organizing workshops, maintaining virtual presence, working with under-represented groups in collaboration with the University of Maryland Baltimore County Shriver Center, and the development of research/instructional laboratories. The results of this project will provide a new generation of data mining algorithms that minimize the cost for maintaining the ubiquitous presence. It will also enhance the awareness among the students at different levels about the importance of data mining education.