CAREER: Ubiquitous Distributed Knowledge Discovery from Heterogeneous Data

该研究项目的目标是开发新一代的算法和系统，用于为普适计算环境挖掘分布式和异构数据。本研究涉及分布式主成分分析（PCA）、分布式随机投影技术、基于这些分布式表示构建技术的聚类算法以及基于傅里叶分析的分布式决策树学习技术的发展。这项研究通过接待本科生、K-12学生和教师、组织研讨会、保持虚拟存在、与马里兰大学巴尔的摩县施莱佛中心合作与代表性不足的群体合作，以及开发研究/教学实验室，与数据挖掘领域的教育推广相结合。这个项目的结果将提供新一代的数据挖掘算法，以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.