CAREER: Memory-Constrained Predictive Data Mining

职业：内存受限的预测数据挖掘

• 批准号: 0546155
• 负责人: Slobodan Vucetic
• 金额: $ 45万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-02-15 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0546155&HistoricalAwards=false
• 关键词: CAREER; Memory; Constrained; Predictive; Data

In the environment where new large-scale problems are emerging in various disciplines and pervasive computing applications are becoming more common, there is an urgent need for techniques that provide efficient and accurate knowledge discovery by limited-capacity computing devices. The objective of this project is to address this need by developing memory-constrained predictive data mining algorithms that operate when data size exceeds the available memory capacity. The approach is based on the integration of data mining and data compression techniques to optimally utilize the memory for data and model storage, learning, and ancillary operations. The methodology will be thoroughly evaluated on a range of real-life problems that includes learning from large sorted databases, biased data and nonstationary data. Various memory constraints will be considered pertaining to devices ranging from powerful workstations to handheld computers and cell phones to small, inexpensive sensors. This research will reveal the memory lower bounds for accurate learning from different types of data and by different types of learning algorithms. The educational component of the project seeks to integrate research into computer science instruction by designing exciting courses, exploring effective teaching techniques, introducing research to undergraduate and graduate students, and involving underrepresented student groups in research. Broader impacts of the project will be in extending the frontiers of computer and information science and in facilitating knowledge discovery in various scientific, engineering, and business disciplines. Teaching materials and research results, including developed software and databases, will be widely disseminated via Internet (http://www.ist.temple.edu/~vucetic/CAREER.htm) to promote learning and enhance scientific understanding.

在新的大规模的问题正在出现在各个学科和普适计算应用变得越来越普遍的环境中，迫切需要的技术，提供有效的和准确的知识发现的有限容量的计算设备。这个项目的目标是通过开发内存受限的预测数据挖掘算法，当数据大小超过可用的内存容量时，可以满足这一需求。该方法基于数据挖掘和数据压缩技术的集成，以最佳地利用存储器进行数据和模型存储、学习和辅助操作。该方法将在一系列现实问题上进行彻底评估，包括从大型排序数据库，有偏见的数据和非平稳数据中学习。各种内存限制将被认为是属于从强大的工作站，手持计算机和手机的小型，廉价的传感器的设备。这项研究将揭示不同类型的数据和不同类型的学习算法的准确学习的记忆下限。该项目的教育部分旨在通过设计令人兴奋的课程，探索有效的教学技术，向本科生和研究生介绍研究，并让代表性不足的学生群体参与研究，将研究融入计算机科学教学。该项目的更广泛影响将是扩大计算机和信息科学的前沿，促进各种科学，工程和商业学科的知识发现。将通过因特网（http：//www.ist.temple.edu/carevucetic/CAREER.htm）广泛传播教材和研究成果，包括开发的软件和数据库，以促进学习和提高科学认识。