CAREER: Memory-Constrained Predictive Data Mining

在各种学科中出现新的大规模问题和普适计算应用越来越普遍的环境中，迫切需要能够通过有限容量的计算设备提供高效、准确的知识发现的技术。该项目的目标是通过开发内存受限的预测数据挖掘算法来解决这一需求，该算法在数据大小超过可用内存容量时运行。该方法基于数据挖掘和数据压缩技术的集成，以最佳地利用内存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全面评估，包括从大型排序数据库、有偏差数据和非平稳数据中学习。从功能强大的工作站到掌上电脑和移动电话，再到小型、廉价的传感器，将考虑各种存储器限制。本研究将揭示从不同类型的数据和不同类型的学习算法中进行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，以及让代表性不足的学生群体参与研究，将研究整合到计算机科学教学中。该项目的更广泛影响将是扩展计算机和信息科学的前沿，并促进各种科学、工程和商业学科的知识发现。教材和研究成果，包括开发的软件和数据库，将通过互联网（http://www.ist.temple.edu/~vucetic/CAREER.htm）广泛传播，以促进学习和增进科学认识。

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.