The Second Workshop on Large-Scale Data Mining: Theory and Applications

该项目将提供旅行奖学金，支持美国学生参加2010年7月25日在华盛顿特区举行的第二次大规模数据挖掘：理论与应用研讨会（LDMTA）。LDMTA研讨会将涵盖可扩展机器学习和数据挖掘算法及其应用的主题，如医疗信息学、电信、社会网络分析和电子商务。pi旨在从理论和实验的角度研究现有机器学习和数据挖掘算法的可扩展性和效率。由于最近许多应用程序中的数据爆炸式增长，政府和公司可以很容易地收集跨越tb、pb或更多的数据。一些传统的数据挖掘算法需要被替换，或者彻底重新设计，以便通过并行架构（如MapReduce/Hadoop）处理这样的数据量。研讨会的重点正是弥合数据挖掘理论与实践之间的差距，重点关注大规模数据挖掘理论与应用的基础研究。本次研讨会的目的是聚集数据挖掘研究和行业的领导者，就大规模数据挖掘的需求和挑战提出他们的观点，并吸引研究生学习大规模数据挖掘。拟提供的学生资助将吸引美国学生参加本次研讨会，并展示其与大规模数据挖掘相关的主题作品，鼓励他们将极具前景的大规模数据挖掘研究方向作为论文研究方向。特别是，学院将努力扩大女性和少数族裔学生的参与，优先向她们颁发奖项。有关该项目的进一步信息，请参阅该项目的网站http://arnetminer.org/LDMTA2010

This project will provide travel fellowships to support US students to attend the 2nd workshop on Large-scale Data Mining: Theory and Applications (LDMTA) in conjunction with KDD 2010 on July 25, 2010 in Washington DC. The LDMTA workshop will cover topics on scalable machine learning and data mining algorithms, and their applications, such as medical informatics, telecommunications, social network analysis, and e-commerce. PIs aim at investigating the scalability and efficiency of existing machine learning and data mining algorithms with respect to both theoretical and experimental perspectives. Due to the recent data explosion in many applications, governments and companies can easily collect data spanning terabytes, petabytes or more. Several traditional data mining algorithms need to be replaced, or drastically re-designed, to handle such volumes through parallel architecture such as MapReduce/Hadoop. The focus of the workshop is exactly to bridge the gap between theory and practice of data mining, focusing on the fundamental research on large-scale data mining theory and applications. The goal of this workshop is to assemble the leaders in data mining research and industry to present their views on the need and challenges for large-scale data mining andalso attract graduate students to study on large-scale data mining.The proposed student support will attract the US students to participate in this workshop and present their works on topics related to large-scale data mining, to encourage them to focus on the extremely promising research direction of large-scale data mining as their thesis research. In particular,PIs will try to broaden the involvement of female and minority students byprioritizing the award to them. For further information about this project see the project website at http://arnetminer.org/LDMTA2010