Student Travel Support for the 2012 ACM Conference on Knowledge Discovery and Data Mining (KDD 2012).

2012 年 ACM 知识发现和数据挖掘会议 (KDD 2012) 的学生差旅支持。

• 批准号: 1241017
• 负责人: Jennifer Neville
• 金额: $ 2.5万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1241017&HistoricalAwards=false
• 关键词: Student; Travel; Support; 2012; ACM

The annual ACM SIGKDD conference is the premier international forum for data mining researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD 2012 will feature keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits, demonstrations, and the KDD Cup competition. KDD-2012 will be held at Beijing, China from August 12 to 16, 2012. Strong student participation is a long running tradition of the KDD conference. This project seeks to encourage and support graduate students enrolled in doctoral programs at US institutions to participate in KDD. Approximately 20 Student Travel Awards will be made to enable the students to attend KDD 2012 and participate in a Doctoral Forum.Intellectual Merit: The field of Knowledge Discovery and Data Mining has grown rapidly in recent years. Massive data sets have driven research, applications, and tool development in business, science, government, and academia. The continued growth in data collection in all of these areas ensures that the fundamental problem which KDD addresses, namely how does one understand and use one's data, will continue to be of critical importance across a large range of organizations. 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. SIGKDD conference focuses on innovative research on all aspects of knowledge discovery and data mining. Examples of topic of interest include (but are not limited to): association analysis, classification and regression methods, semi-supervised learning, clustering, factorization, transfer and multi-task learning, feature selection, social networks, mining of graph data, temporal and spatial data analysis, scalability, privacy, security, visualization, text analysis, Web mining, mining mobile data, recommender systems, bioinformatics, e-commerce, online advertising, anomaly detection, and knowledge discovery from big data, including the data on the cloud. Papers emphasizing theoretical foundations, novel modeling and algorithmic approaches to specific data mining problems in scientific, business, medical, and engineering applications are particularly encouraged. It is important to attract students working on these topics to become the future leaders of the field.Broader Impacts: The student travel awards will support will enable US students to participate in one of the leading data mining conferences, and encourage them to pursue research on topics that are at the forefront of the current state of the art. The awards will help broaden the participation of female and minority students who are currently under-represented within the data mining research community.

一年一度的ACM SIGKDD会议是来自学术界、工业界和政府的数据挖掘研究人员和实践者分享他们的想法、研究成果和经验的首要国际论坛。KDD 2012将以主题演讲、口头论文演讲、海报会议、工作坊、教程、小组讨论、展览、演示和KDD杯竞赛为特色。KDD-2012将于2012年8月12日至16日在中国北京举行。强大的学生参与是KDD会议的长期传统。本项目旨在鼓励和支持在美国机构攻读博士学位的研究生参加KDD。大约20个学生旅行奖将提供给学生参加KDD 2012和参加博士论坛。知识价值：近年来，知识发现和数据挖掘领域发展迅速。海量数据集推动了商业、科学、政府和学术界的研究、应用和工具开发。所有这些领域中数据收集的持续增长确保了KDD解决的基本问题，即如何理解和使用数据，将继续在大范围的组织中具有至关重要的意义。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，以处理如此庞大的数据量。SIGKDD会议关注知识发现和数据挖掘各方面的创新研究。感兴趣的话题包括（但不限于）：关联分析、分类和回归方法、半监督学习、聚类、分解、迁移和多任务学习、特征选择、社交网络、图数据挖掘、时空数据分析、可扩展性、隐私、安全、可视化、文本分析、Web挖掘、移动数据挖掘、推荐系统、生物信息学、电子商务、在线广告、异常检测、从大数据（包括云上的数据）中发现知识。特别鼓励论文强调科学、商业、医学和工程应用中特定数据挖掘问题的理论基础、新颖建模和算法方法。吸引研究这些课题的学生成为该领域未来的领导者是很重要的。更广泛的影响：学生旅行奖将支持美国学生参加一个领先的数据挖掘会议，并鼓励他们对当前最先进的主题进行研究。该奖项将有助于扩大目前在数据挖掘研究界代表性不足的女性和少数族裔学生的参与。