课题基金 / 基金详情

Student Travel Support for the 2014 IEEE International Conference on Big Data

Student Travel Support for the 2014 IEEE International Conference on Big Data
2014 年 IEEE 国际大数据会议学生旅行支持
批准号:
1444666
负责人:
Jimmy Lin
金额:
$2.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-06-15 至 2015-05-31

项目摘要

项目成果

Jimmy Lin的其他基金

相似基金

相关文献

中文摘要
翻译
“大数据”已经成为一种新的计算方式,它正在改变科学、工程、医学、医疗保健、金融、商业,并最终改变社会本身。IEEE 2014国际大数据会议(IEEE BigData 2014)为传播大数据研究、开发和应用方面的最新研究提供了一个领先的论坛。会议将于2014年10月27日至30日在华盛顿特区举行。联席主席是吉米·林(马里兰大学)和裴健(西蒙·弗雷泽大学)。该奖项将帮助美国大学的博士生出差,他们是已被技术项目接受或正在参加博士生研讨会的完整论文的主要作者。本次会议征集大数据各个方面的高质量原创研究论文,包括基础设施、管理、搜索和挖掘、安全和隐私以及应用程序。贡献将推动技术、算法和系统的最先进水平。欲了解更多信息,请访问会议主页:http://cci.drexel.edu/bigdata/bigdata2014/
英文摘要
"Big Data" has emerged as a new approach to computing that is transforming science, engineering, medicine, health care, finance, business, and ultimately society itself. The IEEE International Conference on Big Data 2014 (IEEE BigData 2014) provides a leadin forum for disseminating the latest research in big data research, development, and applications. The conference will take place in the Washington, D.C. area from October 27-30, 2014 in Washington, D.C. The PC co-chairs are Jimmy Lin (University of Maryland) and Jian Pei (Simon Fraser University). This award will help support travel of Ph.D. students at U.S. universities who are primary authors on full papers that have been accepted to the technical program or are participating in the doctoral student symposium.This conference solicits high-quality original research papers in any aspect of big data, including infrastructure, management, search and mining, security and privacy, and applications. Contributions will advance the state of the art in techniques, algorithms, and systems.For further information see the conference homepage: http://cci.drexel.edu/bigdata/bigdata2014/
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
II-EN: Hadoop NextGen Infrastructure for Heterogeneous Approaches to Data-Intensive Computing
III: Small: Providing Relevant and Timely Results: Real-Time Search Architectures and Relevance Algorithms
EAGER: Learning to Efficiently Rank with Cascades
DC: Small: Cross-Language Bayesian Models for Web-Scale Text Analysis Using MapReduce
海外基金