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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

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中文摘要
翻译
“大数据”作为一种新的计算方法已经出现,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。2014年IEEE大数据国际会议(IEEE BigData 2014)为传播大数据研究、开发和应用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。PC联合主席是Jimmy Lin(马里兰大学)和Jian Pei(西蒙弗雷泽大学)。该奖项将资助美国大学的博士研究生的旅行,这些博士生是被技术项目接受的论文的主要作者或参加博士生研讨会。本次大会面向大数据基础设施、大数据管理、大数据搜索与挖掘、大数据安全与隐私、大数据应用等领域的高质量原创研究论文。贡献将推动技术,算法和系统的艺术状态。欲了解更多信息,请参阅会议主页: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)
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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