Statistical inference for distributed datasets

分布式数据集的统计推断

• 批准号: RGPIN-2016-06296
• 负责人: Plante, JeanFrançois
• 金额: $ 1.09万
• 依托单位: HEC Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635172
• 关键词: Statistical; inference; distributed; datasets

Technology allows us to store and describe massive amounts of data. These data are often about humans, keeping a personal trace of individual lives, allowing others to know a customer better than himself, and providing a special insight into social dynamics. The era of big data is here, and such massive amounts of information cannot be stored on a personal computer, but rather require clusters of machines that are interconnected. Such large scale setups are typically based on distributed file system (such as Hadoop) because no single drive could possibly store that much information. With “distributed data”, a single processor is unable to access the whole dataset, but instead, a large number of processors each have access to only a part of the data.

技术允许我们存储和描述大量数据。这些数据通常是关于人类的，保留了个人生活的个人痕迹，使其他人比自己更了解客户，并提供了对社会动态的特殊见解。大数据时代已经到来，如此海量的信息无法存储在个人电脑上，而是需要相互连接的机器集群。这种大规模的设置通常基于分布式文件系统（如Hadoop），因为没有一个驱动器可能存储那么多信息。对于“分布式数据”，单个处理器无法访问整个数据集，而是大量处理器各自只能访问数据的一部分。