BIGDATA: Mid-Scale: ESCE: Collaborative Research: Discovery and Social Analytics for Large-Scale Scientific Literature

大数据：中等规模：ESCE：协作研究：大规模科学文献的发现和社会分析

• 批准号: 1502780
• 负责人: David Blei
• 金额: $ 64.35万
• 依托单位: Columbia University
• 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
• 起止时间: 2014-07-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1502780&HistoricalAwards=false
• 关键词: BIGDATA; Mid; Scale; ESCE; Collaborative

Big data analytics is, fundamentally, the problem of bringing the massive amounts of data produced today down to human scale. In particular scientists, engineers, physicians, and many others in knowledge-intensive professions face data that is beyond human scale. This data is in the repositories that collect the data and the reports or results in their fields. This project will address the problem of bringing all this knowledge under control by using even more data, namely the individual and social patterns of how these repositories are accessed and used, and user-specific judgments (valuations) of the data. The proposed research will develop novel algorithms and an open-source infrastructure for improving discovery within and access to data repositories. These algorithms will aggregate and analyze the social analytic data, gathered from professional communities of data users, and will motivate them to participate by providing recommendations.The transformative goal is to develop methods for organizing, and operationalizing the access and preference patterns of users of large repositories, and for integrating those valuations to accelerate discovery within the collections. Diverse human minds interacting with data collections, as they carry out their own research or operational activities, provide a powerful source of information about the value of the data itself. Those data items may be textual documents, numerical datasets, or other kinds of media content. The novel methods for representing, aggregating, organizing and valuating interactions between the users and the items can reveal structures within data collections, which were previously invisible to any individual. This discovery of interrelations within data, driven by the capture of human intelligence, will accelerate the processes of scientific discovery. Users who are permitted to valuate data, and who are motivated by receiving valuable recommendations in return, reveal more about their own interests. This makes it possible to discover relations among the data items and among the users themselves. The educational goals are to: (a) contribute to the education of specific graduate students supported by the project, and undergraduates via the REU mechanism; (b) generate new educational materials related to algorithmic innovations, and to research findings; and (c) improve access to and discovery within specific collections of materials. Research findings will be included in courses at all three collaborating universities.Additional information about the project (including publication, software, data sets) will be made available through the project web site: http://arxiv_xs.rutgers.edu/.

从根本上说，大数据分析是将今天产生的大量数据降至人类规模的问题。特别是科学家、工程师、医生和许多其他从事知识密集型职业的人，他们面临着超出人类规模的数据。这些数据位于存储库中，存储库在其字段中收集数据和报告或结果。这个项目将解决通过使用更多的数据来控制所有这些知识的问题，即如何访问和使用这些存储库的个人和社会模式，以及用户对数据的特定判断（估值）。拟议的研究将开发新的算法和开源基础设施，以改进数据存储库中的发现和访问。这些算法将汇总和分析从数据用户的专业社区收集的社会分析数据，并将通过提供建议来激励他们参与。变革的目标是开发用于组织和操作大型存储库用户的访问和偏好模式的方法，以及用于集成这些评估以加速集合中的发现。在开展自己的研究或业务活动时，不同的人类思维与数据收集相互作用，提供了有关数据本身价值的强大信息来源。这些数据项可以是文本文档、数字数据集或其他类型的媒体内容。表示、聚合、组织和评估用户与项目之间交互的新方法可以揭示数据集合中的结构，这些结构以前对任何个人都是不可见的。在人类智慧的驱动下，这种对数据内部相互关系的发现将加速科学发现的进程。那些被允许评估数据的用户，以及那些因收到有价值的建议而受到激励的用户，会更多地透露他们自己的兴趣。这使得发现数据项之间和用户本身之间的关系成为可能。教育目标是：(a)通过REU机制为项目支持的特定研究生和本科生的教育做出贡献；(b)编制与算法创新和研究成果有关的新教材；(c)改进对特定材料集合的获取和发现。研究成果将纳入所有三所合作大学的课程。关于该项目的其他信息（包括出版物、软件、数据集）将通过该项目的网站：http://arxiv_xs.rutgers.edu/提供。