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: 1247664
• 负责人: David Blei
• 金额: $ 69.96万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1247664&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/.

从根本上讲，大数据分析是将今天生产的大量数据降至人类规模的问题。特别是科学家，工程师，医师和许多其他知识密集型专业的其他人面临的数据超出了人类规模。这些数据在收集数据以及报告或导致其字段的存储库中。该项目将通过使用更多数据来解决所有这些知识的问题，即对这些存储库的访问和使用方式的个人和社会模式，以及数据的特定用户判断（估值）。 拟议的研究将开发新颖的算法和开源基础架构，以改善内部发现和访问数据存储库。这些算法将汇总和分析从数据使用者的专业社区收集的社会分析数据，并通过提供建议来激励他们参与。变革性目标是开发组织和运营大型存储库用户的访问和优先模式的方法，并将这些估值的访问模式纳入集合中。当他们进行自己的研究或操作活动时，各种各样的人与数据收集互动，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，数值数据集或其他类型的媒体内容。代表，汇总，组织和评估用户与项目之间相互作用的新方法可以揭示数据收集中的结构，这些结构以前是任何个人都不可见的。在捕获人类智能的驱动的数据中发现相互关系的发现将加速科学发现的过程。被允许通过收到有价值的建议来回报的用户，并通过收到有价值的建议来揭示自己的利益。这使得发现数据项和用户本身之间的关系成为可能。教育目标是：（a）有助于对该项目支持的特定研究生的教育，并通过REU机制的本科生进行教育； （b）生成与算法创新有关的新教育材料，并研究发现； （c）改善特定材料集合中的获取和发现。研究结果将包括在所有三个合作的大学的课程中。