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

从根本上说，大数据分析的问题是将当今产生的大量数据缩小到人类规模。特别是科学家、工程师、医生和许多其他知识密集型职业面临着超出人类规模的数据。该数据位于收集数据和报告或其字段中的结果的存储库中。该项目将解决通过使用更多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，以及用户特定的数据判断（评估）。 拟议的研究将开发新颖的算法和开源基础设施，以改善数据存储库内的发现和访问。这些算法将聚合和分析从数据用户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。变革性的目标是开发组织和操作大型存储库用户的访问和偏好模式的方法，并整合这些评估以加速馆藏中的发现。在开展自己的研究或运营活动时，不同的人类思维与数据收集进行交互，提供了有关数据本身价值的强大信息源。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。用于表示、聚合、组织和评估用户和项目之间的交互的新颖方法可以揭示数据集合中的结构，而这些结构以前对任何个人来说都是不可见的。在捕捉人类智慧的驱动下发现数据内部的相互关系，将加速科学发现的进程。被允许评估数据的用户，以及通过收到有价值的建议作为回报而受到激励的用户，会更多地透露自己的兴趣。这使得发现数据项之间以及用户本身之间的关系成为可能。教育目标是： (a) 为项目支持的特定研究生和通过 REU 机制支持的本科生的教育做出贡献； (b) 制作与算法创新和研究成果相关的新教育材料； (c) 改善特定材料收藏的获取和发现。研究成果将包含在所有三所合作大学的课程中。有关该项目的其他信息（包括出版物、软件、数据集）将通过项目网站提供：http://arxiv_xs.rutgers.edu/。

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/.