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今天产生的海量数据降到人类的规模。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的数据超出了人类的规模。这些数据存储在存储库中，这些存储库收集数据以及其字段中的报告或结果。该项目将通过使用更多的数据，即如何访问和使用这些储存库的个人和社会模式，以及用户对数据的具体判断(估值)，来解决控制所有这些知识的问题。拟议的研究将开发新的算法和开放源码的基础设施，以改进数据仓库内的发现和访问。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，并通过提供推荐来激励他们参与。变革性的目标是开发方法来组织和操作大型存储库用户的访问和偏好模式，并整合这些评估以加速集合中的发现。在进行自己的研究或操作活动时，不同的人类思维与数据收集互动，提供了关于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。用于表示、聚集、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内的结构，这些结构以前对任何个人都不可见。在人类智能捕获的推动下，这种对数据内部相互关系的发现将加快科学发现的进程。那些被允许评估数据的用户，以及那些因收到有价值的推荐而受到激励的用户，会更多地透露他们自己的兴趣。这使得能够发现数据项之间以及用户自身之间的关系。教育目标是：(A)促进该项目支持的特定研究生的教育，并通过区域教育联盟机制促进本科生的教育；(B)编写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。研究成果将包括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)将通过该项目的网站提供：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/.